Question 1

If a researcher wants to predict the value category of the variable "high school graduation" (yes or no) based upon several predictor variables of different levels of measurement (nominal, ordinal, and interval), she should use a form of:
A) logistical regression
B) discriminate analysis
C) multiple linear regression
D) simple linear regression

Accepted Answer

B

Question 2

A regression equation cannot be used to predict the value of an outcome variable if:
A) the correlation between variables is not a perfect one.
B) the value of the predictor varible is larger or smaller than any value that was used to produce the regression equation.
C) the value of the predictor value falls between two values that were used to produce the regression equation.
D) the value of the predictor variable is not a whole number.

Accepted Answer

A regression equation is based on the values of the predictor variable that were used to produce it. If a value falls outside of the range of values used to create the equation, it may not be accurate for predicting the outcome variable. Therefore, if the value of the predictor variable is larger or smaller than any value used to produce the regression equation, it cannot be used to predict the value of the outcome variable. The other choices do not accurately describe the limitations of using a regression equation for prediction.

Question 3

When a potential predictor variable is only nominal level (e.g., marital status), it can still be included in multiple linear regression analysis by the use of:
A) simultaneous data entry
B) incremental data entry
C) stepwise data entry
D) creation of dummy variables

Accepted Answer

When a potential predictor variable is only nominal level, it cannot be directly included in multiple linear regression analysis. The best way to include it is by creating dummy variables. This creates a separate variable for each level of the nominal variable, allowing them to be included in the analysis as binary (0 or 1) variables. Therefore, the best choice is D, creation of dummy variables.

Question 4

If a correlation between variables is quite weak why would a researcher not conduct a regression analysis?
A) It would be impossible to construct a regression equation.
B) The correlation between variables could not possibly be statistically significant.
C) Predicted values would be so inaccurate as to be virtually useless.
D) all of the above

Accepted Answer

A weak correlation does not prevent the construction of a regression equation or imply that the correlation cannot be statistically significant. However, it does mean that any predictions made using the regression model would be highly inaccurate, making the model practically useless for predictive purposes.

Question 5

If another student taking the make-up test did not study at all for the test (just like three other students who took the original test), what grade can we predict that she will receive?
A) 85
B) 55
C) 25
D) 65

Accepted Answer

Since the three students who did not study at all received a score of 55 on the original test, it is safe to assume that this student will receive the same score since they did not study either.

Question 6

If all of the predictor variables in the previous question had been interval or ratio level and normally distributed, the statistical analysis of choice would have been:
A) logistic regression
B) discriminate analysis
C) multiple linear regression
D) simple linear regression

Accepted Answer

The answer of If all of the predictor variables in...

Question 7

In a regression equation, what symbol is used to denote the point where the regression line (if continued) would intercept the y axis?
A) Y'
B) b
C) a
D) X

Accepted Answer

The symbol used to denote the point where the regression line would intercept the y-axis is "a", which represents the y-intercept.

Question 8

Marcia also was sick the day of the test. How many hours would we estimate that she would need to study to get a grade of 91 (an A) on the make-up test?
A) 10
B) 6
C) 9
D) 4

Accepted Answer

The answer of Marcia also was sick the day of...

Question 9

In a social work research class, the instructor asked each student how many hours he or she had studied for a test and then compared this (predictor) variable with his or her test grade (the outcome variable). Data analysis produced a high positive correlation and the regression equation Y' = 55 + 6(X). Mark was sick when the exam was given. He studied 5 hours prior to taking the make-up test. What is Mark's predicted grade on the test?
A) 61
B) 30
C) 85
D) 94

Accepted Answer

Using the regression equation Y' = 55 + 6(X):
Y' = 55 + 6(5)
Y' = 85
Therefore, Mark's predicted grade on the test is 85.

Question 10

What do statisticians call the estimate of how well a regression line will predict measurements of the outcome variable?
A) the confusion matrix
B) the standard error
C) the slope
D) the discriminant analysis

Accepted Answer

The estimate of how well a regression line will predict measurements of the outcome variable is measured by the standard error.

Question 11

The terms "simultaneous," "heirarchal," and "stepwise" describe different ways that when conducting multiple linear regression analysis.
A) variables are categorized
B) regression equations are created
C) data are entered
D) findings are interpreted

Accepted Answer

The terms "simultaneous," "hierarchical," and "stepwise" describe different approaches to entering variables into a multiple linear regression analysis. These approaches relate to how the data is entered and how the regression equation is created. Therefore, the correct answer is C - data are entered. Variables can be categorized based on their type (e.g., continuous, categorical), but this is not necessarily related to the approach used in entering the variables. Findings are interpreted after the regression analysis is completed, so this is not the correct answer either.

Question 12

When conducting regression analysis, can the predictor variable and the outcome variable be reversed?
A) No, when two variables are correlated one always produces the values of the other and not vice versa.
B) No, it would be impossible to construct a regression equation.
C) Yes, the regression equation would be unchanged. However, it would not produce accurate predictions.
D) Yes, but a different regression equation would be produced.

Accepted Answer

Yes, the predictor variable and outcome variable can be reversed, but a different regression equation would be produced. This is because the regression equation is based on the relationship between the predictor variable and the outcome variable, and reversing the roles of the variables would change this relationship. However, a valid regression analysis can still be conducted, but it would require a new analysis with a new equation.

Question 13

What statistical analysis allows a researcher to predict the value of an interval or ratio level outcome variable from the values of two or more interval or ratio level predictor variables?
A) multiple linear regression
B) logistical regression
C) simple linear regression
D) discriminant analysis

Accepted Answer

Multiple linear regression is used to predict the value of an interval or ratio level outcome variable from two or more interval or ratio level predictor variables. Logistic regression is used to predict the probability of a binary outcome variable, while simple linear regression is used to predict the value of an outcome variable from a single predictor variable. Discriminant analysis is used to classify cases into one of two or more categories based on their values on several predictor variables.

Question 14

What can regression analysis tell the researcher that correlation analysis alone cannot?
A) the strength of a relationship between variables
B) the direction of a relationship between variables
C) the probability that the relationship between variables within a research sample could have been produced by sampling error
D) the predicted value of an outcome variable if we know the value of a case's predictor variable.

Accepted Answer

Regression analysis can provide information on the predicted value of an outcome variable based on the known value of a predictor variable. Correlation analysis can only provide information on the strength and direction of a relationship between variables. The probability of sampling error can be calculated through statistical testing regardless of whether regression or correlation analysis is used.

Question 15

In a regression equation, what symbol is used to denote the predicted value of the outcome variable?
A) Y'
B) b
C) a
D) X

Accepted Answer

In regression analysis, the predicted value of the outcome variable is denoted by Y'. This symbol represents the value that the regression equation estimates for the outcome variable based on the values of the predictor variable(s).

Quiz 10: Regression Analyses

If a researcher wants to predict the value category of the variable "high school graduation" (yes or no) based upon several predictor variables of different levels of measurement (nominal, ordinal, and interval) , she should use a form of:

A regression equation cannot be used to predict the value of an outcome variable if:

When a potential predictor variable is only nominal level (e.g., marital status) , it can still be included in multiple linear regression analysis by the use of:

If a correlation between variables is quite weak why would a researcher not conduct a regression analysis?

If another student taking the make-up test did not study at all for the test (just like three other students who took the original test) , what grade can we predict that she will receive?

If all of the predictor variables in the previous question had been interval or ratio level and normally distributed, the statistical analysis of choice would have been:

In a regression equation, what symbol is used to denote the point where the regression line (if continued) would intercept the y axis?

Marcia also was sick the day of the test. How many hours would we estimate that she would need to study to get a grade of 91 (an A) on the make-up test?

What do statisticians call the estimate of how well a regression line will predict measurements of the outcome variable?

The terms "simultaneous," "heirarchal," and "stepwise" describe different ways that when conducting multiple linear regression analysis.

When conducting regression analysis, can the predictor variable and the outcome variable be reversed?

What statistical analysis allows a researcher to predict the value of an interval or ratio level outcome variable from the values of two or more interval or ratio level predictor variables?

What can regression analysis tell the researcher that correlation analysis alone cannot?

In a regression equation, what symbol is used to denote the predicted value of the outcome variable?