Deck 4: Regression Models

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سؤال
The null hypothesis in the F-test is that there is a linear relationship between the X and Y variables.
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سؤال
There is no relationship between variables unless the data points lie in a straight line.
سؤال
In regression, a dependent variable is sometimes called a predictor variable.
سؤال
The coefficient of determination gives the proportion of the variability in the dependent variable that is explained by the regression equation.
سؤال
The correlation coefficient has values between −1 and +1.
سؤال
Both the p-value for the F-test and r2 can be interpreted the same with multiple regression models as they are with simple linear models.
سؤال
Errors are also called residuals.
سؤال
The regression line minimizes the sum of the squared errors.
سؤال
When the significance level is small enough in the F-test, we can reject the null hypothesis that there is no linear relationship.
سؤال
An F-test is used to determine if there is a relationship between the dependent and independent variables.
سؤال
The coefficients of each independent variable in a multiple regression model represent slopes.
سؤال
If the significance level for the F-test is high enough, there is a relationship between the dependent and independent variables.
سؤال
For statistical tests of significance about the coefficients, the null hypothesis is that the slope is 1.
سؤال
The multiple regression model includes several dependent variables.
سؤال
Estimates of the slope, intercept, and error of a regression model are found from sample data.
سؤال
Summing the error values in a regression model is misleading because negative errors cancel out positive errors.
سؤال
In regression, there is random error that can be predicted.
سؤال
In regression, an independent variable is sometimes called a response variable.
سؤال
Often, a plot of the residuals will highlight any glaring violations of the assumptions.
سؤال
If the assumptions of regression have been met, errors plotted against the independent variable will typically show patterns.
سؤال
A dummy variable can be assigned up to three values.
سؤال
If multicollinearity exists, then individual interpretation of the variables is questionable, but the overall model is still good for prediction purposes.
سؤال
The random error in a regression equation

A)is the predicted error.
B)includes both positive and negative terms.
C)will sum to a large positive number.
D)is used to estimate the accuracy of the slope.
سؤال
A high correlation always implies that one variable is causing a change in the other variable.
سؤال
Multicollinearity exists when a variable is correlated to other variables.
سؤال
The adjusted r2 will always increase as additional variables are added to the model.
سؤال
The best model is a statistically significant model with a high r-square and few variables.
سؤال
Which of these statements is best?

A)In regression, an independent variable is sometimes called a response variable.
B)A scatter diagram is a graphical depiction of the relationship between the dependent and independent variables.
C)In a scatter diagram, the dependent variable is typically plotted on the horizontal axis.
D)There is no relationship between variables unless the data points lie in a straight line.
سؤال
Dummy variables for regression analysis can take on a value of either -1 or +1.
سؤال
Which of the following statements is true regarding a scatter diagram?

A)It provides very little information about the relationship between the regression variables.
B)It is a plot of the independent and dependent variables.
C)It is a line chart of the independent and dependent variables.
D)It has a value between -1 and +1.
سؤال
The value of r2 can never decrease when more variables are added to the model.
سؤال
The multiple regression model includes several intercept terms.
سؤال
In regression, a binary variable is also called an indicator variable.
سؤال
Transformations may be used when nonlinear relationships exist between variables.
سؤال
The number of dummy variables must equal 1 less than the number of categories of the qualitative variable.
سؤال
A variable should be added to the model regardless of the impact (increase or decrease)on the adjusted r2 value.
سؤال
Another name for a dummy variable is a binary variable.
سؤال
Which of these statements is best?

A)In regression, an independent variable is sometimes called a response variable.
B)One purpose of regression is to understand the relationship between variables.
C)One purpose of regression is to predict the value of one variable based on the standard deviation of that variable.
D)The variable to be predicted is the independent variable.
سؤال
A reference to the criterion used to select the regression line, to minimize the squared distances between the estimated straight line and the observed values is called

A)Mean square error.
B)Sum of Squares.
C)Maximum likelihood.
D)Least Squares.
سؤال
The multiple regression model includes multiple slope coefficients.
سؤال
Which of the following conditions can be detected from residual analysis?

A)nonconstant variance
B)multicollinearity
C)dummy variables
D)presence of transformations
سؤال
The problem of nonconstant error variance is detected in residual analysis by which of the following?

A)a cone pattern
B)an arched pattern
C)a random pattern
D)an increasing pattern
سؤال
Which of these statements is best?

A)The regression model assumes the error terms are dependent.
B)The regression model assumes the errors are normally distributed.
C)The errors in a regression model are assumed to have an increasing mean.
D)The errors in a regression model are assumed to have zero variance.
سؤال
The correlation coefficient resulting from a particular regression analysis was 0.25.What was the coefficient of determination?

A)0.5
B)-0.5
C)0.0625
D)There is insufficient information to answer the question.
سؤال
The coefficient of determination resulting from a particular regression analysis was 0.85.What was the slope of the regression line?

A)0.85
B)-0.85
C)0.922
D)There is insufficient information to answer the question.
سؤال
Which of the following statements is true about r2?

A)It is also called the coefficient of correlation.
B)It is also called the coefficient of determination.
C)It represents the percent of variation in X that is explained by Y.
D)It represents the percent of variation in the error that is explained by Y.
سؤال
Which of the following is not an assumption of the regression model?

A)The errors are independent.
B)The errors should have a standard deviation equal to one.
C)The errors are normally distributed.
D)The errors have constant variance.
سؤال
Which of these statements is best?

A)The SST measures the total variability in the dependent variable about the regression line.
B)The SSE measures the total variability in the independent variable about the regression line.
C)The SSR indicates how much of the total variability in the dependent variable is explained by the regression model.
D)The coefficient of determination takes on values between -1 and + 1.
سؤال
A dummy variable is also called a(n)

A)indicator variable.
B)dependent variable.
C)continuous variable.
D)response variable.
سؤال
In a good regression model the residual plot shows

A)a cone pattern.
B)an arched pattern.
C)a random pattern.
D)an increasing pattern.
سؤال
The diagram below illustrates data with a <strong>The diagram below illustrates data with a  </strong> A)negative correlation coefficient. B)zero correlation coefficient. C)positive correlation coefficient. D)correlation coefficient equal to +1. <div style=padding-top: 35px>

A)negative correlation coefficient.
B)zero correlation coefficient.
C)positive correlation coefficient.
D)correlation coefficient equal to +1.
سؤال
The coefficient of determination resulting from a particular regression analysis was 0.85.What was the correlation coefficient, assuming a positive linear relationship?

A)0.5
B)-0.5
C)0.922
D)There is insufficient information to answer the question.
سؤال
The sum of squared error (SSE)is

A)a measure of the total variation in Y about the mean.
B)a measure of the total variation in X about the mean.
C)a measure in the variation of Y about the regression line.
D)a measure in the variation of X about the regression line.
سؤال
Which of the following is an assumption of the regression model?

A)The errors are independent.
B)The errors are not normally distributed.
C)The errors have a standard deviation of zero.
D)The errors have an irregular variance.
سؤال
Which of the following statements is not true about regression models?

A)Estimates of the slope are found from sample data.
B)The regression line minimizes the sum of the squared errors.
C)The error is found by subtracting the actual data value from the predicted data value.
D)The dependent variable is the explanatory variable.
سؤال
Which of the following equalities is correct?

A)SST = SSR + SSE
B)SSR = SST + SSE
C)SSE = SSR + SST
D)SST = SSC + SSR
سؤال
Which of these statements is best?

A)If the assumptions of regression have been met, errors plotted against the independent variable will typically show patterns.
B)The error standard deviation is estimated by MSE.
C)Often, a plot of the residuals will highlight any glaring violations of the assumptions.
D)The standard error of the estimate is also called the variance of the regression.
سؤال
Which of these statements is best?

A)In any regression model, there is an implicit assumption that there is no preexisting relationship between the variables.
B)In regression, there is random error that can be predicted.
C)Estimates of the slope, intercept, and error of a regression model are found from sample data.
D)Error is the difference in the actual value and the predicted value.
سؤال
If computing a causal linear regression model of Y = a + bX and the resultant r2 is very near zero, then one would be able to conclude that

A)Y = a + bX is a good forecasting method.
B)Y = a + bX is not a good forecasting method.
C)a multiple linear regression model is a good forecasting method for the data.
D)a multiple linear regression model is not a good forecasting method for the data.
سؤال
The problem of a nonlinear relationship is detected in residual analysis by which of the following?

A)a cone pattern
B)an arched pattern
C)a random pattern
D)an increasing pattern
سؤال
When an independent variable is correlated with one other independent variable, the variables are said to be

A)collinear.
B)pairwise.
C)independent.
D)mutually exclusive.
سؤال
Which of the following is true regarding a regression model with multicollinearity, a high r2 value, and a low F-test significance level?

A)The model is not a good prediction model.
B)The high value of r2 is due to the multicollinearity.
C)The interpretation of the coefficients is valuable.
D)The significance level tests for the coefficients are not valid.
سؤال
If a qualitative variable has three categories, how many dummy variables are needed?

A)0
B)1
C)2
D)3
E)4
سؤال
A prediction equation for starting salaries (in $1,000s)and SAT scores was performed using simple linear regression.In the regression printout shown below, what can be said about the level of significance for the overall model? SUMMARY OUTPUT
<strong>A prediction equation for starting salaries (in $1,000s)and SAT scores was performed using simple linear regression.In the regression printout shown below, what can be said about the level of significance for the overall model? SUMMARY OUTPUT   ANOVA    </strong> A)SAT is not a good predictor for starting salary. B)The significance level for the intercept indicates the model is not valid. C)The significance level for SAT indicates the slope is equal to zero. D)The significance level for SAT indicates the slope is not equal to zero. <div style=padding-top: 35px> ANOVA
<strong>A prediction equation for starting salaries (in $1,000s)and SAT scores was performed using simple linear regression.In the regression printout shown below, what can be said about the level of significance for the overall model? SUMMARY OUTPUT   ANOVA    </strong> A)SAT is not a good predictor for starting salary. B)The significance level for the intercept indicates the model is not valid. C)The significance level for SAT indicates the slope is equal to zero. D)The significance level for SAT indicates the slope is not equal to zero. <div style=padding-top: 35px> <strong>A prediction equation for starting salaries (in $1,000s)and SAT scores was performed using simple linear regression.In the regression printout shown below, what can be said about the level of significance for the overall model? SUMMARY OUTPUT   ANOVA    </strong> A)SAT is not a good predictor for starting salary. B)The significance level for the intercept indicates the model is not valid. C)The significance level for SAT indicates the slope is equal to zero. D)The significance level for SAT indicates the slope is not equal to zero. <div style=padding-top: 35px>

A)SAT is not a good predictor for starting salary.
B)The significance level for the intercept indicates the model is not valid.
C)The significance level for SAT indicates the slope is equal to zero.
D)The significance level for SAT indicates the slope is not equal to zero.
سؤال
The sum of the squares total (SST)

A)measures the total variability in Y about the mean.
B)measures the total variability in X about the mean.
C)measures the variability in Y about the regression line.
D)measures the variability in X about the regression line.
سؤال
A healthcare executive is using regression to predict total revenues.She is deciding whether or not to include both patient length of stay and insurance type in her model.Her first regression model only included patient length of stay.The resulting r2 was .83, with an adjusted r2 of .82 and her level of significance was .003.In the second model, she included both patient length of stay and insurance type.The r2 was .84 and the adjusted r2 was .80 for the second model and the level of significance did not change.Which of the following statements is true?

A)The second model is a better model.
B)The first model is a better model.
C)The r2 increased when additional variables were added because these variables significantly contribute to the prediction of total revenues.
D)The adjusted r2 always increases when additional variables are added to the model.
سؤال
Suppose that you believe that a cubic relationship exists between the independent variable (of time)and the dependent variable Y.Which of the following would represent a valid linear regression model?

A)Y = b0 + b1 X, where X = time3
B)Y = b0 + b1 X3, where X = time
C)Y = b0 + 3b1 X, where X = time3
D)Y = b0 + 3b1 X, where X = time
سؤال
Which of these statements about multiple regression is best?

A)The p-value for the F-test may not be interpreted the same with multiple regression models as they are with simple linear models.
B)The multiple regression model includes several dependent variables.
C)The multiple regression model includes several intercept terms.
D)The multiple regression model includes multiple slope coefficients.
سؤال
In the model Y = β0 + β1X1 + β2X2 + ε, which of these parameters represents an independent variable?

A)the X1
B)the Y
C)the β1
D)the ε
سؤال
An automated process to systematically add or delete independent variables from a regression model is called

A)nonlinear regression.
B)linear regression.
C)residual analysis.
D)stepwise regression.
سؤال
Which of the following statements provides the best guidance for model building?

A)If the value of r2 increases as more variables are added to the model, the variables should remain in the model, regardless of the magnitude of increase.
B)If the value of the adjusted r2 increases as more variables are added to the model, the variables should remain in the model.
C)If the value of r2 increases as more variables are added to the model, the variables should not remain in the model, regardless of the magnitude of the increase.
D)If the value of the adjusted r2 increases as more variables are added to the model, the variables should not remain in the model.
سؤال
The condition of an independent variable being correlated to one or more other independent variables is referred to as

A)multicollinearity.
B)statistical significance.
C)linearity.
D)nonlinearity.
سؤال
The mean square error (MSE)is

A)denoted by s.
B)denoted by k.
C)the SSE divided by the number of observations.
D)the SSE divided by the degrees of freedom.
سؤال
Which of the following statements is false concerning the hypothesis testing procedure for a regression model?

A)The F-test statistic is used.
B)The null hypothesis is that the true slope coefficient is equal to zero.
C)The null hypothesis is rejected if the adjusted r2 is above the critical value.
D)An α level must be selected.
سؤال
A prediction equation for sales and payroll was performed using simple linear regression.In the regression printout shown below, which of the following statements is not true? SUMMARY OUTPUT
<strong>A prediction equation for sales and payroll was performed using simple linear regression.In the regression printout shown below, which of the following statements is not true? SUMMARY OUTPUT   ANOVA    </strong> A)Payroll is a good predictor of Sales based on α = 0.05. B)There is evidence of a positive linear relationship between Sales and Payroll based on α = 0.05. C)Payroll is not a good predictor of Sales based on α = 0.01. D)The coefficient of determination is equal to 0.833333. <div style=padding-top: 35px> ANOVA
<strong>A prediction equation for sales and payroll was performed using simple linear regression.In the regression printout shown below, which of the following statements is not true? SUMMARY OUTPUT   ANOVA    </strong> A)Payroll is a good predictor of Sales based on α = 0.05. B)There is evidence of a positive linear relationship between Sales and Payroll based on α = 0.05. C)Payroll is not a good predictor of Sales based on α = 0.01. D)The coefficient of determination is equal to 0.833333. <div style=padding-top: 35px> <strong>A prediction equation for sales and payroll was performed using simple linear regression.In the regression printout shown below, which of the following statements is not true? SUMMARY OUTPUT   ANOVA    </strong> A)Payroll is a good predictor of Sales based on α = 0.05. B)There is evidence of a positive linear relationship between Sales and Payroll based on α = 0.05. C)Payroll is not a good predictor of Sales based on α = 0.01. D)The coefficient of determination is equal to 0.833333. <div style=padding-top: 35px>

A)Payroll is a good predictor of Sales based on α = 0.05.
B)There is evidence of a positive linear relationship between Sales and Payroll based on α = 0.05.
C)Payroll is not a good predictor of Sales based on α = 0.01.
D)The coefficient of determination is equal to 0.833333.
سؤال
Which of the following represents the underlying linear model for hypothesis testing?

A)Y = b0 + b1 X + ε
B)Y = b0 + b1 X
C)Y = β0 + β1 X + ε
D)Y = β0 + β1 X
سؤال
The primary difference between r2 and the adjusted r2 is that

A)the adjusted r2 accounts for the total number of variables in the regression model.
B)the adjusted r2 accounts for the number of independent variables in the regression model.
C)the adjusted r2 accounts for the number of dependent variables in the regression model.
D)the adjusted r2 accounts for multicollinearity.
سؤال
Which of the following is not a common pitfall of regression?

A)If the assumptions are not met, the statistical tests may not be valid.
B)Nonlinear relationships cannot be incorporated.
C)Two variables may be highly correlated to one another but one is not causing the other to change.
D)Concluding that a statistically significant relationship implies practical value.
سؤال
A healthcare executive is using regression to predict total revenues.She has decided to include both patient length of stay and insurance type in her model.Insurance type can be grouped into the following categories: Medicare, Medicaid, Managed Care, Self-Pay, and Charity.Which of the following is true?

A)Insurance type will be represented in the regression model by five binary variables.
B)Insurance type will be represented in the regression model by six dummy variables.
C)Insurance type will be represented in the regression model by five dummy variables.
D)Insurance type will be represented in the regression model by four binary variables.
سؤال
A healthcare executive is using regression to predict total revenues.She has decided to include both patient length of stay and insurance type in her model.Insurance type can be grouped into three categories: Government-Funded, Private-Pay, and Other.Her model is

A)Y = b0.
B)Y = b0 + b1 X1.
C)Y = b0 + b1 X1 + b2 X2.
D)Y = b0 + b1 X1 + b2 X2 + b3 X3.
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Deck 4: Regression Models
1
The null hypothesis in the F-test is that there is a linear relationship between the X and Y variables.
False
2
There is no relationship between variables unless the data points lie in a straight line.
False
3
In regression, a dependent variable is sometimes called a predictor variable.
False
4
The coefficient of determination gives the proportion of the variability in the dependent variable that is explained by the regression equation.
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5
The correlation coefficient has values between −1 and +1.
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6
Both the p-value for the F-test and r2 can be interpreted the same with multiple regression models as they are with simple linear models.
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7
Errors are also called residuals.
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8
The regression line minimizes the sum of the squared errors.
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9
When the significance level is small enough in the F-test, we can reject the null hypothesis that there is no linear relationship.
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10
An F-test is used to determine if there is a relationship between the dependent and independent variables.
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11
The coefficients of each independent variable in a multiple regression model represent slopes.
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12
If the significance level for the F-test is high enough, there is a relationship between the dependent and independent variables.
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13
For statistical tests of significance about the coefficients, the null hypothesis is that the slope is 1.
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14
The multiple regression model includes several dependent variables.
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15
Estimates of the slope, intercept, and error of a regression model are found from sample data.
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16
Summing the error values in a regression model is misleading because negative errors cancel out positive errors.
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17
In regression, there is random error that can be predicted.
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18
In regression, an independent variable is sometimes called a response variable.
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19
Often, a plot of the residuals will highlight any glaring violations of the assumptions.
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20
If the assumptions of regression have been met, errors plotted against the independent variable will typically show patterns.
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21
A dummy variable can be assigned up to three values.
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22
If multicollinearity exists, then individual interpretation of the variables is questionable, but the overall model is still good for prediction purposes.
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23
The random error in a regression equation

A)is the predicted error.
B)includes both positive and negative terms.
C)will sum to a large positive number.
D)is used to estimate the accuracy of the slope.
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24
A high correlation always implies that one variable is causing a change in the other variable.
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25
Multicollinearity exists when a variable is correlated to other variables.
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26
The adjusted r2 will always increase as additional variables are added to the model.
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27
The best model is a statistically significant model with a high r-square and few variables.
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28
Which of these statements is best?

A)In regression, an independent variable is sometimes called a response variable.
B)A scatter diagram is a graphical depiction of the relationship between the dependent and independent variables.
C)In a scatter diagram, the dependent variable is typically plotted on the horizontal axis.
D)There is no relationship between variables unless the data points lie in a straight line.
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29
Dummy variables for regression analysis can take on a value of either -1 or +1.
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30
Which of the following statements is true regarding a scatter diagram?

A)It provides very little information about the relationship between the regression variables.
B)It is a plot of the independent and dependent variables.
C)It is a line chart of the independent and dependent variables.
D)It has a value between -1 and +1.
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31
The value of r2 can never decrease when more variables are added to the model.
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32
The multiple regression model includes several intercept terms.
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33
In regression, a binary variable is also called an indicator variable.
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34
Transformations may be used when nonlinear relationships exist between variables.
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35
The number of dummy variables must equal 1 less than the number of categories of the qualitative variable.
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36
A variable should be added to the model regardless of the impact (increase or decrease)on the adjusted r2 value.
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37
Another name for a dummy variable is a binary variable.
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38
Which of these statements is best?

A)In regression, an independent variable is sometimes called a response variable.
B)One purpose of regression is to understand the relationship between variables.
C)One purpose of regression is to predict the value of one variable based on the standard deviation of that variable.
D)The variable to be predicted is the independent variable.
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39
A reference to the criterion used to select the regression line, to minimize the squared distances between the estimated straight line and the observed values is called

A)Mean square error.
B)Sum of Squares.
C)Maximum likelihood.
D)Least Squares.
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40
The multiple regression model includes multiple slope coefficients.
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41
Which of the following conditions can be detected from residual analysis?

A)nonconstant variance
B)multicollinearity
C)dummy variables
D)presence of transformations
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42
The problem of nonconstant error variance is detected in residual analysis by which of the following?

A)a cone pattern
B)an arched pattern
C)a random pattern
D)an increasing pattern
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43
Which of these statements is best?

A)The regression model assumes the error terms are dependent.
B)The regression model assumes the errors are normally distributed.
C)The errors in a regression model are assumed to have an increasing mean.
D)The errors in a regression model are assumed to have zero variance.
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44
The correlation coefficient resulting from a particular regression analysis was 0.25.What was the coefficient of determination?

A)0.5
B)-0.5
C)0.0625
D)There is insufficient information to answer the question.
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45
The coefficient of determination resulting from a particular regression analysis was 0.85.What was the slope of the regression line?

A)0.85
B)-0.85
C)0.922
D)There is insufficient information to answer the question.
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46
Which of the following statements is true about r2?

A)It is also called the coefficient of correlation.
B)It is also called the coefficient of determination.
C)It represents the percent of variation in X that is explained by Y.
D)It represents the percent of variation in the error that is explained by Y.
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47
Which of the following is not an assumption of the regression model?

A)The errors are independent.
B)The errors should have a standard deviation equal to one.
C)The errors are normally distributed.
D)The errors have constant variance.
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48
Which of these statements is best?

A)The SST measures the total variability in the dependent variable about the regression line.
B)The SSE measures the total variability in the independent variable about the regression line.
C)The SSR indicates how much of the total variability in the dependent variable is explained by the regression model.
D)The coefficient of determination takes on values between -1 and + 1.
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49
A dummy variable is also called a(n)

A)indicator variable.
B)dependent variable.
C)continuous variable.
D)response variable.
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50
In a good regression model the residual plot shows

A)a cone pattern.
B)an arched pattern.
C)a random pattern.
D)an increasing pattern.
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51
The diagram below illustrates data with a <strong>The diagram below illustrates data with a  </strong> A)negative correlation coefficient. B)zero correlation coefficient. C)positive correlation coefficient. D)correlation coefficient equal to +1.

A)negative correlation coefficient.
B)zero correlation coefficient.
C)positive correlation coefficient.
D)correlation coefficient equal to +1.
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52
The coefficient of determination resulting from a particular regression analysis was 0.85.What was the correlation coefficient, assuming a positive linear relationship?

A)0.5
B)-0.5
C)0.922
D)There is insufficient information to answer the question.
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53
The sum of squared error (SSE)is

A)a measure of the total variation in Y about the mean.
B)a measure of the total variation in X about the mean.
C)a measure in the variation of Y about the regression line.
D)a measure in the variation of X about the regression line.
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54
Which of the following is an assumption of the regression model?

A)The errors are independent.
B)The errors are not normally distributed.
C)The errors have a standard deviation of zero.
D)The errors have an irregular variance.
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55
Which of the following statements is not true about regression models?

A)Estimates of the slope are found from sample data.
B)The regression line minimizes the sum of the squared errors.
C)The error is found by subtracting the actual data value from the predicted data value.
D)The dependent variable is the explanatory variable.
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56
Which of the following equalities is correct?

A)SST = SSR + SSE
B)SSR = SST + SSE
C)SSE = SSR + SST
D)SST = SSC + SSR
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57
Which of these statements is best?

A)If the assumptions of regression have been met, errors plotted against the independent variable will typically show patterns.
B)The error standard deviation is estimated by MSE.
C)Often, a plot of the residuals will highlight any glaring violations of the assumptions.
D)The standard error of the estimate is also called the variance of the regression.
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58
Which of these statements is best?

A)In any regression model, there is an implicit assumption that there is no preexisting relationship between the variables.
B)In regression, there is random error that can be predicted.
C)Estimates of the slope, intercept, and error of a regression model are found from sample data.
D)Error is the difference in the actual value and the predicted value.
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59
If computing a causal linear regression model of Y = a + bX and the resultant r2 is very near zero, then one would be able to conclude that

A)Y = a + bX is a good forecasting method.
B)Y = a + bX is not a good forecasting method.
C)a multiple linear regression model is a good forecasting method for the data.
D)a multiple linear regression model is not a good forecasting method for the data.
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60
The problem of a nonlinear relationship is detected in residual analysis by which of the following?

A)a cone pattern
B)an arched pattern
C)a random pattern
D)an increasing pattern
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61
When an independent variable is correlated with one other independent variable, the variables are said to be

A)collinear.
B)pairwise.
C)independent.
D)mutually exclusive.
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62
Which of the following is true regarding a regression model with multicollinearity, a high r2 value, and a low F-test significance level?

A)The model is not a good prediction model.
B)The high value of r2 is due to the multicollinearity.
C)The interpretation of the coefficients is valuable.
D)The significance level tests for the coefficients are not valid.
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63
If a qualitative variable has three categories, how many dummy variables are needed?

A)0
B)1
C)2
D)3
E)4
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64
A prediction equation for starting salaries (in $1,000s)and SAT scores was performed using simple linear regression.In the regression printout shown below, what can be said about the level of significance for the overall model? SUMMARY OUTPUT
<strong>A prediction equation for starting salaries (in $1,000s)and SAT scores was performed using simple linear regression.In the regression printout shown below, what can be said about the level of significance for the overall model? SUMMARY OUTPUT   ANOVA    </strong> A)SAT is not a good predictor for starting salary. B)The significance level for the intercept indicates the model is not valid. C)The significance level for SAT indicates the slope is equal to zero. D)The significance level for SAT indicates the slope is not equal to zero. ANOVA
<strong>A prediction equation for starting salaries (in $1,000s)and SAT scores was performed using simple linear regression.In the regression printout shown below, what can be said about the level of significance for the overall model? SUMMARY OUTPUT   ANOVA    </strong> A)SAT is not a good predictor for starting salary. B)The significance level for the intercept indicates the model is not valid. C)The significance level for SAT indicates the slope is equal to zero. D)The significance level for SAT indicates the slope is not equal to zero. <strong>A prediction equation for starting salaries (in $1,000s)and SAT scores was performed using simple linear regression.In the regression printout shown below, what can be said about the level of significance for the overall model? SUMMARY OUTPUT   ANOVA    </strong> A)SAT is not a good predictor for starting salary. B)The significance level for the intercept indicates the model is not valid. C)The significance level for SAT indicates the slope is equal to zero. D)The significance level for SAT indicates the slope is not equal to zero.

A)SAT is not a good predictor for starting salary.
B)The significance level for the intercept indicates the model is not valid.
C)The significance level for SAT indicates the slope is equal to zero.
D)The significance level for SAT indicates the slope is not equal to zero.
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65
The sum of the squares total (SST)

A)measures the total variability in Y about the mean.
B)measures the total variability in X about the mean.
C)measures the variability in Y about the regression line.
D)measures the variability in X about the regression line.
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66
A healthcare executive is using regression to predict total revenues.She is deciding whether or not to include both patient length of stay and insurance type in her model.Her first regression model only included patient length of stay.The resulting r2 was .83, with an adjusted r2 of .82 and her level of significance was .003.In the second model, she included both patient length of stay and insurance type.The r2 was .84 and the adjusted r2 was .80 for the second model and the level of significance did not change.Which of the following statements is true?

A)The second model is a better model.
B)The first model is a better model.
C)The r2 increased when additional variables were added because these variables significantly contribute to the prediction of total revenues.
D)The adjusted r2 always increases when additional variables are added to the model.
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67
Suppose that you believe that a cubic relationship exists between the independent variable (of time)and the dependent variable Y.Which of the following would represent a valid linear regression model?

A)Y = b0 + b1 X, where X = time3
B)Y = b0 + b1 X3, where X = time
C)Y = b0 + 3b1 X, where X = time3
D)Y = b0 + 3b1 X, where X = time
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68
Which of these statements about multiple regression is best?

A)The p-value for the F-test may not be interpreted the same with multiple regression models as they are with simple linear models.
B)The multiple regression model includes several dependent variables.
C)The multiple regression model includes several intercept terms.
D)The multiple regression model includes multiple slope coefficients.
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69
In the model Y = β0 + β1X1 + β2X2 + ε, which of these parameters represents an independent variable?

A)the X1
B)the Y
C)the β1
D)the ε
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70
An automated process to systematically add or delete independent variables from a regression model is called

A)nonlinear regression.
B)linear regression.
C)residual analysis.
D)stepwise regression.
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71
Which of the following statements provides the best guidance for model building?

A)If the value of r2 increases as more variables are added to the model, the variables should remain in the model, regardless of the magnitude of increase.
B)If the value of the adjusted r2 increases as more variables are added to the model, the variables should remain in the model.
C)If the value of r2 increases as more variables are added to the model, the variables should not remain in the model, regardless of the magnitude of the increase.
D)If the value of the adjusted r2 increases as more variables are added to the model, the variables should not remain in the model.
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72
The condition of an independent variable being correlated to one or more other independent variables is referred to as

A)multicollinearity.
B)statistical significance.
C)linearity.
D)nonlinearity.
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73
The mean square error (MSE)is

A)denoted by s.
B)denoted by k.
C)the SSE divided by the number of observations.
D)the SSE divided by the degrees of freedom.
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74
Which of the following statements is false concerning the hypothesis testing procedure for a regression model?

A)The F-test statistic is used.
B)The null hypothesis is that the true slope coefficient is equal to zero.
C)The null hypothesis is rejected if the adjusted r2 is above the critical value.
D)An α level must be selected.
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75
A prediction equation for sales and payroll was performed using simple linear regression.In the regression printout shown below, which of the following statements is not true? SUMMARY OUTPUT
<strong>A prediction equation for sales and payroll was performed using simple linear regression.In the regression printout shown below, which of the following statements is not true? SUMMARY OUTPUT   ANOVA    </strong> A)Payroll is a good predictor of Sales based on α = 0.05. B)There is evidence of a positive linear relationship between Sales and Payroll based on α = 0.05. C)Payroll is not a good predictor of Sales based on α = 0.01. D)The coefficient of determination is equal to 0.833333. ANOVA
<strong>A prediction equation for sales and payroll was performed using simple linear regression.In the regression printout shown below, which of the following statements is not true? SUMMARY OUTPUT   ANOVA    </strong> A)Payroll is a good predictor of Sales based on α = 0.05. B)There is evidence of a positive linear relationship between Sales and Payroll based on α = 0.05. C)Payroll is not a good predictor of Sales based on α = 0.01. D)The coefficient of determination is equal to 0.833333. <strong>A prediction equation for sales and payroll was performed using simple linear regression.In the regression printout shown below, which of the following statements is not true? SUMMARY OUTPUT   ANOVA    </strong> A)Payroll is a good predictor of Sales based on α = 0.05. B)There is evidence of a positive linear relationship between Sales and Payroll based on α = 0.05. C)Payroll is not a good predictor of Sales based on α = 0.01. D)The coefficient of determination is equal to 0.833333.

A)Payroll is a good predictor of Sales based on α = 0.05.
B)There is evidence of a positive linear relationship between Sales and Payroll based on α = 0.05.
C)Payroll is not a good predictor of Sales based on α = 0.01.
D)The coefficient of determination is equal to 0.833333.
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76
Which of the following represents the underlying linear model for hypothesis testing?

A)Y = b0 + b1 X + ε
B)Y = b0 + b1 X
C)Y = β0 + β1 X + ε
D)Y = β0 + β1 X
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77
The primary difference between r2 and the adjusted r2 is that

A)the adjusted r2 accounts for the total number of variables in the regression model.
B)the adjusted r2 accounts for the number of independent variables in the regression model.
C)the adjusted r2 accounts for the number of dependent variables in the regression model.
D)the adjusted r2 accounts for multicollinearity.
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78
Which of the following is not a common pitfall of regression?

A)If the assumptions are not met, the statistical tests may not be valid.
B)Nonlinear relationships cannot be incorporated.
C)Two variables may be highly correlated to one another but one is not causing the other to change.
D)Concluding that a statistically significant relationship implies practical value.
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79
A healthcare executive is using regression to predict total revenues.She has decided to include both patient length of stay and insurance type in her model.Insurance type can be grouped into the following categories: Medicare, Medicaid, Managed Care, Self-Pay, and Charity.Which of the following is true?

A)Insurance type will be represented in the regression model by five binary variables.
B)Insurance type will be represented in the regression model by six dummy variables.
C)Insurance type will be represented in the regression model by five dummy variables.
D)Insurance type will be represented in the regression model by four binary variables.
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80
A healthcare executive is using regression to predict total revenues.She has decided to include both patient length of stay and insurance type in her model.Insurance type can be grouped into three categories: Government-Funded, Private-Pay, and Other.Her model is

A)Y = b0.
B)Y = b0 + b1 X1.
C)Y = b0 + b1 X1 + b2 X2.
D)Y = b0 + b1 X1 + b2 X2 + b3 X3.
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