Question 1

Which of the following situations is best addressed by regression?

Accepted Answer

A)  Is there an association between market share and size of the sales force after adjusting for the effect of sales promotion? 
B)  Determine how much of the variation in the dependent variable (store sales) can be explained by the independent variables (price and level of advertisement). 
C)  Are consumers' perceptions of quality related to their perceptions of prices when the effect of brand image is controlled? 
D)  Is there an association between market share and the size of the sales force? 
A)  Is there an association between market share and size of the sales force after adjusting for the effect of sales promotion? 
B)  Determine how much of the variation in the dependent variable (store sales) can be explained by the independent variables (price and level of advertisement). 
C)  Are consumers' perceptions of quality related to their perceptions of prices when the effect of brand image is controlled? 
D)  Is there an association between market share and the size of the sales force?

Question 2

The correlation coefficient between two variables varies depending on their underlying units of measurement.

Accepted Answer

A) True 
 B)False

Question 3

Which statement is true about cross- validation.

Accepted Answer

A)  It examines whether the regression model continues to hold on comparable data not used in the estimation. 
B)  A special form of validation is called double cross- validation where the sample is split into halves. 
C)  It can be used for evaluating the model for chance variations in the data and other problems associated with regression. 
D)  All of the above statements are true. 
A)  It examines whether the regression model continues to hold on comparable data not used in the estimation. 
B)  A special form of validation is called double cross- validation where the sample is split into halves. 
C)  It can be used for evaluating the model for chance variations in the data and other problems associated with regression. 
D)  All of the above statements are true.

Question 4

When fitting a straight line to a scattergram, the best- fitting line is called the regression line.

Accepted Answer

A) True 
 B)False

Question 5

Multicollinearity arises when intercorrelations among the predictors are very low.

Accepted Answer

A) True 
 B)False

Question 6

Which equation depicts the relationship between the standardized and non- standardized regression coefficients?

Accepted Answer

A)  B2yx = byx(Sx/Sy) 
B)  B2yx = byx(S2x/S2y) 
C)  Byx = byx(Sx/Sy) 
D)  Byx = byx(S2x/S2y) 
A)  B2yx = byx(Sx/Sy) 
B)  B2yx = byx(S2x/S2y) 
C)  Byx = byx(Sx/Sy) 
D)  Byx = byx(S2x/S2y)

Question 7

The hypotheses for the test for significance of the coefficient of determination are: H0: R2pop = 0 ; H1: R2pop > 0.

Accepted Answer

A) True 
 B)False

Question 8

Given the multiple regression equation, Y^ = a + b1X1 + b2X2, and the bivariate equation Y^ = a + bX, why is the partial regression coefficient, b1, different from the regression coefficient, b, obtained by regressing Y on only X1?

Accepted Answer

This happens because X₁and X₂are usually c

Question 9

In the equation COVxy/ SxSy, Sx and Sy represent .

Accepted Answer

A)  the means of X and Y 
B)  the strength of the effects of X and Y 
C)  the variances of X and Y 
D)  the standard deviation of X and Y 
A)  the means of X and Y 
B)  the strength of the effects of X and Y 
C)  the variances of X and Y 
D)  the standard deviation of X and Y

Question 10

Regression in which a single independent variable has been recoded into dummy variables is equivalent to one- way analysis of variance.

Accepted Answer

A) True 
 B)False

Question 11

In what ways can regression analysis be used?

Accepted Answer

1. Determine whether the independent var

Question 12

If the nonmetric variables are nominal and numeric, Spearman's rho and Kendall's tau are two measures of nonmetric correlation that can be used to examine the correlation between them.

Accepted Answer

A) True 
 B)False

Question 13

What are the assumptions made by the regression model in estimating the parameters and in significance testing?

Accepted Answer

1. The error term is normally distribute

Question 14

The bivariate regression model that accounts for the probabilistic or stochastic nature of the relationship between X and Y is _ .

Accepted Answer

A)  Y^ = a + b1X1 + b2X2  
B)  Y^ i = a + bXi 
C)  Y = þ0 + þ1 Xi  
D)  Yi = þ0 + þ1 Xi + ei 
A)  Y^ = a + b1X1 + b2X2  
B)  Y^ i = a + bXi 
C)  Y = þ0 + þ1 Xi  
D)  Yi = þ0 + þ1 Xi + ei

Question 15

The is a statistic summarizing the strength of association between two metric variables.

Accepted Answer

A)  ANOVA 
B)  partial correlation coefficient 
C)  product moment correlation 
D)  multiple regression analysis 
A)  ANOVA 
B)  partial correlation coefficient 
C)  product moment correlation 
D)  multiple regression analysis

Question 16

The partial correlation coefficient is generally viewed as more important than the part correlation coefficient.

Accepted Answer

A) True 
 B)False

Question 17

ry(x.z) represents the .

Accepted Answer

A)  partition correlation 
B)  Pearson correlation 
C)  part correlation 
D)  partial correlation 
A)  partition correlation 
B)  Pearson correlation 
C)  part correlation 
D)  partial correlation

Question 18

Briefly explain how a scatter diagram benefits the researcher.

Accepted Answer

A scatter diagram is useful for determin

Question 19

To estimate the accuracy of predicted values, Y^ , found in bivariate regression, it is useful to calculate the , the standard deviation of the actual Y values for the predicted Y^  values.

Accepted Answer

A)  coefficient of determination 
B)  covariance 
C)  standard error of the estimate 
D)  standard error 
A)  coefficient of determination 
B)  covariance 
C)  standard error of the estimate 
D)  standard error

Question 20

Which statement is not correct about the partial correlation coefficient?

Accepted Answer

A)  The partial correlation coefficient can be calculated by a knowledge of the simple correlations alone. 
B)  Partial correlations can be helpful for detecting spurious relationships. 
C)  The partial correlation coefficient represents the correlation between Y and X when the linear effects of the other independent variables have been removed from X but not from Y. 
D)  The partial correlation coefficient is generally viewed as more important than the part correlation coefficient. 
A)  The partial correlation coefficient can be calculated by a knowledge of the simple correlations alone. 
B)  Partial correlations can be helpful for detecting spurious relationships. 
C)  The partial correlation coefficient represents the correlation between Y and X when the linear effects of the other independent variables have been removed from X but not from Y. 
D)  The partial correlation coefficient is generally viewed as more important than the part correlation coefficient.

Exam 17: Correlation and Regression

Which of the following situations is best addressed by regression?

The correlation coefficient between two variables varies depending on their underlying units of measurement.

Which statement is true about cross- validation.

When fitting a straight line to a scattergram, the best- fitting line is called the regression line.

Multicollinearity arises when intercorrelations among the predictors are very low.

Which equation depicts the relationship between the standardized and non- standardized regression coefficients?

The hypotheses for the test for significance of the coefficient of determination are: H0: R2pop = 0 ; H1: R2pop > 0.

Given the multiple regression equation, Y^ = a + b1X1 + b2X2, and the bivariate equation Y^ = a + bX, why is the partial regression coefficient, b1, different from the regression coefficient, b, obtained by regressing Y on only X1?

In the equation COVxy/ SxSy, Sx and Sy represent .

Regression in which a single independent variable has been recoded into dummy variables is equivalent to one- way analysis of variance.

In what ways can regression analysis be used?

If the nonmetric variables are nominal and numeric, Spearman's rho and Kendall's tau are two measures of nonmetric correlation that can be used to examine the correlation between them.

What are the assumptions made by the regression model in estimating the parameters and in significance testing?

The bivariate regression model that accounts for the probabilistic or stochastic nature of the relationship between X and Y is _ .

The is a statistic summarizing the strength of association between two metric variables.

The partial correlation coefficient is generally viewed as more important than the part correlation coefficient.

ry(x.z) represents the .

Briefly explain how a scatter diagram benefits the researcher.

To estimate the accuracy of predicted values, Y^ , found in bivariate regression, it is useful to calculate the , the standard deviation of the actual Y values for the predicted Y^ values.

Which statement is not correct about the partial correlation coefficient?

Introduction to Marketing

Defining the Marketing Research

Research Design

Exploratory Research Design

Exploratory Research Design

Descriptive Research Design

Causal Research Design

Measurement and Scaling

Measurement and Scaling

Questionnaire and Form

Sampling: Design and

Sampling: Final and Initial

Fieldwork

Data Preparation

Frequency Distribution, Crosstabulation, and Hypothesis

Analysis of Variance and

Discriminant and Logit

Factor Analysis

Cluster Analysis

Multidimensional Scaling and

Structural Equation Modeling

Report Preparation and

International Marketing

Filters

The hypotheses for the test for significance of the coefficient of determination are: H₀: R²_pop= 0 ; H₁: R²_pop> 0.

Given the multiple regression equation, Y^{^}= a + b₁X₁+ b₂X₂_,and the bivariate equation Y^{^}= a + bX, why is the partial regression coefficient, b₁, different from the regression coefficient, b, obtained by regressing Y on only X₁?

In the equation COV_xy/ S_xS_y, S_xand S_yrepresent .

r_y(x.z)represents the .

To estimate the accuracy of predicted values, Y^{^}, found in bivariate regression, it is useful to calculate the , the standard deviation of the actual Y values for the predicted Y^{^}values.