Question 1

Which statement is not true about regression analysis?

Accepted Answer

A)  Regression analysis is a powerful and flexible procedure for analyzing associative relationships between a metric dependent variable and one or more independent variables. 
B)  Regression can be used to predict the values of the dependent variable. 
C)  The terms dependent or criterion variables and independent or predictor variables in regression analysis do not imply that the criterion variable is dependent on the independent variables in a causal sense. 
D)  Regression analysis can be used to determine if color preference is related to product size and price. 
A)  Regression analysis is a powerful and flexible procedure for analyzing associative relationships between a metric dependent variable and one or more independent variables. 
B)  Regression can be used to predict the values of the dependent variable. 
C)  The terms dependent or criterion variables and independent or predictor variables in regression analysis do not imply that the criterion variable is dependent on the independent variables in a causal sense. 
D)  Regression analysis can be used to determine if color preference is related to product size and price.

Question 2

The regression equation for a categorical variable with four categories would be modeled as .

Accepted Answer

A)  Y^ = b1 D1 + b2 D2 + b3D3 + b4D4  
B)  Y^ = b1 D1 + b2 D2 + b3D3 
C)  Y^ i = a + b1 D1 + b2 D2 + b3D3 + b4D4  
D)  Y^ i = a + b1 D1 + b2 D2 + b3D3 
A)  Y^ = b1 D1 + b2 D2 + b3D3 + b4D4  
B)  Y^ = b1 D1 + b2 D2 + b3D3 
C)  Y^ i = a + b1 D1 + b2 D2 + b3D3 + b4D4  
D)  Y^ i = a + b1 D1 + b2 D2 + b3D3

Question 3

The estimated parameter b is usually referred to as the non- standardized regression coefficient.

Accepted Answer

A) True 
 B)False

Question 4

R2 cannot decrease as more independent variables are added to the regression equation.

Accepted Answer

A) True 
 B)False

Question 5

The coefficient of multiple determination is adjusted for the number of dependent variables and the sample size to account for diminishing returns.

Accepted Answer

A) True 
 B)False

Question 6

Which of the following is not a problem associated with multicollinearity?

Accepted Answer

A)  Predictor variables may be incorrectly included or removed in stepwise regression. 
B)  It becomes difficult to assess the relative importance of the independent variables in explaining the variation in the dependent variables. 
C)  It becomes difficult to compute the correct test statistic. 
D)  The partial regression coefficients may not be estimated precisely. The standard errors are likely to be high. 
A)  Predictor variables may be incorrectly included or removed in stepwise regression. 
B)  It becomes difficult to assess the relative importance of the independent variables in explaining the variation in the dependent variables. 
C)  It becomes difficult to compute the correct test statistic. 
D)  The partial regression coefficients may not be estimated precisely. The standard errors are likely to be high.

Question 7

is the appropriate test statistic to use to determine the significance of the coefficient of determination in bivariate regression.

Accepted Answer

A)  F statistic 
B)  m2  
C)  Z statistic 
D)  T statistic 
A)  F statistic 
B)  m2  
C)  Z statistic 
D)  T statistic

Question 8

The question of "How strongly are sales related to advertising expenditures when the effect of price is controlled?" is best answered via .

Accepted Answer

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

Question 9

The relationship between X and Y is spurious if .

Accepted Answer

A)  the correlation between X and Y disappears when the effect of Z is controlled 
B)  Y decreases exponentially with decreases in X 
C)  Y increases exponentially with increases in X 
D)  both A and C are correct 
A)  the correlation between X and Y disappears when the effect of Z is controlled 
B)  Y decreases exponentially with decreases in X 
C)  Y increases exponentially with increases in X 
D)  both A and C are correct

Question 10

is a state of very high intercorrelations among independent variables.

Accepted Answer

A)  Partial collinearity 
B)  Variable collinearity 
C)  Multicollinearity 
D)  Hypercollinearity 
A)  Partial collinearity 
B)  Variable collinearity 
C)  Multicollinearity 
D)  Hypercollinearity

Question 11

is best to use to determine how strongly sales are related to advertising expenditures.

Accepted Answer

A)  Multiple regression analysis 
B)  Partial correlation coefficient 
C)  Product moment correlation (r) 
D)  ANOVA 
A)  Multiple regression analysis 
B)  Partial correlation coefficient 
C)  Product moment correlation (r) 
D)  ANOVA

Question 12

The multiple correlation coefficient, R, can also be viewed as the simple correlation coefficient, r, between Y and Y^ .

Accepted Answer

A) True 
 B)False

Question 13

Which statement about the correlation coefficient, r, is true?

Accepted Answer

A)  Data obtained by using rating scales with a small number of categories tends to deflate r. 
B)  The calculation of r assumes that X and Y are metric variables whose distributions have the same shape. 
C)  The correlation coefficient computed for a population is denoted by q(rho). 
D)  All of the statements are true. 
A)  Data obtained by using rating scales with a small number of categories tends to deflate r. 
B)  The calculation of r assumes that X and Y are metric variables whose distributions have the same shape. 
C)  The correlation coefficient computed for a population is denoted by q(rho). 
D)  All of the statements are true.

Question 14

In the absence of ties, Kendall's l yields a closer approximation to the Pearson product moment correlation coefficient, q, than Spearman's qs.

Accepted Answer

A) True 
 B)False

Question 15

Because r indicates the degrees to which variation in one variable is related to variation in another, it can also be expressed in terms of the decomposition of the total variation.

Accepted Answer

A) True 
 B)False

Question 16

Partial correlations have an order associated with them. The order indicates how many variables are being adjusted or controlled. The simple correlation coefficient, r, has a , as it does not control for any additional variables when measuring the association between two variables. The coefficient rsy.z is a partial correlation coefficient, as it controls for the effect of one additional variable, Z.

Accepted Answer

A)  first- order; second- order 
B)  zero- order; second- order 
C)  first- order; third- order 
D)  zero- order; first- order 
A)  first- order; second- order 
B)  zero- order; second- order 
C)  first- order; third- order 
D)  zero- order; first- order

Question 17

The product moment correlation is also known as the Pearson correlation coefficient and as .

Accepted Answer

A)  correlation coefficient 
B)  simple correlation 
C)  bivariate correlation 
D)  all of the above 
A)  correlation coefficient 
B)  simple correlation 
C)  bivariate correlation 
D)  all of the above

Question 18

The general form of the multiple regression model is: Y = þ0 + þ1 X1 + þ2 X2 + þ3X3 + + þkXk + e

Accepted Answer

A) True 
 B)False

Question 19

The covariance may be either positive or negative.

Accepted Answer

A) True 
 B)False

Question 20

is a statistical procedure for analyzing associative relationships between a metric dependent variable and one or more independent variables.

Accepted Answer

A)  Product moment correlation 
B)  Partial correlation coefficient 
C)  ANOVA 
D)  Regression analysis 
A)  Product moment correlation 
B)  Partial correlation coefficient 
C)  ANOVA 
D)  Regression analysis

Exam 17: Correlation and Regression

Which statement is not true about regression analysis?

The regression equation for a categorical variable with four categories would be modeled as .

The estimated parameter b is usually referred to as the non- standardized regression coefficient.

R2 cannot decrease as more independent variables are added to the regression equation.

The coefficient of multiple determination is adjusted for the number of dependent variables and the sample size to account for diminishing returns.

Which of the following is not a problem associated with multicollinearity?

is the appropriate test statistic to use to determine the significance of the coefficient of determination in bivariate regression.

The question of "How strongly are sales related to advertising expenditures when the effect of price is controlled?" is best answered via .

The relationship between X and Y is spurious if .

is a state of very high intercorrelations among independent variables.

is best to use to determine how strongly sales are related to advertising expenditures.

The multiple correlation coefficient, R, can also be viewed as the simple correlation coefficient, r, between Y and Y^ .

Which statement about the correlation coefficient, r, is true?

In the absence of ties, Kendall's l yields a closer approximation to the Pearson product moment correlation coefficient, q, than Spearman's qs.

Because r indicates the degrees to which variation in one variable is related to variation in another, it can also be expressed in terms of the decomposition of the total variation.

The product moment correlation is also known as the Pearson correlation coefficient and as .

The general form of the multiple regression model is: Y = þ0 + þ1 X1 + þ2 X2 + þ3X3 + + þkXk + e

The covariance may be either positive or negative.

is a statistical procedure for analyzing associative relationships between a metric dependent variable and one or more independent variables.

Introduction to Marketing

Defining the Marketing Research

Research Design

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Measurement and Scaling

Measurement and Scaling

Questionnaire and Form

Sampling: Design and

Sampling: Final and Initial

Fieldwork

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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

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Filters

R²cannot decrease as more independent variables are added to the regression equation.

The multiple correlation coefficient, R, can also be viewed as the simple correlation coefficient, r, between Y and Y^{^}.

In the absence of ties, Kendall's l yields a closer approximation to the Pearson product moment correlation coefficient, q, than Spearman's q_s.

The general form of the multiple regression model is: Y = þ₀+ þ₁X₁+ þ₂X₂+ þ₃X₃+ + þ_kX_k₊e