Deck 14: Introduction to Multiple Regression

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سؤال
SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, what is the estimated mean consumption level for an economy with GDP equal to $4 billion and an aggregate price index of 150?</strong> A)$1.39 billion B)$2.89 billion C)$4.75 billion D)$9.45 billion <div style=padding-top: 35px>
Referring to Scenario 14-3, what is the estimated mean consumption level for an economy with GDP equal to $4 billion and an aggregate price index of 150?

A)$1.39 billion
B)$2.89 billion
C)$4.75 billion
D)$9.45 billion
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سؤال
The variation attributable to factors other than the relationship between the independent variables and the explained variable in a regression analysis is represented by

A)regression sum of squares.
B)error sum of squares.
C)total sum of squares.
D)regression mean squares.
سؤال
SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, what is the predicted consumption level for an economy with GDP equal to $4 billion and an aggregate price index of 150?</strong> A)$1.39 billion B)$2.89 billion C)$4.75 billion D)$9.45 billion <div style=padding-top: 35px>
Referring to Scenario 14-3, what is the predicted consumption level for an economy with GDP equal to $4 billion and an aggregate price index of 150?

A)$1.39 billion
B)$2.89 billion
C)$4.75 billion
D)$9.45 billion
سؤال
SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, for these data, what is the estimated coefficient for performance rating,  </strong> A)0.616 B)1.054 C)6.932 D)9.103 <div style=padding-top: 35px> and the number of economics courses the employee successfully completed in college <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, for these data, what is the estimated coefficient for performance rating,  </strong> A)0.616 B)1.054 C)6.932 D)9.103 <div style=padding-top: 35px> The professor randomly selects 6 workers and collects the following information: <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, for these data, what is the estimated coefficient for performance rating,  </strong> A)0.616 B)1.054 C)6.932 D)9.103 <div style=padding-top: 35px>
Referring to Scenario 14-2, for these data, what is the estimated coefficient for performance rating, <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, for these data, what is the estimated coefficient for performance rating,  </strong> A)0.616 B)1.054 C)6.932 D)9.103 <div style=padding-top: 35px>

A)0.616
B)1.054
C)6.932
D)9.103
سؤال
SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, an employee who took 12 economics courses scores 10 on the performance rating.What is her estimated expected wage rate?</strong> A)10.90 B)12.20 C)24.87 D)25.70 <div style=padding-top: 35px> and the number of economics courses the employee successfully completed in college <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, an employee who took 12 economics courses scores 10 on the performance rating.What is her estimated expected wage rate?</strong> A)10.90 B)12.20 C)24.87 D)25.70 <div style=padding-top: 35px> The professor randomly selects 6 workers and collects the following information: <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, an employee who took 12 economics courses scores 10 on the performance rating.What is her estimated expected wage rate?</strong> A)10.90 B)12.20 C)24.87 D)25.70 <div style=padding-top: 35px>
Referring to Scenario 14-2, an employee who took 12 economics courses scores 10 on the performance rating.What is her estimated expected wage rate?

A)10.90
B)12.20
C)24.87
D)25.70
سؤال
In a multiple regression model, which of the following is correct regarding the value of the adjusted <strong>In a multiple regression model, which of the following is correct regarding the value of the adjusted   ?</strong> A)It can be negative. B)It has to be positive. C)It has to be larger than the coefficient of multiple determination. D)It can be larger than 1. <div style=padding-top: 35px> ?

A)It can be negative.
B)It has to be positive.
C)It has to be larger than the coefficient of multiple determination.
D)It can be larger than 1.
سؤال
SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, the p-value for the regression model as a whole is</strong> A)0.05 B)0.01 C)0.001 D)None of the above. <div style=padding-top: 35px>
Referring to Scenario 14-3, the p-value for the regression model as a whole is

A)0.05
B)0.01
C)0.001
D)None of the above.
سؤال
SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, if an employee who had been with the company 5 years scored a 9 on the aptitude test, what would his estimated expected sales be?</strong> A)79.09 B)60.88 C)55.62 D)17.98 <div style=padding-top: 35px> and how he/she scored on a business aptitude test <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, if an employee who had been with the company 5 years scored a 9 on the aptitude test, what would his estimated expected sales be?</strong> A)79.09 B)60.88 C)55.62 D)17.98 <div style=padding-top: 35px> A random sample of 8 employees provides the following: <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, if an employee who had been with the company 5 years scored a 9 on the aptitude test, what would his estimated expected sales be?</strong> A)79.09 B)60.88 C)55.62 D)17.98 <div style=padding-top: 35px>
Referring to Scenario 14-1, if an employee who had been with the company 5 years scored a 9 on the aptitude test, what would his estimated expected sales be?

A)79.09
B)60.88
C)55.62
D)17.98
سؤال
In a multiple regression problem involving two independent variables, if <strong>In a multiple regression problem involving two independent variables, if   is computed to be +2.0, it means that</strong> A)the relationship between   and Y is significant. B)the estimated mean of Y increases by 2 units for each increase of 1 unit of   holding   constant. C)the estimated mean of Y increases by 2 units for each increase of 1 unit of   without regard to   D)the estimated mean of Y is 2 when   equals zero. <div style=padding-top: 35px> is computed to be +2.0, it means that

A)the relationship between <strong>In a multiple regression problem involving two independent variables, if   is computed to be +2.0, it means that</strong> A)the relationship between   and Y is significant. B)the estimated mean of Y increases by 2 units for each increase of 1 unit of   holding   constant. C)the estimated mean of Y increases by 2 units for each increase of 1 unit of   without regard to   D)the estimated mean of Y is 2 when   equals zero. <div style=padding-top: 35px> and Y is significant.
B)the estimated mean of Y increases by 2 units for each increase of 1 unit of <strong>In a multiple regression problem involving two independent variables, if   is computed to be +2.0, it means that</strong> A)the relationship between   and Y is significant. B)the estimated mean of Y increases by 2 units for each increase of 1 unit of   holding   constant. C)the estimated mean of Y increases by 2 units for each increase of 1 unit of   without regard to   D)the estimated mean of Y is 2 when   equals zero. <div style=padding-top: 35px> holding <strong>In a multiple regression problem involving two independent variables, if   is computed to be +2.0, it means that</strong> A)the relationship between   and Y is significant. B)the estimated mean of Y increases by 2 units for each increase of 1 unit of   holding   constant. C)the estimated mean of Y increases by 2 units for each increase of 1 unit of   without regard to   D)the estimated mean of Y is 2 when   equals zero. <div style=padding-top: 35px> constant.
C)the estimated mean of Y increases by 2 units for each increase of 1 unit of <strong>In a multiple regression problem involving two independent variables, if   is computed to be +2.0, it means that</strong> A)the relationship between   and Y is significant. B)the estimated mean of Y increases by 2 units for each increase of 1 unit of   holding   constant. C)the estimated mean of Y increases by 2 units for each increase of 1 unit of   without regard to   D)the estimated mean of Y is 2 when   equals zero. <div style=padding-top: 35px> without regard to <strong>In a multiple regression problem involving two independent variables, if   is computed to be +2.0, it means that</strong> A)the relationship between   and Y is significant. B)the estimated mean of Y increases by 2 units for each increase of 1 unit of   holding   constant. C)the estimated mean of Y increases by 2 units for each increase of 1 unit of   without regard to   D)the estimated mean of Y is 2 when   equals zero. <div style=padding-top: 35px>
D)the estimated mean of Y is 2 when <strong>In a multiple regression problem involving two independent variables, if   is computed to be +2.0, it means that</strong> A)the relationship between   and Y is significant. B)the estimated mean of Y increases by 2 units for each increase of 1 unit of   holding   constant. C)the estimated mean of Y increases by 2 units for each increase of 1 unit of   without regard to   D)the estimated mean of Y is 2 when   equals zero. <div style=padding-top: 35px> equals zero.
سؤال
SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, for these data, what is the value for the regression constant,  </strong> A)0.998 B)3.103 C)4.698 D)21.293 <div style=padding-top: 35px> and how he/she scored on a business aptitude test <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, for these data, what is the value for the regression constant,  </strong> A)0.998 B)3.103 C)4.698 D)21.293 <div style=padding-top: 35px> A random sample of 8 employees provides the following: <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, for these data, what is the value for the regression constant,  </strong> A)0.998 B)3.103 C)4.698 D)21.293 <div style=padding-top: 35px>
Referring to Scenario 14-1, for these data, what is the value for the regression constant, <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, for these data, what is the value for the regression constant,  </strong> A)0.998 B)3.103 C)4.698 D)21.293 <div style=padding-top: 35px>

A)0.998
B)3.103
C)4.698
D)21.293
سؤال
SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, suppose an employee had never taken an economics course and managed to score a 5 on his performance rating.What is his estimated expected wage rate?</strong> A)10.90 B)12.20 C)17.23 D)25.11 <div style=padding-top: 35px> and the number of economics courses the employee successfully completed in college <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, suppose an employee had never taken an economics course and managed to score a 5 on his performance rating.What is his estimated expected wage rate?</strong> A)10.90 B)12.20 C)17.23 D)25.11 <div style=padding-top: 35px> The professor randomly selects 6 workers and collects the following information: <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, suppose an employee had never taken an economics course and managed to score a 5 on his performance rating.What is his estimated expected wage rate?</strong> A)10.90 B)12.20 C)17.23 D)25.11 <div style=padding-top: 35px>
Referring to Scenario 14-2, suppose an employee had never taken an economics course and managed to score a 5 on his performance rating.What is his estimated expected wage rate?

A)10.90
B)12.20
C)17.23
D)25.11
سؤال
SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, for these data, what is the estimated coefficient for the number of economics courses taken,  </strong> A)0.616 B)1.054 C)6.932 D)9.103 <div style=padding-top: 35px> and the number of economics courses the employee successfully completed in college <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, for these data, what is the estimated coefficient for the number of economics courses taken,  </strong> A)0.616 B)1.054 C)6.932 D)9.103 <div style=padding-top: 35px> The professor randomly selects 6 workers and collects the following information: <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, for these data, what is the estimated coefficient for the number of economics courses taken,  </strong> A)0.616 B)1.054 C)6.932 D)9.103 <div style=padding-top: 35px>
Referring to Scenario 14-2, for these data, what is the estimated coefficient for the number of economics courses taken, <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, for these data, what is the estimated coefficient for the number of economics courses taken,  </strong> A)0.616 B)1.054 C)6.932 D)9.103 <div style=padding-top: 35px>

A)0.616
B)1.054
C)6.932
D)9.103
سؤال
In a multiple regression model, the value of the coefficient of multiple determination

A)has to fall between -1 and +1.
B)has to fall between 0 and +1.
C)has to fall between -1 and 0.
D)can fall between any pair of real numbers.
سؤال
SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, for these data, what is the estimated coefficient for the variable representing scores on the aptitude test,  </strong> A)0.998 B)3.103 C)4.698 D)21.293 <div style=padding-top: 35px> and how he/she scored on a business aptitude test <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, for these data, what is the estimated coefficient for the variable representing scores on the aptitude test,  </strong> A)0.998 B)3.103 C)4.698 D)21.293 <div style=padding-top: 35px> A random sample of 8 employees provides the following: <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, for these data, what is the estimated coefficient for the variable representing scores on the aptitude test,  </strong> A)0.998 B)3.103 C)4.698 D)21.293 <div style=padding-top: 35px>
Referring to Scenario 14-1, for these data, what is the estimated coefficient for the variable representing scores on the aptitude test, <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, for these data, what is the estimated coefficient for the variable representing scores on the aptitude test,  </strong> A)0.998 B)3.103 C)4.698 D)21.293 <div style=padding-top: 35px>

A)0.998
B)3.103
C)4.698
D)21.293
سؤال
SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, for these data, what is the estimated coefficient for the variable representing years an employee has been with the company,  </strong> A)0.998 B)3.103 C)4.698 D)21.293 <div style=padding-top: 35px> and how he/she scored on a business aptitude test <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, for these data, what is the estimated coefficient for the variable representing years an employee has been with the company,  </strong> A)0.998 B)3.103 C)4.698 D)21.293 <div style=padding-top: 35px> A random sample of 8 employees provides the following: <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, for these data, what is the estimated coefficient for the variable representing years an employee has been with the company,  </strong> A)0.998 B)3.103 C)4.698 D)21.293 <div style=padding-top: 35px>
Referring to Scenario 14-1, for these data, what is the estimated coefficient for the variable representing years an employee has been with the company, <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, for these data, what is the estimated coefficient for the variable representing years an employee has been with the company,  </strong> A)0.998 B)3.103 C)4.698 D)21.293 <div style=padding-top: 35px>

A)0.998
B)3.103
C)4.698
D)21.293
سؤال
SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, for these data, what is the value for the regression constant,  </strong> A)0.616 B)1.054 C)6.932 D)9.103 <div style=padding-top: 35px> and the number of economics courses the employee successfully completed in college <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, for these data, what is the value for the regression constant,  </strong> A)0.616 B)1.054 C)6.932 D)9.103 <div style=padding-top: 35px> The professor randomly selects 6 workers and collects the following information: <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, for these data, what is the value for the regression constant,  </strong> A)0.616 B)1.054 C)6.932 D)9.103 <div style=padding-top: 35px>
Referring to Scenario 14-2, for these data, what is the value for the regression constant, <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, for these data, what is the value for the regression constant,  </strong> A)0.616 B)1.054 C)6.932 D)9.103 <div style=padding-top: 35px>

A)0.616
B)1.054
C)6.932
D)9.103
سؤال
SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, the p-value for the aggregated price index is</strong> A)0.05 B)0.01 C)0.001 D)None of the above. <div style=padding-top: 35px>
Referring to Scenario 14-3, the p-value for the aggregated price index is

A)0.05
B)0.01
C)0.001
D)None of the above.
سؤال
The coefficient of multiple determination <strong>The coefficient of multiple determination  </strong> A)measures the variation around the predicted regression equation. B)measures the proportion of variation in Y that is explained by   C)measures the proportion of variation in Y that is explained by   holding   constant. D)will have the same sign as   <div style=padding-top: 35px>

A)measures the variation around the predicted regression equation.
B)measures the proportion of variation in Y that is explained by <strong>The coefficient of multiple determination  </strong> A)measures the variation around the predicted regression equation. B)measures the proportion of variation in Y that is explained by   C)measures the proportion of variation in Y that is explained by   holding   constant. D)will have the same sign as   <div style=padding-top: 35px>
C)measures the proportion of variation in Y that is explained by <strong>The coefficient of multiple determination  </strong> A)measures the variation around the predicted regression equation. B)measures the proportion of variation in Y that is explained by   C)measures the proportion of variation in Y that is explained by   holding   constant. D)will have the same sign as   <div style=padding-top: 35px> holding <strong>The coefficient of multiple determination  </strong> A)measures the variation around the predicted regression equation. B)measures the proportion of variation in Y that is explained by   C)measures the proportion of variation in Y that is explained by   holding   constant. D)will have the same sign as   <div style=padding-top: 35px> constant.
D)will have the same sign as <strong>The coefficient of multiple determination  </strong> A)measures the variation around the predicted regression equation. B)measures the proportion of variation in Y that is explained by   C)measures the proportion of variation in Y that is explained by   holding   constant. D)will have the same sign as   <div style=padding-top: 35px>
سؤال
SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, the p-value for GDP is</strong> A)0.05 B)0.01 C)0.001 D)None of the above. <div style=padding-top: 35px>
Referring to Scenario 14-3, the p-value for GDP is

A)0.05
B)0.01
C)0.001
D)None of the above.
سؤال
SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, when the economist used a simple linear regression model with consumption as the dependent variable and GDP as the independent variable, he obtained an   value of 0.971.What additional percentage of the total variation of consumption has been explained by including aggregate prices in the multiple regression?</strong> A)98.2 B)11.1 C)2.8 D)1.1 <div style=padding-top: 35px>
Referring to Scenario 14-3, when the economist used a simple linear regression model with consumption as the dependent variable and GDP as the independent variable, he obtained an <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, when the economist used a simple linear regression model with consumption as the dependent variable and GDP as the independent variable, he obtained an   value of 0.971.What additional percentage of the total variation of consumption has been explained by including aggregate prices in the multiple regression?</strong> A)98.2 B)11.1 C)2.8 D)1.1 <div style=padding-top: 35px> value of 0.971.What additional percentage of the total variation of consumption has been explained by including aggregate prices in the multiple regression?

A)98.2
B)11.1
C)2.8
D)1.1
سؤال
SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, to test whether gross domestic product has a positive impact on consumption, the p-value is</strong> A)0.00005 B)0.0001 C)0.9999 D)0.99995 <div style=padding-top: 35px>
Referring to Scenario 14-3, to test whether gross domestic product has a positive impact on consumption, the p-value is

A)0.00005
B)0.0001
C)0.9999
D)0.99995
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, which of the following values for the level of significance is the smallest for which the regression model as a whole is significant?</strong> A)0.0005 B)0.001 C)0.01 D)0.05 <div style=padding-top: 35px>
Referring to Scenario 14-4, which of the following values for the level of significance is the smallest for which the regression model as a whole is significant?

A)0.0005
B)0.001
C)0.01
D)0.05
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, which of the following values for the level of significance is the smallest for which each explanatory variable is significant individually?</strong> A)0.001 B)0.010 C)0.025 D)0.050 <div style=padding-top: 35px>
Referring to Scenario 14-4, which of the following values for the level of significance is the smallest for which each explanatory variable is significant individually?

A)0.001
B)0.010
C)0.025
D)0.050
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, when the builder used a simple linear regression model with house size (House)as the dependent variable and family size (Size)as the independent variable, he obtained an r2 value of 1.25%.What additional percentage of the total variation in house size has been explained by including income in the multiple regression?</strong> A)15.00% B)70.64% C)71.50% D)73.62% <div style=padding-top: 35px>
Referring to Scenario 14-4, when the builder used a simple linear regression model with house size (House)as the dependent variable and family size (Size)as the independent variable, he obtained an r2 value of 1.25%.What additional percentage of the total variation in house size has been explained by including income in the multiple regression?

A)15.00%
B)70.64%
C)71.50%
D)73.62%
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, what annual income (in thousands of dollars)would an individual with a family size of 4 need to attain a predicted 10,000 square foot home (House = 100)?<div style=padding-top: 35px>
Referring to Scenario 14-4, what annual income (in thousands of dollars)would an individual with a family size of 4 need to attain a predicted 10,000 square foot home (House = 100)?
سؤال
SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, to test for the significance of the coefficient on gross domestic product, the   value is</strong> A)0.0001 B)0.8330 C)0.8837 D)0.9999 <div style=padding-top: 35px>
Referring to Scenario 14-3, to test for the significance of the coefficient on gross domestic product, the <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, to test for the significance of the coefficient on gross domestic product, the   value is</strong> A)0.0001 B)0.8330 C)0.8837 D)0.9999 <div style=padding-top: 35px> value is

A)0.0001
B)0.8330
C)0.8837
D)0.9999
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, what fraction of the variability in house size is explained by income and size of family?</strong> A)17.56% B)70.69% C)71.89% D)84.79% <div style=padding-top: 35px>
Referring to Scenario 14-4, what fraction of the variability in house size is explained by income and size of family?

A)17.56%
B)70.69%
C)71.89%
D)84.79%
سؤال
SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, one economy in the sample had an aggregate consumption level of $3 billion, a GDP of $3.5 billion, and an aggregate price level of 125.What is the residual for this data point?</strong> A)$2.52 billion B)$0.48 billion C)- $1.33 billion D)- $2.52 billion <div style=padding-top: 35px>
Referring to Scenario 14-3, one economy in the sample had an aggregate consumption level of $3 billion, a GDP of $3.5 billion, and an aggregate price level of 125.What is the residual for this data point?

A)$2.52 billion
B)$0.48 billion
C)- $1.33 billion
D)- $2.52 billion
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, what annual income (in thousands of dollars)would an individual with a family size of 9 need to attain a predicted 5,000 square foot home (House = 50)?<div style=padding-top: 35px>
Referring to Scenario 14-4, what annual income (in thousands of dollars)would an individual with a family size of 9 need to attain a predicted 5,000 square foot home (House = 50)?
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, which of the following values for the level of significance is the smallest for which at least one explanatory variable is significant individually?</strong> A)0.005 B)0.010 C)0.025 D)0.050 <div style=padding-top: 35px>
Referring to Scenario 14-4, which of the following values for the level of significance is the smallest for which at least one explanatory variable is significant individually?

A)0.005
B)0.010
C)0.025
D)0.050
سؤال
SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, to test for the significance of the coefficient on aggregate price index, the p-value is</strong> A)0.0001 B)0.8330 C)0.8837 D)0.9999 <div style=padding-top: 35px>
Referring to Scenario 14-3, to test for the significance of the coefficient on aggregate price index, the p-value is

A)0.0001
B)0.8330
C)0.8837
D)0.9999
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, which of the independent variables in the model are significant at the 5% level?</strong> A)Income only B)Size only C)Income and Size D)None <div style=padding-top: 35px>
Referring to Scenario 14-4, which of the independent variables in the model are significant at the 5% level?

A)Income only
B)Size only
C)Income and Size
D)None
سؤال
SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, what is the estimated mean consumption level for an economy with GDP equal to $2 billion and an aggregate price index of 90?</strong> A)$1.39 billion B)$2.89 billion C)$4.75 billion D)$9.45 billion <div style=padding-top: 35px>
Referring to Scenario 14-3, what is the estimated mean consumption level for an economy with GDP equal to $2 billion and an aggregate price index of 90?

A)$1.39 billion
B)$2.89 billion
C)$4.75 billion
D)$9.45 billion
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, one individual in the sample had an annual income of $100,000 and a family size of 10.This individual owned a home with an area of 7,000 square feet (House = 70.00).What is the residual (in hundreds of square feet)for this data point?<div style=padding-top: 35px>
Referring to Scenario 14-4, one individual in the sample had an annual income of $100,000 and a family size of 10.This individual owned a home with an area of 7,000 square feet (House = 70.00).What is the residual (in hundreds of square feet)for this data point?
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, which of the following values for the level of significance is the smallest for which at most one explanatory variable is significant individually?</strong> A)0.001 B)0.010 C)0.025 D)0.050 <div style=padding-top: 35px>
Referring to Scenario 14-4, which of the following values for the level of significance is the smallest for which at most one explanatory variable is significant individually?

A)0.001
B)0.010
C)0.025
D)0.050
سؤال
SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, to test whether aggregate price index has a negative impact on consumption, the p-value is _______?</strong> A)0.0001 B)0.4165 C)0.8330 D)0.8837 <div style=padding-top: 35px>
Referring to Scenario 14-3, to test whether aggregate price index has a negative impact on consumption, the p-value is _______?

A)0.0001
B)0.4165
C)0.8330
D)0.8837
سؤال
SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, one economy in the sample had an aggregate consumption level of $4 billion, a GDP of $6 billion, and an aggregate price level of 200.What is the residual for this data point?</strong> A)$4.39 billion B)$0.39 billion C)- $0.39 billion D)- $1.33 billion <div style=padding-top: 35px>
Referring to Scenario 14-3, one economy in the sample had an aggregate consumption level of $4 billion, a GDP of $6 billion, and an aggregate price level of 200.What is the residual for this data point?

A)$4.39 billion
B)$0.39 billion
C)- $0.39 billion
D)- $1.33 billion
سؤال
SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, to test for the significance of the coefficient on aggregate price, the value of the relevant t-statistic is</strong> A)2.365 B)0.143 C)- 0.219 D)- 1.960 <div style=padding-top: 35px>
Referring to Scenario 14-3, to test for the significance of the coefficient on aggregate price, the value of the relevant t-statistic is

A)2.365
B)0.143
C)- 0.219
D)- 1.960
سؤال
SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, to test whether aggregate price index has a positive impact on consumption, the p-value is</strong> A)0.0001 B)0.4165 C)0.5835 D)0.8330 <div style=padding-top: 35px>
Referring to Scenario 14-3, to test whether aggregate price index has a positive impact on consumption, the p-value is

A)0.0001
B)0.4165
C)0.5835
D)0.8330
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, what is the predicted house size (in hundreds of square feet)for an individual earning an annual income of $40,000 and having a family size of 4?<div style=padding-top: 35px>
Referring to Scenario 14-4, what is the predicted house size (in hundreds of square feet)for an individual earning an annual income of $40,000 and having a family size of 4?
سؤال
SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, what fraction of the variability in sales is explained by spending on capital and wages?</strong> A)27.0% B)50.9% C)68.9% D)83.0% <div style=padding-top: 35px>
Referring to Scenario 14-5, what fraction of the variability in sales is explained by spending on capital and wages?

A)27.0%
B)50.9%
C)68.9%
D)83.0%
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, at the 0.01 level of significance, what conclusion should the builder draw regarding the inclusion of Size in the regression model?</strong> A)Size is significant in explaining house size and should be included in the model because its p-value is less than 0.01. B)Size is significant in explaining house size and should be included in the model because its p-value is more than 0.01. C)Size is not significant in explaining house size and should not be included in the model because its p-value is less than 0.01. D)Size is not significant in explaining house size and should not be included in the model because its p-value is more than 0.01. <div style=padding-top: 35px>
Referring to Scenario 14-4, at the 0.01 level of significance, what conclusion should the builder draw regarding the inclusion of Size in the regression model?

A)Size is significant in explaining house size and should be included in the model because its p-value is less than 0.01.
B)Size is significant in explaining house size and should be included in the model because its p-value is more than 0.01.
C)Size is not significant in explaining house size and should not be included in the model because its p-value is less than 0.01.
D)Size is not significant in explaining house size and should not be included in the model because its p-value is more than 0.01.
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the value of the partial F test statistic is ____ for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included<div style=padding-top: 35px>
Referring to Scenario 14-4, the value of the partial F test statistic is ____ for H₀ : Variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the value of the partial F test statistic is ____ for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included<div style=padding-top: 35px> does not significantly improve the model after variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the value of the partial F test statistic is ____ for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included<div style=padding-top: 35px> has been included H₁ : Variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the value of the partial F test statistic is ____ for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included<div style=padding-top: 35px> significantly improves the model after variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the value of the partial F test statistic is ____ for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included<div style=padding-top: 35px> has been included
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the partial F test for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included has ____ and ____ degrees of freedom.<div style=padding-top: 35px>
Referring to Scenario 14-4, the partial F test for H₀ : Variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the partial F test for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included has ____ and ____ degrees of freedom.<div style=padding-top: 35px> does not significantly improve the model after variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the partial F test for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included has ____ and ____ degrees of freedom.<div style=padding-top: 35px> has been included H₁ : Variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the partial F test for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included has ____ and ____ degrees of freedom.<div style=padding-top: 35px> significantly improves the model after variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the partial F test for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included has ____ and ____ degrees of freedom.<div style=padding-top: 35px> has been included has ____ and ____ degrees of freedom.
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the coefficient of partial determination   is ____.<div style=padding-top: 35px>
Referring to Scenario 14-4, the coefficient of partial determination SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the coefficient of partial determination   is ____.<div style=padding-top: 35px> is ____.
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, ____% of the variation in the house size can be explained by the variation in the family income while holding the family size constant.<div style=padding-top: 35px>
Referring to Scenario 14-4, ____% of the variation in the house size can be explained by the variation in the family income while holding the family size constant.
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, suppose the builder wants to test whether the coefficient on Size is significantly different from 0.What is the value of the relevant t-statistic?</strong> A)- 0.7630 B)3.2708 C)10.8668 D)60.0864 <div style=padding-top: 35px>
Referring to Scenario 14-4, suppose the builder wants to test whether the coefficient on Size is significantly different from 0.What is the value of the relevant t-statistic?

A)- 0.7630
B)3.2708
C)10.8668
D)60.0864
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, one individual in the sample had an annual income of $40,000 and a family size of 1.This individual owned a home with an area of 1,000 square feet (House = 10.00).What is the residual (in hundreds of square feet)for this data point?<div style=padding-top: 35px>
Referring to Scenario 14-4, one individual in the sample had an annual income of $40,000 and a family size of 1.This individual owned a home with an area of 1,000 square feet (House = 10.00).What is the residual (in hundreds of square feet)for this data point?
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, suppose the builder wants to test whether the coefficient on Income is significantly different from 0.What is the value of the relevant t-statistic?</strong> A)-0.7630 B)3.2708 C)10.8668 D)60.0864 <div style=padding-top: 35px>
Referring to Scenario 14-4, suppose the builder wants to test whether the coefficient on Income is significantly different from 0.What is the value of the relevant t-statistic?

A)-0.7630
B)3.2708
C)10.8668
D)60.0864
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the partial F test for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included has ____ and ____ degrees of freedom.<div style=padding-top: 35px>
Referring to Scenario 14-4, the partial F test for H₀ : Variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the partial F test for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included has ____ and ____ degrees of freedom.<div style=padding-top: 35px> does not significantly improve the model after variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the partial F test for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included has ____ and ____ degrees of freedom.<div style=padding-top: 35px> has been included H₁ : Variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the partial F test for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included has ____ and ____ degrees of freedom.<div style=padding-top: 35px> significantly improves the model after variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the partial F test for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included has ____ and ____ degrees of freedom.<div style=padding-top: 35px> has been included has ____ and ____ degrees of freedom.
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the coefficient of partial determination   is ____.<div style=padding-top: 35px>
Referring to Scenario 14-4, the coefficient of partial determination SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the coefficient of partial determination   is ____.<div style=padding-top: 35px> is ____.
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the observed value of the F-statistic is missing from the printout.What are the degrees of freedom for this F-statistic?</strong> A)2 for the numerator, 47 for the denominator B)2 for the numerator, 49 for the denominator C)49 for the numerator, 47 for the denominator D)47 for the numerator, 49 for the denominator <div style=padding-top: 35px>
Referring to Scenario 14-4, the observed value of the F-statistic is missing from the printout.What are the degrees of freedom for this F-statistic?

A)2 for the numerator, 47 for the denominator
B)2 for the numerator, 49 for the denominator
C)49 for the numerator, 47 for the denominator
D)47 for the numerator, 49 for the denominator
سؤال
SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, which of the independent variables in the model are significant at the 5% level?</strong> A)Capital, Wages B)Capital C)Wages D)None of the above <div style=padding-top: 35px>
Referring to Scenario 14-5, which of the independent variables in the model are significant at the 5% level?

A)Capital, Wages
B)Capital
C)Wages
D)None of the above
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4 and allowing for a 1% probability of committing a type I error, what is the decision and conclusion for the test  </strong> A)Do not reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on house size. B)Do not reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on house size. C)Reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on house size. D)Reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on house size. <div style=padding-top: 35px>
Referring to Scenario 14-4 and allowing for a 1% probability of committing a type I error, what is the decision and conclusion for the test <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4 and allowing for a 1% probability of committing a type I error, what is the decision and conclusion for the test  </strong> A)Do not reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on house size. B)Do not reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on house size. C)Reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on house size. D)Reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on house size. <div style=padding-top: 35px>

A)Do not reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on house size.
B)Do not reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on house size.
C)Reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on house size.
D)Reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on house size.
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the value of the partial F test statistic is ____ for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included<div style=padding-top: 35px>
Referring to Scenario 14-4, the value of the partial F test statistic is ____ for H₀ : Variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the value of the partial F test statistic is ____ for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included<div style=padding-top: 35px> does not significantly improve the model after variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the value of the partial F test statistic is ____ for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included<div style=padding-top: 35px> has been included H₁ : Variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the value of the partial F test statistic is ____ for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included<div style=padding-top: 35px> significantly improves the model after variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the value of the partial F test statistic is ____ for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included<div style=padding-top: 35px> has been included
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, what is the value of the calculated F test statistic that is missing from the output for testing whether the whole regression model is significant?<div style=padding-top: 35px>
Referring to Scenario 14-4, what is the value of the calculated F test statistic that is missing from the output for testing whether the whole regression model is significant?
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, at the 0.01 level of significance, what conclusion should the builder reach regarding the inclusion of Income in the regression model?</strong> A)Income is significant in explaining house size and should be included in the model because its p-value is less than 0.01. B)Income is significant in explaining house size and should be included in the model because its p-value is more than 0.01. C)Income is not significant in explaining house size and should not be included in the model because its p-value is less than 0.01. D)Income is not significant in explaining house size and should not be included in the model because its p-value is more than 0.01. <div style=padding-top: 35px>
Referring to Scenario 14-4, at the 0.01 level of significance, what conclusion should the builder reach regarding the inclusion of Income in the regression model?

A)Income is significant in explaining house size and should be included in the model because its p-value is less than 0.01.
B)Income is significant in explaining house size and should be included in the model because its p-value is more than 0.01.
C)Income is not significant in explaining house size and should not be included in the model because its p-value is less than 0.01.
D)Income is not significant in explaining house size and should not be included in the model because its p-value is more than 0.01.
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, ____% of the variation in the house size can be explained by the variation in the family size while holding the family income constant.<div style=padding-top: 35px>
Referring to Scenario 14-4, ____% of the variation in the house size can be explained by the variation in the family size while holding the family income constant.
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, what are the regression degrees of freedom that are missing from the output?</strong> A)2 B)47 C)49 D)50 <div style=padding-top: 35px>
Referring to Scenario 14-4, what are the regression degrees of freedom that are missing from the output?

A)2
B)47
C)49
D)50
سؤال
SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, what are the residual degrees of freedom that are missing from the output?</strong> A)2 B)47 C)49 D)50 <div style=padding-top: 35px>
Referring to Scenario 14-4, what are the residual degrees of freedom that are missing from the output?

A)2
B)47
C)49
D)50
سؤال
SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, at the 0.01 level of significance, what conclusion should the microeconomist reach regarding the inclusion of Capital in the regression model?</strong> A)Capital is significant in explaining corporate sales and should be included in the model because its p-value is less than 0.01. B)Capital is significant in explaining corporate sales and should be included in the model because its p-value is more than 0.01. C)Capital is not significant in explaining corporate sales and should not be included in the model because its p-value is less than 0.01. D)Capital is not significant in explaining corporate sales and should not be included in the model because its p-value is more than 0.01. <div style=padding-top: 35px>
Referring to Scenario 14-5, at the 0.01 level of significance, what conclusion should the microeconomist reach regarding the inclusion of Capital in the regression model?

A)Capital is significant in explaining corporate sales and should be included in the model because its p-value is less than 0.01.
B)Capital is significant in explaining corporate sales and should be included in the model because its p-value is more than 0.01.
C)Capital is not significant in explaining corporate sales and should not be included in the model because its p-value is less than 0.01.
D)Capital is not significant in explaining corporate sales and should not be included in the model because its p-value is more than 0.01.
سؤال
SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, what is the p-value for Capital?</strong> A)0.01 B)0.025 C)0.05 D)None of the above <div style=padding-top: 35px>
Referring to Scenario 14-5, what is the p-value for Capital?

A)0.01
B)0.025
C)0.05
D)None of the above
سؤال
SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, what is the 95% confidence interval for the expected change in heating costs as a result of a 1 degree Fahrenheit change in the daily minimum outside temperature?</strong> A)[256.7522, 639.8328] B)[204.7854, 497.1733] C)[-5.3721, -0.1520] D)[-37.1736, 5.2919] <div style=padding-top: 35px> and the amount of insulation in inches <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, what is the 95% confidence interval for the expected change in heating costs as a result of a 1 degree Fahrenheit change in the daily minimum outside temperature?</strong> A)[256.7522, 639.8328] B)[204.7854, 497.1733] C)[-5.3721, -0.1520] D)[-37.1736, 5.2919] <div style=padding-top: 35px> Given below is EXCEL output of the regression model. <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, what is the 95% confidence interval for the expected change in heating costs as a result of a 1 degree Fahrenheit change in the daily minimum outside temperature?</strong> A)[256.7522, 639.8328] B)[204.7854, 497.1733] C)[-5.3721, -0.1520] D)[-37.1736, 5.2919] <div style=padding-top: 35px> <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, what is the 95% confidence interval for the expected change in heating costs as a result of a 1 degree Fahrenheit change in the daily minimum outside temperature?</strong> A)[256.7522, 639.8328] B)[204.7854, 497.1733] C)[-5.3721, -0.1520] D)[-37.1736, 5.2919] <div style=padding-top: 35px>
Referring to Scenario 14-6, what is the 95% confidence interval for the expected change in heating costs as a result of a 1 degree Fahrenheit change in the daily minimum outside temperature?

A)[256.7522, 639.8328]
B)[204.7854, 497.1733]
C)[-5.3721, -0.1520]
D)[-37.1736, 5.2919]
سؤال
SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6 and allowing for a 1% probability of committing a type I error, what is the decision and conclusion for the test   At least one  </strong> A)Do not reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs. B)Do not reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs. C)Reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs. D)Reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs. <div style=padding-top: 35px> and the amount of insulation in inches <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6 and allowing for a 1% probability of committing a type I error, what is the decision and conclusion for the test   At least one  </strong> A)Do not reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs. B)Do not reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs. C)Reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs. D)Reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs. <div style=padding-top: 35px> Given below is EXCEL output of the regression model. <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6 and allowing for a 1% probability of committing a type I error, what is the decision and conclusion for the test   At least one  </strong> A)Do not reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs. B)Do not reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs. C)Reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs. D)Reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs. <div style=padding-top: 35px> <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6 and allowing for a 1% probability of committing a type I error, what is the decision and conclusion for the test   At least one  </strong> A)Do not reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs. B)Do not reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs. C)Reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs. D)Reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs. <div style=padding-top: 35px>
Referring to Scenario 14-6 and allowing for a 1% probability of committing a type I error, what is the decision and conclusion for the test <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6 and allowing for a 1% probability of committing a type I error, what is the decision and conclusion for the test   At least one  </strong> A)Do not reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs. B)Do not reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs. C)Reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs. D)Reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs. <div style=padding-top: 35px> At least one <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6 and allowing for a 1% probability of committing a type I error, what is the decision and conclusion for the test   At least one  </strong> A)Do not reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs. B)Do not reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs. C)Reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs. D)Reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs. <div style=padding-top: 35px>

A)Do not reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs.
B)Do not reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs.
C)Reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs.
D)Reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs.
سؤال
SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, when the microeconomist used a simple linear regression model with sales as the dependent variable and wages as the independent variable, she obtained an   value of 0.601.What additional percentage of the total variation of sales has been explained by including capital spending in the multiple regression?</strong> A)60.1% B)31.1% C)22.9% D)8.8% <div style=padding-top: 35px>
Referring to Scenario 14-5, when the microeconomist used a simple linear regression model with sales as the dependent variable and wages as the independent variable, she obtained an <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, when the microeconomist used a simple linear regression model with sales as the dependent variable and wages as the independent variable, she obtained an   value of 0.601.What additional percentage of the total variation of sales has been explained by including capital spending in the multiple regression?</strong> A)60.1% B)31.1% C)22.9% D)8.8% <div style=padding-top: 35px> value of 0.601.What additional percentage of the total variation of sales has been explained by including capital spending in the multiple regression?

A)60.1%
B)31.1%
C)22.9%
D)8.8%
سؤال
SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, one company in the sample had sales of $21.439 billion (Sales = 21,439).This company spent $300 million on capital and $700 million on wages. What is the residual (in millions of dollars)for this data point?</strong> A)790.69 B)648.31 C)-648.31 D)-790.69 <div style=padding-top: 35px>
Referring to Scenario 14-5, one company in the sample had sales of $21.439 billion (Sales = 21,439).This company spent $300 million on capital and $700 million on wages. What is the residual (in millions of dollars)for this data point?

A)790.69
B)648.31
C)-648.31
D)-790.69
سؤال
SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, what is the p-value for testing whether Capital has a negative influence on corporate sales?</strong> A)0.05 B)0.2743 C)0.5485 D)0.7258 <div style=padding-top: 35px>
Referring to Scenario 14-5, what is the p-value for testing whether Capital has a negative influence on corporate sales?

A)0.05
B)0.2743
C)0.5485
D)0.7258
سؤال
SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, suppose the microeconomist wants to test whether the coefficient on Capital is significantly different from 0.What is the value of the relevant t-statistic?</strong> A)0.609 B)2.617 C)4.804 D)25.432 <div style=padding-top: 35px>
Referring to Scenario 14-5, suppose the microeconomist wants to test whether the coefficient on Capital is significantly different from 0.What is the value of the relevant t-statistic?

A)0.609
B)2.617
C)4.804
D)25.432
سؤال
SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, what is your decision and conclusion for the test   level of significance?</strong> A)Do not reject H₀ and conclude that the amount of insulation has a linear effect on heating costs. B)Reject H₀ and conclude that the amount of insulation does not have a linear effect on heating costs. C)Reject H₀ and conclude that the amount of insulation has a linear effect on heating costs. D)Do not reject H₀ and conclude that the amount of insulation does not have a linear effect on heating costs. <div style=padding-top: 35px> and the amount of insulation in inches <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, what is your decision and conclusion for the test   level of significance?</strong> A)Do not reject H₀ and conclude that the amount of insulation has a linear effect on heating costs. B)Reject H₀ and conclude that the amount of insulation does not have a linear effect on heating costs. C)Reject H₀ and conclude that the amount of insulation has a linear effect on heating costs. D)Do not reject H₀ and conclude that the amount of insulation does not have a linear effect on heating costs. <div style=padding-top: 35px> Given below is EXCEL output of the regression model. <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, what is your decision and conclusion for the test   level of significance?</strong> A)Do not reject H₀ and conclude that the amount of insulation has a linear effect on heating costs. B)Reject H₀ and conclude that the amount of insulation does not have a linear effect on heating costs. C)Reject H₀ and conclude that the amount of insulation has a linear effect on heating costs. D)Do not reject H₀ and conclude that the amount of insulation does not have a linear effect on heating costs. <div style=padding-top: 35px> <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, what is your decision and conclusion for the test   level of significance?</strong> A)Do not reject H₀ and conclude that the amount of insulation has a linear effect on heating costs. B)Reject H₀ and conclude that the amount of insulation does not have a linear effect on heating costs. C)Reject H₀ and conclude that the amount of insulation has a linear effect on heating costs. D)Do not reject H₀ and conclude that the amount of insulation does not have a linear effect on heating costs. <div style=padding-top: 35px>
Referring to Scenario 14-6, what is your decision and conclusion for the test <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, what is your decision and conclusion for the test   level of significance?</strong> A)Do not reject H₀ and conclude that the amount of insulation has a linear effect on heating costs. B)Reject H₀ and conclude that the amount of insulation does not have a linear effect on heating costs. C)Reject H₀ and conclude that the amount of insulation has a linear effect on heating costs. D)Do not reject H₀ and conclude that the amount of insulation does not have a linear effect on heating costs. <div style=padding-top: 35px> level of significance?

A)Do not reject H₀ and conclude that the amount of insulation has a linear effect on heating costs.
B)Reject H₀ and conclude that the amount of insulation does not have a linear effect on heating costs.
C)Reject H₀ and conclude that the amount of insulation has a linear effect on heating costs.
D)Do not reject H₀ and conclude that the amount of insulation does not have a linear effect on heating costs.
سؤال
SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, what are the predicted sales (in millions of dollars)for a company spending $500 million on capital and $200 million on wages?</strong> A)15,800.00 B)16,520.07 C)17,277.49 D)20,455.98 <div style=padding-top: 35px>
Referring to Scenario 14-5, what are the predicted sales (in millions of dollars)for a company spending $500 million on capital and $200 million on wages?

A)15,800.00
B)16,520.07
C)17,277.49
D)20,455.98
سؤال
SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, one company in the sample had sales of $20 billion (Sales = 20,000).This company spent $300 million on capital and $700 million on wages. What is the residual (in millions of dollars)for this data point?</strong> A)874.55 B)622.87 C)-790.69 D)-983.56 <div style=padding-top: 35px>
Referring to Scenario 14-5, one company in the sample had sales of $20 billion (Sales = 20,000).This company spent $300 million on capital and $700 million on wages. What is the residual (in millions of dollars)for this data point?

A)874.55
B)622.87
C)-790.69
D)-983.56
سؤال
SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, what is the p-value for testing whether Wages have a positive impact on corporate sales?</strong> A)0.01 B)0.05 C)0.0001 D)0.00005 <div style=padding-top: 35px>
Referring to Scenario 14-5, what is the p-value for testing whether Wages have a positive impact on corporate sales?

A)0.01
B)0.05
C)0.0001
D)0.00005
سؤال
SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, what are the predicted sales (in millions of dollars)for a company spending $100 million on capital and $100 million on wages?</strong> A)15,800.00 B)16,520.07 C)17,277.49 D)20,455.98 <div style=padding-top: 35px>
Referring to Scenario 14-5, what are the predicted sales (in millions of dollars)for a company spending $100 million on capital and $100 million on wages?

A)15,800.00
B)16,520.07
C)17,277.49
D)20,455.98
سؤال
SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, which of the following values for   is the smallest for which the regression model as a whole is significant?</strong> A)0.00005 B)0.001 C)0.01 D)0.05 <div style=padding-top: 35px>
Referring to Scenario 14-5, which of the following values for <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, which of the following values for   is the smallest for which the regression model as a whole is significant?</strong> A)0.00005 B)0.001 C)0.01 D)0.05 <div style=padding-top: 35px> is the smallest for which the regression model as a whole is significant?

A)0.00005
B)0.001
C)0.01
D)0.05
سؤال
SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, the observed value of the F-statistic is given on the printout as 25.432.What are the degrees of freedom for this F-statistic?</strong> A)25 for the numerator, 2 for the denominator B)2 for the numerator, 23 for the denominator C)23 for the numerator, 25 for the denominator D)2 for the numerator, 25 for the denominator <div style=padding-top: 35px>
Referring to Scenario 14-5, the observed value of the F-statistic is given on the printout as 25.432.What are the degrees of freedom for this F-statistic?

A)25 for the numerator, 2 for the denominator
B)2 for the numerator, 23 for the denominator
C)23 for the numerator, 25 for the denominator
D)2 for the numerator, 25 for the denominator
سؤال
SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, what can we say about the regression model?</strong> A)The model explains 17.12% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 27.78% of the sample variability of heating Costs. B)The model explains 19.28% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 27.78% of the sample variability of heating Costs. C)The model explains 27.78% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 19.28% of the sample variability of heating Costs. D)The model explains 19.28% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 17.12% of the sample variability of heating Costs. <div style=padding-top: 35px> and the amount of insulation in inches <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, what can we say about the regression model?</strong> A)The model explains 17.12% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 27.78% of the sample variability of heating Costs. B)The model explains 19.28% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 27.78% of the sample variability of heating Costs. C)The model explains 27.78% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 19.28% of the sample variability of heating Costs. D)The model explains 19.28% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 17.12% of the sample variability of heating Costs. <div style=padding-top: 35px> Given below is EXCEL output of the regression model. <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, what can we say about the regression model?</strong> A)The model explains 17.12% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 27.78% of the sample variability of heating Costs. B)The model explains 19.28% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 27.78% of the sample variability of heating Costs. C)The model explains 27.78% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 19.28% of the sample variability of heating Costs. D)The model explains 19.28% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 17.12% of the sample variability of heating Costs. <div style=padding-top: 35px> <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, what can we say about the regression model?</strong> A)The model explains 17.12% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 27.78% of the sample variability of heating Costs. B)The model explains 19.28% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 27.78% of the sample variability of heating Costs. C)The model explains 27.78% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 19.28% of the sample variability of heating Costs. D)The model explains 19.28% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 17.12% of the sample variability of heating Costs. <div style=padding-top: 35px>
Referring to Scenario 14-6, what can we say about the regression model?

A)The model explains 17.12% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 27.78% of the sample variability of heating
Costs.
B)The model explains 19.28% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 27.78% of the sample variability of heating
Costs.
C)The model explains 27.78% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 19.28% of the sample variability of heating
Costs.
D)The model explains 19.28% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 17.12% of the sample variability of heating
Costs.
سؤال
SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, what is the p-value for Wages?</strong> A)0.01 B)0.05 C)0.0001 D)None of the above <div style=padding-top: 35px>
Referring to Scenario 14-5, what is the p-value for Wages?

A)0.01
B)0.05
C)0.0001
D)None of the above
سؤال
SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, the estimated value of the regression parameter   in means that</strong> A)holding the effect of the amount of insulation constant, an estimated expected $1 increase in heating costs is associated with a decrease in the daily minimum outside Temperature by 2.76 degrees. B)holding the effect of the amount of insulation constant, a 1 degree increase in the daily minimum outside temperature results in a decrease in heating costs by $2.76. C)holding the effect of the amount of insulation constant, a 1 degree increase in the daily minimum outside temperature results in an estimated decrease in mean heating Costs by $2.76. D)holding the effect of the amount of insulation constant, a 1% increase in the daily minimum outside temperature results in an estimated decrease in mean heating costs By 2.76%. <div style=padding-top: 35px> and the amount of insulation in inches <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, the estimated value of the regression parameter   in means that</strong> A)holding the effect of the amount of insulation constant, an estimated expected $1 increase in heating costs is associated with a decrease in the daily minimum outside Temperature by 2.76 degrees. B)holding the effect of the amount of insulation constant, a 1 degree increase in the daily minimum outside temperature results in a decrease in heating costs by $2.76. C)holding the effect of the amount of insulation constant, a 1 degree increase in the daily minimum outside temperature results in an estimated decrease in mean heating Costs by $2.76. D)holding the effect of the amount of insulation constant, a 1% increase in the daily minimum outside temperature results in an estimated decrease in mean heating costs By 2.76%. <div style=padding-top: 35px> Given below is EXCEL output of the regression model. <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, the estimated value of the regression parameter   in means that</strong> A)holding the effect of the amount of insulation constant, an estimated expected $1 increase in heating costs is associated with a decrease in the daily minimum outside Temperature by 2.76 degrees. B)holding the effect of the amount of insulation constant, a 1 degree increase in the daily minimum outside temperature results in a decrease in heating costs by $2.76. C)holding the effect of the amount of insulation constant, a 1 degree increase in the daily minimum outside temperature results in an estimated decrease in mean heating Costs by $2.76. D)holding the effect of the amount of insulation constant, a 1% increase in the daily minimum outside temperature results in an estimated decrease in mean heating costs By 2.76%. <div style=padding-top: 35px> <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, the estimated value of the regression parameter   in means that</strong> A)holding the effect of the amount of insulation constant, an estimated expected $1 increase in heating costs is associated with a decrease in the daily minimum outside Temperature by 2.76 degrees. B)holding the effect of the amount of insulation constant, a 1 degree increase in the daily minimum outside temperature results in a decrease in heating costs by $2.76. C)holding the effect of the amount of insulation constant, a 1 degree increase in the daily minimum outside temperature results in an estimated decrease in mean heating Costs by $2.76. D)holding the effect of the amount of insulation constant, a 1% increase in the daily minimum outside temperature results in an estimated decrease in mean heating costs By 2.76%. <div style=padding-top: 35px>
Referring to Scenario 14-6, the estimated value of the regression parameter <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, the estimated value of the regression parameter   in means that</strong> A)holding the effect of the amount of insulation constant, an estimated expected $1 increase in heating costs is associated with a decrease in the daily minimum outside Temperature by 2.76 degrees. B)holding the effect of the amount of insulation constant, a 1 degree increase in the daily minimum outside temperature results in a decrease in heating costs by $2.76. C)holding the effect of the amount of insulation constant, a 1 degree increase in the daily minimum outside temperature results in an estimated decrease in mean heating Costs by $2.76. D)holding the effect of the amount of insulation constant, a 1% increase in the daily minimum outside temperature results in an estimated decrease in mean heating costs By 2.76%. <div style=padding-top: 35px> in means that

A)holding the effect of the amount of insulation constant, an estimated expected $1 increase in heating costs is associated with a decrease in the daily minimum outside
Temperature by 2.76 degrees.
B)holding the effect of the amount of insulation constant, a 1 degree increase in the daily minimum outside temperature results in a decrease in heating costs by $2.76.
C)holding the effect of the amount of insulation constant, a 1 degree increase in the daily minimum outside temperature results in an estimated decrease in mean heating
Costs by $2.76.
D)holding the effect of the amount of insulation constant, a 1% increase in the daily minimum outside temperature results in an estimated decrease in mean heating costs
By 2.76%.
سؤال
SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, what is the p-value for testing whether Capital has a positive influence on corporate sales?</strong> A)0.025 B)0.05 C)0.2743 D)0.5485 <div style=padding-top: 35px>
Referring to Scenario 14-5, what is the p-value for testing whether Capital has a positive influence on corporate sales?

A)0.025
B)0.05
C)0.2743
D)0.5485
سؤال
SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, what is the p-value for testing whether Wages have a negative impact on corporate sales?</strong> A)0.05 B)0.0001 C)0.00005 D)0.99995 <div style=padding-top: 35px>
Referring to Scenario 14-5, what is the p-value for testing whether Wages have a negative impact on corporate sales?

A)0.05
B)0.0001
C)0.00005
D)0.99995
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Deck 14: Introduction to Multiple Regression
1
SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, what is the estimated mean consumption level for an economy with GDP equal to $4 billion and an aggregate price index of 150?</strong> A)$1.39 billion B)$2.89 billion C)$4.75 billion D)$9.45 billion
Referring to Scenario 14-3, what is the estimated mean consumption level for an economy with GDP equal to $4 billion and an aggregate price index of 150?

A)$1.39 billion
B)$2.89 billion
C)$4.75 billion
D)$9.45 billion
$2.89 billion
2
The variation attributable to factors other than the relationship between the independent variables and the explained variable in a regression analysis is represented by

A)regression sum of squares.
B)error sum of squares.
C)total sum of squares.
D)regression mean squares.
error sum of squares.
3
SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, what is the predicted consumption level for an economy with GDP equal to $4 billion and an aggregate price index of 150?</strong> A)$1.39 billion B)$2.89 billion C)$4.75 billion D)$9.45 billion
Referring to Scenario 14-3, what is the predicted consumption level for an economy with GDP equal to $4 billion and an aggregate price index of 150?

A)$1.39 billion
B)$2.89 billion
C)$4.75 billion
D)$9.45 billion
$2.89 billion
4
SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, for these data, what is the estimated coefficient for performance rating,  </strong> A)0.616 B)1.054 C)6.932 D)9.103 and the number of economics courses the employee successfully completed in college <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, for these data, what is the estimated coefficient for performance rating,  </strong> A)0.616 B)1.054 C)6.932 D)9.103 The professor randomly selects 6 workers and collects the following information: <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, for these data, what is the estimated coefficient for performance rating,  </strong> A)0.616 B)1.054 C)6.932 D)9.103
Referring to Scenario 14-2, for these data, what is the estimated coefficient for performance rating, <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, for these data, what is the estimated coefficient for performance rating,  </strong> A)0.616 B)1.054 C)6.932 D)9.103

A)0.616
B)1.054
C)6.932
D)9.103
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SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, an employee who took 12 economics courses scores 10 on the performance rating.What is her estimated expected wage rate?</strong> A)10.90 B)12.20 C)24.87 D)25.70 and the number of economics courses the employee successfully completed in college <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, an employee who took 12 economics courses scores 10 on the performance rating.What is her estimated expected wage rate?</strong> A)10.90 B)12.20 C)24.87 D)25.70 The professor randomly selects 6 workers and collects the following information: <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, an employee who took 12 economics courses scores 10 on the performance rating.What is her estimated expected wage rate?</strong> A)10.90 B)12.20 C)24.87 D)25.70
Referring to Scenario 14-2, an employee who took 12 economics courses scores 10 on the performance rating.What is her estimated expected wage rate?

A)10.90
B)12.20
C)24.87
D)25.70
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In a multiple regression model, which of the following is correct regarding the value of the adjusted <strong>In a multiple regression model, which of the following is correct regarding the value of the adjusted   ?</strong> A)It can be negative. B)It has to be positive. C)It has to be larger than the coefficient of multiple determination. D)It can be larger than 1. ?

A)It can be negative.
B)It has to be positive.
C)It has to be larger than the coefficient of multiple determination.
D)It can be larger than 1.
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SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, the p-value for the regression model as a whole is</strong> A)0.05 B)0.01 C)0.001 D)None of the above.
Referring to Scenario 14-3, the p-value for the regression model as a whole is

A)0.05
B)0.01
C)0.001
D)None of the above.
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SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, if an employee who had been with the company 5 years scored a 9 on the aptitude test, what would his estimated expected sales be?</strong> A)79.09 B)60.88 C)55.62 D)17.98 and how he/she scored on a business aptitude test <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, if an employee who had been with the company 5 years scored a 9 on the aptitude test, what would his estimated expected sales be?</strong> A)79.09 B)60.88 C)55.62 D)17.98 A random sample of 8 employees provides the following: <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, if an employee who had been with the company 5 years scored a 9 on the aptitude test, what would his estimated expected sales be?</strong> A)79.09 B)60.88 C)55.62 D)17.98
Referring to Scenario 14-1, if an employee who had been with the company 5 years scored a 9 on the aptitude test, what would his estimated expected sales be?

A)79.09
B)60.88
C)55.62
D)17.98
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In a multiple regression problem involving two independent variables, if <strong>In a multiple regression problem involving two independent variables, if   is computed to be +2.0, it means that</strong> A)the relationship between   and Y is significant. B)the estimated mean of Y increases by 2 units for each increase of 1 unit of   holding   constant. C)the estimated mean of Y increases by 2 units for each increase of 1 unit of   without regard to   D)the estimated mean of Y is 2 when   equals zero. is computed to be +2.0, it means that

A)the relationship between <strong>In a multiple regression problem involving two independent variables, if   is computed to be +2.0, it means that</strong> A)the relationship between   and Y is significant. B)the estimated mean of Y increases by 2 units for each increase of 1 unit of   holding   constant. C)the estimated mean of Y increases by 2 units for each increase of 1 unit of   without regard to   D)the estimated mean of Y is 2 when   equals zero. and Y is significant.
B)the estimated mean of Y increases by 2 units for each increase of 1 unit of <strong>In a multiple regression problem involving two independent variables, if   is computed to be +2.0, it means that</strong> A)the relationship between   and Y is significant. B)the estimated mean of Y increases by 2 units for each increase of 1 unit of   holding   constant. C)the estimated mean of Y increases by 2 units for each increase of 1 unit of   without regard to   D)the estimated mean of Y is 2 when   equals zero. holding <strong>In a multiple regression problem involving two independent variables, if   is computed to be +2.0, it means that</strong> A)the relationship between   and Y is significant. B)the estimated mean of Y increases by 2 units for each increase of 1 unit of   holding   constant. C)the estimated mean of Y increases by 2 units for each increase of 1 unit of   without regard to   D)the estimated mean of Y is 2 when   equals zero. constant.
C)the estimated mean of Y increases by 2 units for each increase of 1 unit of <strong>In a multiple regression problem involving two independent variables, if   is computed to be +2.0, it means that</strong> A)the relationship between   and Y is significant. B)the estimated mean of Y increases by 2 units for each increase of 1 unit of   holding   constant. C)the estimated mean of Y increases by 2 units for each increase of 1 unit of   without regard to   D)the estimated mean of Y is 2 when   equals zero. without regard to <strong>In a multiple regression problem involving two independent variables, if   is computed to be +2.0, it means that</strong> A)the relationship between   and Y is significant. B)the estimated mean of Y increases by 2 units for each increase of 1 unit of   holding   constant. C)the estimated mean of Y increases by 2 units for each increase of 1 unit of   without regard to   D)the estimated mean of Y is 2 when   equals zero.
D)the estimated mean of Y is 2 when <strong>In a multiple regression problem involving two independent variables, if   is computed to be +2.0, it means that</strong> A)the relationship between   and Y is significant. B)the estimated mean of Y increases by 2 units for each increase of 1 unit of   holding   constant. C)the estimated mean of Y increases by 2 units for each increase of 1 unit of   without regard to   D)the estimated mean of Y is 2 when   equals zero. equals zero.
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SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, for these data, what is the value for the regression constant,  </strong> A)0.998 B)3.103 C)4.698 D)21.293 and how he/she scored on a business aptitude test <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, for these data, what is the value for the regression constant,  </strong> A)0.998 B)3.103 C)4.698 D)21.293 A random sample of 8 employees provides the following: <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, for these data, what is the value for the regression constant,  </strong> A)0.998 B)3.103 C)4.698 D)21.293
Referring to Scenario 14-1, for these data, what is the value for the regression constant, <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, for these data, what is the value for the regression constant,  </strong> A)0.998 B)3.103 C)4.698 D)21.293

A)0.998
B)3.103
C)4.698
D)21.293
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SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, suppose an employee had never taken an economics course and managed to score a 5 on his performance rating.What is his estimated expected wage rate?</strong> A)10.90 B)12.20 C)17.23 D)25.11 and the number of economics courses the employee successfully completed in college <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, suppose an employee had never taken an economics course and managed to score a 5 on his performance rating.What is his estimated expected wage rate?</strong> A)10.90 B)12.20 C)17.23 D)25.11 The professor randomly selects 6 workers and collects the following information: <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, suppose an employee had never taken an economics course and managed to score a 5 on his performance rating.What is his estimated expected wage rate?</strong> A)10.90 B)12.20 C)17.23 D)25.11
Referring to Scenario 14-2, suppose an employee had never taken an economics course and managed to score a 5 on his performance rating.What is his estimated expected wage rate?

A)10.90
B)12.20
C)17.23
D)25.11
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SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, for these data, what is the estimated coefficient for the number of economics courses taken,  </strong> A)0.616 B)1.054 C)6.932 D)9.103 and the number of economics courses the employee successfully completed in college <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, for these data, what is the estimated coefficient for the number of economics courses taken,  </strong> A)0.616 B)1.054 C)6.932 D)9.103 The professor randomly selects 6 workers and collects the following information: <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, for these data, what is the estimated coefficient for the number of economics courses taken,  </strong> A)0.616 B)1.054 C)6.932 D)9.103
Referring to Scenario 14-2, for these data, what is the estimated coefficient for the number of economics courses taken, <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, for these data, what is the estimated coefficient for the number of economics courses taken,  </strong> A)0.616 B)1.054 C)6.932 D)9.103

A)0.616
B)1.054
C)6.932
D)9.103
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In a multiple regression model, the value of the coefficient of multiple determination

A)has to fall between -1 and +1.
B)has to fall between 0 and +1.
C)has to fall between -1 and 0.
D)can fall between any pair of real numbers.
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SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, for these data, what is the estimated coefficient for the variable representing scores on the aptitude test,  </strong> A)0.998 B)3.103 C)4.698 D)21.293 and how he/she scored on a business aptitude test <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, for these data, what is the estimated coefficient for the variable representing scores on the aptitude test,  </strong> A)0.998 B)3.103 C)4.698 D)21.293 A random sample of 8 employees provides the following: <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, for these data, what is the estimated coefficient for the variable representing scores on the aptitude test,  </strong> A)0.998 B)3.103 C)4.698 D)21.293
Referring to Scenario 14-1, for these data, what is the estimated coefficient for the variable representing scores on the aptitude test, <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, for these data, what is the estimated coefficient for the variable representing scores on the aptitude test,  </strong> A)0.998 B)3.103 C)4.698 D)21.293

A)0.998
B)3.103
C)4.698
D)21.293
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SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, for these data, what is the estimated coefficient for the variable representing years an employee has been with the company,  </strong> A)0.998 B)3.103 C)4.698 D)21.293 and how he/she scored on a business aptitude test <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, for these data, what is the estimated coefficient for the variable representing years an employee has been with the company,  </strong> A)0.998 B)3.103 C)4.698 D)21.293 A random sample of 8 employees provides the following: <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, for these data, what is the estimated coefficient for the variable representing years an employee has been with the company,  </strong> A)0.998 B)3.103 C)4.698 D)21.293
Referring to Scenario 14-1, for these data, what is the estimated coefficient for the variable representing years an employee has been with the company, <strong>SCENARIO 14-1 A manager of a product sales group believes the number of sales made by an employee (Y) depends on how many years that employee has been with the company   and how he/she scored on a business aptitude test   A random sample of 8 employees provides the following:   Referring to Scenario 14-1, for these data, what is the estimated coefficient for the variable representing years an employee has been with the company,  </strong> A)0.998 B)3.103 C)4.698 D)21.293

A)0.998
B)3.103
C)4.698
D)21.293
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SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, for these data, what is the value for the regression constant,  </strong> A)0.616 B)1.054 C)6.932 D)9.103 and the number of economics courses the employee successfully completed in college <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, for these data, what is the value for the regression constant,  </strong> A)0.616 B)1.054 C)6.932 D)9.103 The professor randomly selects 6 workers and collects the following information: <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, for these data, what is the value for the regression constant,  </strong> A)0.616 B)1.054 C)6.932 D)9.103
Referring to Scenario 14-2, for these data, what is the value for the regression constant, <strong>SCENARIO 14-2 A professor of industrial relations believes that an individual's wage rate at a factory (Y)depends on his performance rating   and the number of economics courses the employee successfully completed in college   The professor randomly selects 6 workers and collects the following information:   Referring to Scenario 14-2, for these data, what is the value for the regression constant,  </strong> A)0.616 B)1.054 C)6.932 D)9.103

A)0.616
B)1.054
C)6.932
D)9.103
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SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, the p-value for the aggregated price index is</strong> A)0.05 B)0.01 C)0.001 D)None of the above.
Referring to Scenario 14-3, the p-value for the aggregated price index is

A)0.05
B)0.01
C)0.001
D)None of the above.
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The coefficient of multiple determination <strong>The coefficient of multiple determination  </strong> A)measures the variation around the predicted regression equation. B)measures the proportion of variation in Y that is explained by   C)measures the proportion of variation in Y that is explained by   holding   constant. D)will have the same sign as

A)measures the variation around the predicted regression equation.
B)measures the proportion of variation in Y that is explained by <strong>The coefficient of multiple determination  </strong> A)measures the variation around the predicted regression equation. B)measures the proportion of variation in Y that is explained by   C)measures the proportion of variation in Y that is explained by   holding   constant. D)will have the same sign as
C)measures the proportion of variation in Y that is explained by <strong>The coefficient of multiple determination  </strong> A)measures the variation around the predicted regression equation. B)measures the proportion of variation in Y that is explained by   C)measures the proportion of variation in Y that is explained by   holding   constant. D)will have the same sign as   holding <strong>The coefficient of multiple determination  </strong> A)measures the variation around the predicted regression equation. B)measures the proportion of variation in Y that is explained by   C)measures the proportion of variation in Y that is explained by   holding   constant. D)will have the same sign as   constant.
D)will have the same sign as <strong>The coefficient of multiple determination  </strong> A)measures the variation around the predicted regression equation. B)measures the proportion of variation in Y that is explained by   C)measures the proportion of variation in Y that is explained by   holding   constant. D)will have the same sign as
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SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, the p-value for GDP is</strong> A)0.05 B)0.01 C)0.001 D)None of the above.
Referring to Scenario 14-3, the p-value for GDP is

A)0.05
B)0.01
C)0.001
D)None of the above.
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SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, when the economist used a simple linear regression model with consumption as the dependent variable and GDP as the independent variable, he obtained an   value of 0.971.What additional percentage of the total variation of consumption has been explained by including aggregate prices in the multiple regression?</strong> A)98.2 B)11.1 C)2.8 D)1.1
Referring to Scenario 14-3, when the economist used a simple linear regression model with consumption as the dependent variable and GDP as the independent variable, he obtained an <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, when the economist used a simple linear regression model with consumption as the dependent variable and GDP as the independent variable, he obtained an   value of 0.971.What additional percentage of the total variation of consumption has been explained by including aggregate prices in the multiple regression?</strong> A)98.2 B)11.1 C)2.8 D)1.1 value of 0.971.What additional percentage of the total variation of consumption has been explained by including aggregate prices in the multiple regression?

A)98.2
B)11.1
C)2.8
D)1.1
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SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, to test whether gross domestic product has a positive impact on consumption, the p-value is</strong> A)0.00005 B)0.0001 C)0.9999 D)0.99995
Referring to Scenario 14-3, to test whether gross domestic product has a positive impact on consumption, the p-value is

A)0.00005
B)0.0001
C)0.9999
D)0.99995
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, which of the following values for the level of significance is the smallest for which the regression model as a whole is significant?</strong> A)0.0005 B)0.001 C)0.01 D)0.05
Referring to Scenario 14-4, which of the following values for the level of significance is the smallest for which the regression model as a whole is significant?

A)0.0005
B)0.001
C)0.01
D)0.05
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, which of the following values for the level of significance is the smallest for which each explanatory variable is significant individually?</strong> A)0.001 B)0.010 C)0.025 D)0.050
Referring to Scenario 14-4, which of the following values for the level of significance is the smallest for which each explanatory variable is significant individually?

A)0.001
B)0.010
C)0.025
D)0.050
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, when the builder used a simple linear regression model with house size (House)as the dependent variable and family size (Size)as the independent variable, he obtained an r2 value of 1.25%.What additional percentage of the total variation in house size has been explained by including income in the multiple regression?</strong> A)15.00% B)70.64% C)71.50% D)73.62%
Referring to Scenario 14-4, when the builder used a simple linear regression model with house size (House)as the dependent variable and family size (Size)as the independent variable, he obtained an r2 value of 1.25%.What additional percentage of the total variation in house size has been explained by including income in the multiple regression?

A)15.00%
B)70.64%
C)71.50%
D)73.62%
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, what annual income (in thousands of dollars)would an individual with a family size of 4 need to attain a predicted 10,000 square foot home (House = 100)?
Referring to Scenario 14-4, what annual income (in thousands of dollars)would an individual with a family size of 4 need to attain a predicted 10,000 square foot home (House = 100)?
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SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, to test for the significance of the coefficient on gross domestic product, the   value is</strong> A)0.0001 B)0.8330 C)0.8837 D)0.9999
Referring to Scenario 14-3, to test for the significance of the coefficient on gross domestic product, the <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, to test for the significance of the coefficient on gross domestic product, the   value is</strong> A)0.0001 B)0.8330 C)0.8837 D)0.9999 value is

A)0.0001
B)0.8330
C)0.8837
D)0.9999
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, what fraction of the variability in house size is explained by income and size of family?</strong> A)17.56% B)70.69% C)71.89% D)84.79%
Referring to Scenario 14-4, what fraction of the variability in house size is explained by income and size of family?

A)17.56%
B)70.69%
C)71.89%
D)84.79%
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SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, one economy in the sample had an aggregate consumption level of $3 billion, a GDP of $3.5 billion, and an aggregate price level of 125.What is the residual for this data point?</strong> A)$2.52 billion B)$0.48 billion C)- $1.33 billion D)- $2.52 billion
Referring to Scenario 14-3, one economy in the sample had an aggregate consumption level of $3 billion, a GDP of $3.5 billion, and an aggregate price level of 125.What is the residual for this data point?

A)$2.52 billion
B)$0.48 billion
C)- $1.33 billion
D)- $2.52 billion
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, what annual income (in thousands of dollars)would an individual with a family size of 9 need to attain a predicted 5,000 square foot home (House = 50)?
Referring to Scenario 14-4, what annual income (in thousands of dollars)would an individual with a family size of 9 need to attain a predicted 5,000 square foot home (House = 50)?
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, which of the following values for the level of significance is the smallest for which at least one explanatory variable is significant individually?</strong> A)0.005 B)0.010 C)0.025 D)0.050
Referring to Scenario 14-4, which of the following values for the level of significance is the smallest for which at least one explanatory variable is significant individually?

A)0.005
B)0.010
C)0.025
D)0.050
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SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, to test for the significance of the coefficient on aggregate price index, the p-value is</strong> A)0.0001 B)0.8330 C)0.8837 D)0.9999
Referring to Scenario 14-3, to test for the significance of the coefficient on aggregate price index, the p-value is

A)0.0001
B)0.8330
C)0.8837
D)0.9999
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, which of the independent variables in the model are significant at the 5% level?</strong> A)Income only B)Size only C)Income and Size D)None
Referring to Scenario 14-4, which of the independent variables in the model are significant at the 5% level?

A)Income only
B)Size only
C)Income and Size
D)None
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SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, what is the estimated mean consumption level for an economy with GDP equal to $2 billion and an aggregate price index of 90?</strong> A)$1.39 billion B)$2.89 billion C)$4.75 billion D)$9.45 billion
Referring to Scenario 14-3, what is the estimated mean consumption level for an economy with GDP equal to $2 billion and an aggregate price index of 90?

A)$1.39 billion
B)$2.89 billion
C)$4.75 billion
D)$9.45 billion
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, one individual in the sample had an annual income of $100,000 and a family size of 10.This individual owned a home with an area of 7,000 square feet (House = 70.00).What is the residual (in hundreds of square feet)for this data point?
Referring to Scenario 14-4, one individual in the sample had an annual income of $100,000 and a family size of 10.This individual owned a home with an area of 7,000 square feet (House = 70.00).What is the residual (in hundreds of square feet)for this data point?
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, which of the following values for the level of significance is the smallest for which at most one explanatory variable is significant individually?</strong> A)0.001 B)0.010 C)0.025 D)0.050
Referring to Scenario 14-4, which of the following values for the level of significance is the smallest for which at most one explanatory variable is significant individually?

A)0.001
B)0.010
C)0.025
D)0.050
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SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, to test whether aggregate price index has a negative impact on consumption, the p-value is _______?</strong> A)0.0001 B)0.4165 C)0.8330 D)0.8837
Referring to Scenario 14-3, to test whether aggregate price index has a negative impact on consumption, the p-value is _______?

A)0.0001
B)0.4165
C)0.8330
D)0.8837
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SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, one economy in the sample had an aggregate consumption level of $4 billion, a GDP of $6 billion, and an aggregate price level of 200.What is the residual for this data point?</strong> A)$4.39 billion B)$0.39 billion C)- $0.39 billion D)- $1.33 billion
Referring to Scenario 14-3, one economy in the sample had an aggregate consumption level of $4 billion, a GDP of $6 billion, and an aggregate price level of 200.What is the residual for this data point?

A)$4.39 billion
B)$0.39 billion
C)- $0.39 billion
D)- $1.33 billion
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SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, to test for the significance of the coefficient on aggregate price, the value of the relevant t-statistic is</strong> A)2.365 B)0.143 C)- 0.219 D)- 1.960
Referring to Scenario 14-3, to test for the significance of the coefficient on aggregate price, the value of the relevant t-statistic is

A)2.365
B)0.143
C)- 0.219
D)- 1.960
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SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below. <strong>SCENARIO 14-3 An economist is interested to see how consumption for an economy (in $ billions)is influenced by gross domestic product ($ billions)and aggregate price (consumer price index).The Microsoft Excel output of this regression is partially reproduced below.   Referring to Scenario 14-3, to test whether aggregate price index has a positive impact on consumption, the p-value is</strong> A)0.0001 B)0.4165 C)0.5835 D)0.8330
Referring to Scenario 14-3, to test whether aggregate price index has a positive impact on consumption, the p-value is

A)0.0001
B)0.4165
C)0.5835
D)0.8330
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, what is the predicted house size (in hundreds of square feet)for an individual earning an annual income of $40,000 and having a family size of 4?
Referring to Scenario 14-4, what is the predicted house size (in hundreds of square feet)for an individual earning an annual income of $40,000 and having a family size of 4?
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SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, what fraction of the variability in sales is explained by spending on capital and wages?</strong> A)27.0% B)50.9% C)68.9% D)83.0%
Referring to Scenario 14-5, what fraction of the variability in sales is explained by spending on capital and wages?

A)27.0%
B)50.9%
C)68.9%
D)83.0%
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, at the 0.01 level of significance, what conclusion should the builder draw regarding the inclusion of Size in the regression model?</strong> A)Size is significant in explaining house size and should be included in the model because its p-value is less than 0.01. B)Size is significant in explaining house size and should be included in the model because its p-value is more than 0.01. C)Size is not significant in explaining house size and should not be included in the model because its p-value is less than 0.01. D)Size is not significant in explaining house size and should not be included in the model because its p-value is more than 0.01.
Referring to Scenario 14-4, at the 0.01 level of significance, what conclusion should the builder draw regarding the inclusion of Size in the regression model?

A)Size is significant in explaining house size and should be included in the model because its p-value is less than 0.01.
B)Size is significant in explaining house size and should be included in the model because its p-value is more than 0.01.
C)Size is not significant in explaining house size and should not be included in the model because its p-value is less than 0.01.
D)Size is not significant in explaining house size and should not be included in the model because its p-value is more than 0.01.
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the value of the partial F test statistic is ____ for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included
Referring to Scenario 14-4, the value of the partial F test statistic is ____ for H₀ : Variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the value of the partial F test statistic is ____ for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included does not significantly improve the model after variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the value of the partial F test statistic is ____ for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included has been included H₁ : Variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the value of the partial F test statistic is ____ for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included significantly improves the model after variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the value of the partial F test statistic is ____ for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included has been included
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the partial F test for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included has ____ and ____ degrees of freedom.
Referring to Scenario 14-4, the partial F test for H₀ : Variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the partial F test for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included has ____ and ____ degrees of freedom. does not significantly improve the model after variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the partial F test for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included has ____ and ____ degrees of freedom. has been included H₁ : Variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the partial F test for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included has ____ and ____ degrees of freedom. significantly improves the model after variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the partial F test for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included has ____ and ____ degrees of freedom. has been included has ____ and ____ degrees of freedom.
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the coefficient of partial determination   is ____.
Referring to Scenario 14-4, the coefficient of partial determination SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the coefficient of partial determination   is ____. is ____.
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, ____% of the variation in the house size can be explained by the variation in the family income while holding the family size constant.
Referring to Scenario 14-4, ____% of the variation in the house size can be explained by the variation in the family income while holding the family size constant.
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, suppose the builder wants to test whether the coefficient on Size is significantly different from 0.What is the value of the relevant t-statistic?</strong> A)- 0.7630 B)3.2708 C)10.8668 D)60.0864
Referring to Scenario 14-4, suppose the builder wants to test whether the coefficient on Size is significantly different from 0.What is the value of the relevant t-statistic?

A)- 0.7630
B)3.2708
C)10.8668
D)60.0864
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, one individual in the sample had an annual income of $40,000 and a family size of 1.This individual owned a home with an area of 1,000 square feet (House = 10.00).What is the residual (in hundreds of square feet)for this data point?
Referring to Scenario 14-4, one individual in the sample had an annual income of $40,000 and a family size of 1.This individual owned a home with an area of 1,000 square feet (House = 10.00).What is the residual (in hundreds of square feet)for this data point?
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, suppose the builder wants to test whether the coefficient on Income is significantly different from 0.What is the value of the relevant t-statistic?</strong> A)-0.7630 B)3.2708 C)10.8668 D)60.0864
Referring to Scenario 14-4, suppose the builder wants to test whether the coefficient on Income is significantly different from 0.What is the value of the relevant t-statistic?

A)-0.7630
B)3.2708
C)10.8668
D)60.0864
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the partial F test for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included has ____ and ____ degrees of freedom.
Referring to Scenario 14-4, the partial F test for H₀ : Variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the partial F test for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included has ____ and ____ degrees of freedom. does not significantly improve the model after variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the partial F test for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included has ____ and ____ degrees of freedom. has been included H₁ : Variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the partial F test for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included has ____ and ____ degrees of freedom. significantly improves the model after variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the partial F test for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included has ____ and ____ degrees of freedom. has been included has ____ and ____ degrees of freedom.
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the coefficient of partial determination   is ____.
Referring to Scenario 14-4, the coefficient of partial determination SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the coefficient of partial determination   is ____. is ____.
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the observed value of the F-statistic is missing from the printout.What are the degrees of freedom for this F-statistic?</strong> A)2 for the numerator, 47 for the denominator B)2 for the numerator, 49 for the denominator C)49 for the numerator, 47 for the denominator D)47 for the numerator, 49 for the denominator
Referring to Scenario 14-4, the observed value of the F-statistic is missing from the printout.What are the degrees of freedom for this F-statistic?

A)2 for the numerator, 47 for the denominator
B)2 for the numerator, 49 for the denominator
C)49 for the numerator, 47 for the denominator
D)47 for the numerator, 49 for the denominator
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SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, which of the independent variables in the model are significant at the 5% level?</strong> A)Capital, Wages B)Capital C)Wages D)None of the above
Referring to Scenario 14-5, which of the independent variables in the model are significant at the 5% level?

A)Capital, Wages
B)Capital
C)Wages
D)None of the above
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4 and allowing for a 1% probability of committing a type I error, what is the decision and conclusion for the test  </strong> A)Do not reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on house size. B)Do not reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on house size. C)Reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on house size. D)Reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on house size.
Referring to Scenario 14-4 and allowing for a 1% probability of committing a type I error, what is the decision and conclusion for the test <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4 and allowing for a 1% probability of committing a type I error, what is the decision and conclusion for the test  </strong> A)Do not reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on house size. B)Do not reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on house size. C)Reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on house size. D)Reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on house size.

A)Do not reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on house size.
B)Do not reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on house size.
C)Reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on house size.
D)Reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on house size.
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the value of the partial F test statistic is ____ for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included
Referring to Scenario 14-4, the value of the partial F test statistic is ____ for H₀ : Variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the value of the partial F test statistic is ____ for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included does not significantly improve the model after variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the value of the partial F test statistic is ____ for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included has been included H₁ : Variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the value of the partial F test statistic is ____ for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included significantly improves the model after variable SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, the value of the partial F test statistic is ____ for H₀ : Variable   does not significantly improve the model after variable   has been included H₁ : Variable   significantly improves the model after variable   has been included has been included
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, what is the value of the calculated F test statistic that is missing from the output for testing whether the whole regression model is significant?
Referring to Scenario 14-4, what is the value of the calculated F test statistic that is missing from the output for testing whether the whole regression model is significant?
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, at the 0.01 level of significance, what conclusion should the builder reach regarding the inclusion of Income in the regression model?</strong> A)Income is significant in explaining house size and should be included in the model because its p-value is less than 0.01. B)Income is significant in explaining house size and should be included in the model because its p-value is more than 0.01. C)Income is not significant in explaining house size and should not be included in the model because its p-value is less than 0.01. D)Income is not significant in explaining house size and should not be included in the model because its p-value is more than 0.01.
Referring to Scenario 14-4, at the 0.01 level of significance, what conclusion should the builder reach regarding the inclusion of Income in the regression model?

A)Income is significant in explaining house size and should be included in the model because its p-value is less than 0.01.
B)Income is significant in explaining house size and should be included in the model because its p-value is more than 0.01.
C)Income is not significant in explaining house size and should not be included in the model because its p-value is less than 0.01.
D)Income is not significant in explaining house size and should not be included in the model because its p-value is more than 0.01.
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, ____% of the variation in the house size can be explained by the variation in the family size while holding the family income constant.
Referring to Scenario 14-4, ____% of the variation in the house size can be explained by the variation in the family size while holding the family income constant.
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, what are the regression degrees of freedom that are missing from the output?</strong> A)2 B)47 C)49 D)50
Referring to Scenario 14-4, what are the regression degrees of freedom that are missing from the output?

A)2
B)47
C)49
D)50
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SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below: <strong>SCENARIO 14-4 A real estate builder wishes to determine how house size (House)is influenced by family income (Income)and family size (Size).House size is measured in hundreds of square feet and income is measured in thousands of dollars.The builder randomly selected 50 families and ran the multiple regression.Partial Microsoft Excel output is provided below:   Referring to Scenario 14-4, what are the residual degrees of freedom that are missing from the output?</strong> A)2 B)47 C)49 D)50
Referring to Scenario 14-4, what are the residual degrees of freedom that are missing from the output?

A)2
B)47
C)49
D)50
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SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, at the 0.01 level of significance, what conclusion should the microeconomist reach regarding the inclusion of Capital in the regression model?</strong> A)Capital is significant in explaining corporate sales and should be included in the model because its p-value is less than 0.01. B)Capital is significant in explaining corporate sales and should be included in the model because its p-value is more than 0.01. C)Capital is not significant in explaining corporate sales and should not be included in the model because its p-value is less than 0.01. D)Capital is not significant in explaining corporate sales and should not be included in the model because its p-value is more than 0.01.
Referring to Scenario 14-5, at the 0.01 level of significance, what conclusion should the microeconomist reach regarding the inclusion of Capital in the regression model?

A)Capital is significant in explaining corporate sales and should be included in the model because its p-value is less than 0.01.
B)Capital is significant in explaining corporate sales and should be included in the model because its p-value is more than 0.01.
C)Capital is not significant in explaining corporate sales and should not be included in the model because its p-value is less than 0.01.
D)Capital is not significant in explaining corporate sales and should not be included in the model because its p-value is more than 0.01.
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SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, what is the p-value for Capital?</strong> A)0.01 B)0.025 C)0.05 D)None of the above
Referring to Scenario 14-5, what is the p-value for Capital?

A)0.01
B)0.025
C)0.05
D)None of the above
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SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, what is the 95% confidence interval for the expected change in heating costs as a result of a 1 degree Fahrenheit change in the daily minimum outside temperature?</strong> A)[256.7522, 639.8328] B)[204.7854, 497.1733] C)[-5.3721, -0.1520] D)[-37.1736, 5.2919] and the amount of insulation in inches <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, what is the 95% confidence interval for the expected change in heating costs as a result of a 1 degree Fahrenheit change in the daily minimum outside temperature?</strong> A)[256.7522, 639.8328] B)[204.7854, 497.1733] C)[-5.3721, -0.1520] D)[-37.1736, 5.2919] Given below is EXCEL output of the regression model. <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, what is the 95% confidence interval for the expected change in heating costs as a result of a 1 degree Fahrenheit change in the daily minimum outside temperature?</strong> A)[256.7522, 639.8328] B)[204.7854, 497.1733] C)[-5.3721, -0.1520] D)[-37.1736, 5.2919] <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, what is the 95% confidence interval for the expected change in heating costs as a result of a 1 degree Fahrenheit change in the daily minimum outside temperature?</strong> A)[256.7522, 639.8328] B)[204.7854, 497.1733] C)[-5.3721, -0.1520] D)[-37.1736, 5.2919]
Referring to Scenario 14-6, what is the 95% confidence interval for the expected change in heating costs as a result of a 1 degree Fahrenheit change in the daily minimum outside temperature?

A)[256.7522, 639.8328]
B)[204.7854, 497.1733]
C)[-5.3721, -0.1520]
D)[-37.1736, 5.2919]
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SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6 and allowing for a 1% probability of committing a type I error, what is the decision and conclusion for the test   At least one  </strong> A)Do not reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs. B)Do not reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs. C)Reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs. D)Reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs. and the amount of insulation in inches <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6 and allowing for a 1% probability of committing a type I error, what is the decision and conclusion for the test   At least one  </strong> A)Do not reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs. B)Do not reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs. C)Reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs. D)Reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs. Given below is EXCEL output of the regression model. <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6 and allowing for a 1% probability of committing a type I error, what is the decision and conclusion for the test   At least one  </strong> A)Do not reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs. B)Do not reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs. C)Reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs. D)Reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs. <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6 and allowing for a 1% probability of committing a type I error, what is the decision and conclusion for the test   At least one  </strong> A)Do not reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs. B)Do not reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs. C)Reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs. D)Reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs.
Referring to Scenario 14-6 and allowing for a 1% probability of committing a type I error, what is the decision and conclusion for the test <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6 and allowing for a 1% probability of committing a type I error, what is the decision and conclusion for the test   At least one  </strong> A)Do not reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs. B)Do not reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs. C)Reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs. D)Reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs. At least one <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6 and allowing for a 1% probability of committing a type I error, what is the decision and conclusion for the test   At least one  </strong> A)Do not reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs. B)Do not reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs. C)Reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs. D)Reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs.

A)Do not reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs.
B)Do not reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs.
C)Reject H₀ and conclude that the 2 independent variables taken as a group have significant linear effects on heating costs.
D)Reject H₀ and conclude that the 2 independent variables taken as a group do not have significant linear effects on heating costs.
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SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, when the microeconomist used a simple linear regression model with sales as the dependent variable and wages as the independent variable, she obtained an   value of 0.601.What additional percentage of the total variation of sales has been explained by including capital spending in the multiple regression?</strong> A)60.1% B)31.1% C)22.9% D)8.8%
Referring to Scenario 14-5, when the microeconomist used a simple linear regression model with sales as the dependent variable and wages as the independent variable, she obtained an <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, when the microeconomist used a simple linear regression model with sales as the dependent variable and wages as the independent variable, she obtained an   value of 0.601.What additional percentage of the total variation of sales has been explained by including capital spending in the multiple regression?</strong> A)60.1% B)31.1% C)22.9% D)8.8% value of 0.601.What additional percentage of the total variation of sales has been explained by including capital spending in the multiple regression?

A)60.1%
B)31.1%
C)22.9%
D)8.8%
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SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, one company in the sample had sales of $21.439 billion (Sales = 21,439).This company spent $300 million on capital and $700 million on wages. What is the residual (in millions of dollars)for this data point?</strong> A)790.69 B)648.31 C)-648.31 D)-790.69
Referring to Scenario 14-5, one company in the sample had sales of $21.439 billion (Sales = 21,439).This company spent $300 million on capital and $700 million on wages. What is the residual (in millions of dollars)for this data point?

A)790.69
B)648.31
C)-648.31
D)-790.69
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SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, what is the p-value for testing whether Capital has a negative influence on corporate sales?</strong> A)0.05 B)0.2743 C)0.5485 D)0.7258
Referring to Scenario 14-5, what is the p-value for testing whether Capital has a negative influence on corporate sales?

A)0.05
B)0.2743
C)0.5485
D)0.7258
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SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, suppose the microeconomist wants to test whether the coefficient on Capital is significantly different from 0.What is the value of the relevant t-statistic?</strong> A)0.609 B)2.617 C)4.804 D)25.432
Referring to Scenario 14-5, suppose the microeconomist wants to test whether the coefficient on Capital is significantly different from 0.What is the value of the relevant t-statistic?

A)0.609
B)2.617
C)4.804
D)25.432
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SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, what is your decision and conclusion for the test   level of significance?</strong> A)Do not reject H₀ and conclude that the amount of insulation has a linear effect on heating costs. B)Reject H₀ and conclude that the amount of insulation does not have a linear effect on heating costs. C)Reject H₀ and conclude that the amount of insulation has a linear effect on heating costs. D)Do not reject H₀ and conclude that the amount of insulation does not have a linear effect on heating costs. and the amount of insulation in inches <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, what is your decision and conclusion for the test   level of significance?</strong> A)Do not reject H₀ and conclude that the amount of insulation has a linear effect on heating costs. B)Reject H₀ and conclude that the amount of insulation does not have a linear effect on heating costs. C)Reject H₀ and conclude that the amount of insulation has a linear effect on heating costs. D)Do not reject H₀ and conclude that the amount of insulation does not have a linear effect on heating costs. Given below is EXCEL output of the regression model. <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, what is your decision and conclusion for the test   level of significance?</strong> A)Do not reject H₀ and conclude that the amount of insulation has a linear effect on heating costs. B)Reject H₀ and conclude that the amount of insulation does not have a linear effect on heating costs. C)Reject H₀ and conclude that the amount of insulation has a linear effect on heating costs. D)Do not reject H₀ and conclude that the amount of insulation does not have a linear effect on heating costs. <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, what is your decision and conclusion for the test   level of significance?</strong> A)Do not reject H₀ and conclude that the amount of insulation has a linear effect on heating costs. B)Reject H₀ and conclude that the amount of insulation does not have a linear effect on heating costs. C)Reject H₀ and conclude that the amount of insulation has a linear effect on heating costs. D)Do not reject H₀ and conclude that the amount of insulation does not have a linear effect on heating costs.
Referring to Scenario 14-6, what is your decision and conclusion for the test <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, what is your decision and conclusion for the test   level of significance?</strong> A)Do not reject H₀ and conclude that the amount of insulation has a linear effect on heating costs. B)Reject H₀ and conclude that the amount of insulation does not have a linear effect on heating costs. C)Reject H₀ and conclude that the amount of insulation has a linear effect on heating costs. D)Do not reject H₀ and conclude that the amount of insulation does not have a linear effect on heating costs. level of significance?

A)Do not reject H₀ and conclude that the amount of insulation has a linear effect on heating costs.
B)Reject H₀ and conclude that the amount of insulation does not have a linear effect on heating costs.
C)Reject H₀ and conclude that the amount of insulation has a linear effect on heating costs.
D)Do not reject H₀ and conclude that the amount of insulation does not have a linear effect on heating costs.
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SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, what are the predicted sales (in millions of dollars)for a company spending $500 million on capital and $200 million on wages?</strong> A)15,800.00 B)16,520.07 C)17,277.49 D)20,455.98
Referring to Scenario 14-5, what are the predicted sales (in millions of dollars)for a company spending $500 million on capital and $200 million on wages?

A)15,800.00
B)16,520.07
C)17,277.49
D)20,455.98
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SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, one company in the sample had sales of $20 billion (Sales = 20,000).This company spent $300 million on capital and $700 million on wages. What is the residual (in millions of dollars)for this data point?</strong> A)874.55 B)622.87 C)-790.69 D)-983.56
Referring to Scenario 14-5, one company in the sample had sales of $20 billion (Sales = 20,000).This company spent $300 million on capital and $700 million on wages. What is the residual (in millions of dollars)for this data point?

A)874.55
B)622.87
C)-790.69
D)-983.56
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SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, what is the p-value for testing whether Wages have a positive impact on corporate sales?</strong> A)0.01 B)0.05 C)0.0001 D)0.00005
Referring to Scenario 14-5, what is the p-value for testing whether Wages have a positive impact on corporate sales?

A)0.01
B)0.05
C)0.0001
D)0.00005
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SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, what are the predicted sales (in millions of dollars)for a company spending $100 million on capital and $100 million on wages?</strong> A)15,800.00 B)16,520.07 C)17,277.49 D)20,455.98
Referring to Scenario 14-5, what are the predicted sales (in millions of dollars)for a company spending $100 million on capital and $100 million on wages?

A)15,800.00
B)16,520.07
C)17,277.49
D)20,455.98
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SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, which of the following values for   is the smallest for which the regression model as a whole is significant?</strong> A)0.00005 B)0.001 C)0.01 D)0.05
Referring to Scenario 14-5, which of the following values for <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, which of the following values for   is the smallest for which the regression model as a whole is significant?</strong> A)0.00005 B)0.001 C)0.01 D)0.05 is the smallest for which the regression model as a whole is significant?

A)0.00005
B)0.001
C)0.01
D)0.05
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SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, the observed value of the F-statistic is given on the printout as 25.432.What are the degrees of freedom for this F-statistic?</strong> A)25 for the numerator, 2 for the denominator B)2 for the numerator, 23 for the denominator C)23 for the numerator, 25 for the denominator D)2 for the numerator, 25 for the denominator
Referring to Scenario 14-5, the observed value of the F-statistic is given on the printout as 25.432.What are the degrees of freedom for this F-statistic?

A)25 for the numerator, 2 for the denominator
B)2 for the numerator, 23 for the denominator
C)23 for the numerator, 25 for the denominator
D)2 for the numerator, 25 for the denominator
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SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, what can we say about the regression model?</strong> A)The model explains 17.12% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 27.78% of the sample variability of heating Costs. B)The model explains 19.28% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 27.78% of the sample variability of heating Costs. C)The model explains 27.78% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 19.28% of the sample variability of heating Costs. D)The model explains 19.28% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 17.12% of the sample variability of heating Costs. and the amount of insulation in inches <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, what can we say about the regression model?</strong> A)The model explains 17.12% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 27.78% of the sample variability of heating Costs. B)The model explains 19.28% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 27.78% of the sample variability of heating Costs. C)The model explains 27.78% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 19.28% of the sample variability of heating Costs. D)The model explains 19.28% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 17.12% of the sample variability of heating Costs. Given below is EXCEL output of the regression model. <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, what can we say about the regression model?</strong> A)The model explains 17.12% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 27.78% of the sample variability of heating Costs. B)The model explains 19.28% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 27.78% of the sample variability of heating Costs. C)The model explains 27.78% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 19.28% of the sample variability of heating Costs. D)The model explains 19.28% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 17.12% of the sample variability of heating Costs. <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, what can we say about the regression model?</strong> A)The model explains 17.12% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 27.78% of the sample variability of heating Costs. B)The model explains 19.28% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 27.78% of the sample variability of heating Costs. C)The model explains 27.78% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 19.28% of the sample variability of heating Costs. D)The model explains 19.28% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 17.12% of the sample variability of heating Costs.
Referring to Scenario 14-6, what can we say about the regression model?

A)The model explains 17.12% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 27.78% of the sample variability of heating
Costs.
B)The model explains 19.28% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 27.78% of the sample variability of heating
Costs.
C)The model explains 27.78% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 19.28% of the sample variability of heating
Costs.
D)The model explains 19.28% of the variability of heating costs; after correcting for the degrees of freedom, the model explains 17.12% of the sample variability of heating
Costs.
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SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, what is the p-value for Wages?</strong> A)0.01 B)0.05 C)0.0001 D)None of the above
Referring to Scenario 14-5, what is the p-value for Wages?

A)0.01
B)0.05
C)0.0001
D)None of the above
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SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, the estimated value of the regression parameter   in means that</strong> A)holding the effect of the amount of insulation constant, an estimated expected $1 increase in heating costs is associated with a decrease in the daily minimum outside Temperature by 2.76 degrees. B)holding the effect of the amount of insulation constant, a 1 degree increase in the daily minimum outside temperature results in a decrease in heating costs by $2.76. C)holding the effect of the amount of insulation constant, a 1 degree increase in the daily minimum outside temperature results in an estimated decrease in mean heating Costs by $2.76. D)holding the effect of the amount of insulation constant, a 1% increase in the daily minimum outside temperature results in an estimated decrease in mean heating costs By 2.76%. and the amount of insulation in inches <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, the estimated value of the regression parameter   in means that</strong> A)holding the effect of the amount of insulation constant, an estimated expected $1 increase in heating costs is associated with a decrease in the daily minimum outside Temperature by 2.76 degrees. B)holding the effect of the amount of insulation constant, a 1 degree increase in the daily minimum outside temperature results in a decrease in heating costs by $2.76. C)holding the effect of the amount of insulation constant, a 1 degree increase in the daily minimum outside temperature results in an estimated decrease in mean heating Costs by $2.76. D)holding the effect of the amount of insulation constant, a 1% increase in the daily minimum outside temperature results in an estimated decrease in mean heating costs By 2.76%. Given below is EXCEL output of the regression model. <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, the estimated value of the regression parameter   in means that</strong> A)holding the effect of the amount of insulation constant, an estimated expected $1 increase in heating costs is associated with a decrease in the daily minimum outside Temperature by 2.76 degrees. B)holding the effect of the amount of insulation constant, a 1 degree increase in the daily minimum outside temperature results in a decrease in heating costs by $2.76. C)holding the effect of the amount of insulation constant, a 1 degree increase in the daily minimum outside temperature results in an estimated decrease in mean heating Costs by $2.76. D)holding the effect of the amount of insulation constant, a 1% increase in the daily minimum outside temperature results in an estimated decrease in mean heating costs By 2.76%. <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, the estimated value of the regression parameter   in means that</strong> A)holding the effect of the amount of insulation constant, an estimated expected $1 increase in heating costs is associated with a decrease in the daily minimum outside Temperature by 2.76 degrees. B)holding the effect of the amount of insulation constant, a 1 degree increase in the daily minimum outside temperature results in a decrease in heating costs by $2.76. C)holding the effect of the amount of insulation constant, a 1 degree increase in the daily minimum outside temperature results in an estimated decrease in mean heating Costs by $2.76. D)holding the effect of the amount of insulation constant, a 1% increase in the daily minimum outside temperature results in an estimated decrease in mean heating costs By 2.76%.
Referring to Scenario 14-6, the estimated value of the regression parameter <strong>SCENARIO 14-6 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y).To provide its customers with information on that matter, a large real estate firm used the following 2 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit   and the amount of insulation in inches   Given below is EXCEL output of the regression model.     Referring to Scenario 14-6, the estimated value of the regression parameter   in means that</strong> A)holding the effect of the amount of insulation constant, an estimated expected $1 increase in heating costs is associated with a decrease in the daily minimum outside Temperature by 2.76 degrees. B)holding the effect of the amount of insulation constant, a 1 degree increase in the daily minimum outside temperature results in a decrease in heating costs by $2.76. C)holding the effect of the amount of insulation constant, a 1 degree increase in the daily minimum outside temperature results in an estimated decrease in mean heating Costs by $2.76. D)holding the effect of the amount of insulation constant, a 1% increase in the daily minimum outside temperature results in an estimated decrease in mean heating costs By 2.76%. in means that

A)holding the effect of the amount of insulation constant, an estimated expected $1 increase in heating costs is associated with a decrease in the daily minimum outside
Temperature by 2.76 degrees.
B)holding the effect of the amount of insulation constant, a 1 degree increase in the daily minimum outside temperature results in a decrease in heating costs by $2.76.
C)holding the effect of the amount of insulation constant, a 1 degree increase in the daily minimum outside temperature results in an estimated decrease in mean heating
Costs by $2.76.
D)holding the effect of the amount of insulation constant, a 1% increase in the daily minimum outside temperature results in an estimated decrease in mean heating costs
By 2.76%.
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SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, what is the p-value for testing whether Capital has a positive influence on corporate sales?</strong> A)0.025 B)0.05 C)0.2743 D)0.5485
Referring to Scenario 14-5, what is the p-value for testing whether Capital has a positive influence on corporate sales?

A)0.025
B)0.05
C)0.2743
D)0.5485
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SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression. <strong>SCENARIO 14-5 A microeconomist wants to determine how corporate sales are influenced by capital and wage spending by companies.She proceeds to randomly select 26 large corporations and record information in millions of dollars.The Microsoft Excel output below shows results of this multiple regression.   Referring to Scenario 14-5, what is the p-value for testing whether Wages have a negative impact on corporate sales?</strong> A)0.05 B)0.0001 C)0.00005 D)0.99995
Referring to Scenario 14-5, what is the p-value for testing whether Wages have a negative impact on corporate sales?

A)0.05
B)0.0001
C)0.00005
D)0.99995
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افتح القفل للوصول البطاقات البالغ عددها 336 في هذه المجموعة.