Question 1

Assume you are developing a general conceptual model for the Advanced Automotive Concepts dataset.Which of the following variables would most likely be your dependent variable?

Accepted Answer

A) probability of purchasing a particular auto model 
B) customer income 
C) customer gender 
D) customer education 
E) customer occupation

Question 2

Which form of regression is useful when the researcher has many independent variables and wants to narrow the set down to a smaller number?

Accepted Answer

A) multiple component reduction 
B) stepwise multiple regression 
C) variance deflation regression 
D) variance inflation regression 
E) narrow regression

Question 3

Once we establish,through multiple regression analysis,that certain independent variables are statistically significant in predicting a dependent variable,we may assume this relationship to be one of cause and effect.

Accepted Answer

A) True 
 B)False

Question 4

When you have independent variables that are not significant in multiple regression analysis,it is appropriate to take them out and rerun the regression.The new model is referred to as a "trimmed" model.

Accepted Answer

A) True 
 B)False

Question 5

There is a type of multiple regression,called stepwise multiple regression,that does the trimming operation automatically.

Accepted Answer

A) True 
 B)False

Question 6

In the formula for bivariate regression analysis,the change in y for each one-unit change in x is known as the slope.

Accepted Answer

A) True 
 B)False

Question 7

The multiple R,also called the coefficient of determination,in multiple regression ranges from 0 to +1.00 and represents the amount of the dependent variable "explained" by the combined independent variables.

Accepted Answer

A) True 
 B)False

Question 8

A form of regression analysis where more than one independent variable is used in the regression equation is known as:

Accepted Answer

A) regression planes. 
B) additivity. 
C) multiple regression analysis. 
D) independence assumption. 
E) MANOVA.

Question 9

In regression,the line that runs through the points on a scatter diagram is positioned to minimize the vertical distances away from the line of the various points because of the least squares criterion.

Accepted Answer

A) True 
 B)False

Question 10

Which statistic is used to determine whether or not multicollinearity is a concern in multiple regression?

Accepted Answer

A) coefficient of determination 
B) multicol Z 
C) multicol R 
D) VIF (variance inflation factor) 
E) Q

Question 11

Independent variables are normally measured in different units,so to determine the relative importance of the beta weights between independent variables we would use:

Accepted Answer

A) a screening variable. 
B) a trimmed model. 
C) standardized beta coefficients. 
D) betas measured in "like-units." 
E) weighted beta coefficients.

Question 12

In bivariate regression,if the F value is significant (say .05 or less),then:

Accepted Answer

A) we accept the null hypothesis that a straight-line model fits our data. 
B) we reject the null hypothesis that a straight-line model does not fit our data. 
C) we abandon our efforts to analyze the two variables. 
D) we check for outliers. 
E) we rerun the regression.

Question 13

In bivariate regression analysis,the higher the Adjusted R Square value:

Accepted Answer

A) the lower the predictive power of the analysis. 
B) the better the straight line's fit to the scatter points. 
C) the worse the straight line's fit to the scatter points. 
D) the closer to 0 it will be. 
E) None of the above;there is no Adjusted R Square value in regression analysis.

Question 14

Stepwise multiple regression is useful if a researcher has many dependent variables but needs additional dependent variables in order to obtain a good predictive model.

Accepted Answer

A) True 
 B)False

Question 15

In the formula for a straight line,the slope is defined as:

Accepted Answer

A) the change in y for any 1-unit change in x. 
B) where the line cuts the y axis when x = 0. 
C) the variable used to predict the dependent variable. 
D) the dependent variable. 
E) the predicted variable.

Question 16

Which of the following are warnings that the textbook authors give regarding regression analysis?

Accepted Answer

A) It is complicated and requires large computer memory. 
B) It does not give you cause-and-effect statements,and it is expensive to run. 
C) It does not give you cause-and-effect statements,and the text's coverage of regression analysis only scratches the surface of this topic. 
D) It is expensive,and you should not apply regression to predict data outside the boundaries of the data used to develop the regression model. 
E) No warnings are given.

Question 17

The two ways of making a prediction are extension analysis and baseline predictive modeling.

Accepted Answer

A) True 
 B)False

Question 18

While the scaling assumptions of multiple regression require that both the independent and dependent variables be at least interval-scaled,we may use nominal independent variables by using:

Accepted Answer

A) ratio-scaled variables. 
B) standardized beta coefficients. 
C) dummy variables. 
D) temporary variables. 
E) semi-ratio variables.

Question 19

In regression,the variable being predicted,b,is known as the dependent variable.

Accepted Answer

A) True 
 B)False

Question 20

In regression,the variable being predicted,y,is known as the dependent variable.

Accepted Answer

A) True 
 B)False

Exam 15: Understanding Regression Analysis Basics

Assume you are developing a general conceptual model for the Advanced Automotive Concepts dataset.Which of the following variables would most likely be your dependent variable?

Which form of regression is useful when the researcher has many independent variables and wants to narrow the set down to a smaller number?

Once we establish,through multiple regression analysis,that certain independent variables are statistically significant in predicting a dependent variable,we may assume this relationship to be one of cause and effect.

When you have independent variables that are not significant in multiple regression analysis,it is appropriate to take them out and rerun the regression.The new model is referred to as a "trimmed" model.

There is a type of multiple regression,called stepwise multiple regression,that does the trimming operation automatically.

In the formula for bivariate regression analysis,the change in y for each one-unit change in x is known as the slope.

The multiple R,also called the coefficient of determination,in multiple regression ranges from 0 to +1.00 and represents the amount of the dependent variable "explained" by the combined independent variables.

A form of regression analysis where more than one independent variable is used in the regression equation is known as:

In regression,the line that runs through the points on a scatter diagram is positioned to minimize the vertical distances away from the line of the various points because of the least squares criterion.

Which statistic is used to determine whether or not multicollinearity is a concern in multiple regression?

Independent variables are normally measured in different units,so to determine the relative importance of the beta weights between independent variables we would use:

In bivariate regression,if the F value is significant (say .05 or less) ,then:

In bivariate regression analysis,the higher the Adjusted R Square value:

Stepwise multiple regression is useful if a researcher has many dependent variables but needs additional dependent variables in order to obtain a good predictive model.

In the formula for a straight line,the slope is defined as:

Which of the following are warnings that the textbook authors give regarding regression analysis?

The two ways of making a prediction are extension analysis and baseline predictive modeling.

While the scaling assumptions of multiple regression require that both the independent and dependent variables be at least interval-scaled,we may use nominal independent variables by using:

In regression,the variable being predicted,b,is known as the dependent variable.

In regression,the variable being predicted,y,is known as the dependent variable.