Question 1

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

Accepted Answer

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

Question 2

r = 0 indicates ________.

Accepted Answer

A) X and Y have a relationship 
B) X and Y don't have a linear relationship 
C) X and Y are unrelated 
D) X and Y have a linear relationship 
A) X and Y have a relationship 
B) X and Y don't have a linear relationship 
C) X and Y are unrelated 
D) X and Y have a linear relationship

Question 3

The ________ denotes the change in the predicted value, Ŷ, per unit change in X1 when the other independent variables, X2 to Xk, are held constant.

Accepted Answer

A) partial regression coefficient 
B) partial correlation coefficient 
C) part correlation coefficient 
D) part regression coefficient 
A) partial regression coefficient 
B) partial correlation coefficient 
C) part correlation coefficient 
D) part regression coefficient

Question 4

The standard error of estimate, SEE, may be interpreted as a kind of average residual or average error in predicting Y from the regression equation.

Accepted Answer

A) True 
 B)False

Question 5

The order associated with a partial correlation indicates how many variables are being adjusted or controlled.

Accepted Answer

A) True 
 B)False

Question 6

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

Accepted Answer

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

Question 7

Which statement is not true about regression analysis?

Accepted Answer

A) 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. 
B) Regression analysis can be used to determine if color preference is related to product size and price. 
C) Regression can be used to predict the values of the dependent variable. 
D) Regression analysis is a powerful and flexible procedure for analyzing associative relationships between a metric dependent variable and one or more independent variables. 
A) 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. 
B) Regression analysis can be used to determine if color preference is related to product size and price. 
C) Regression can be used to predict the values of the dependent variable. 
D) Regression analysis is a powerful and flexible procedure for analyzing associative relationships between a metric dependent variable and one or more independent variables.

Question 8

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

Accepted Answer

A) Ŷ = a + b1X1 + b2X2  
B) Y = β0 + β1 Xi  
C) Yi =β0 + β1 Xi + ei  
D) Ŷ i = a + bXi 
A) Ŷ = a + b1X1 + b2X2  
B) Y = β0 + β1 Xi  
C) Yi =β0 + β1 Xi + ei  
D) Ŷ i = a + bXi

Question 9

If a variable explains a significant proportion of the residual variation, it should be considered for inclusion in the regression equation.

Accepted Answer

A) True 
 B)False

Question 10

The standard deviation of b, or the standard error, is denoted as ________.

Accepted Answer

A) SEb  
B) SDb  
C) SSYb  
D) none of the above 
A) SEb  
B) SDb  
C) SSYb  
D) none of the above

Question 11

Multicollinearity arises when intercorrelations among the predictors are very low.

Accepted Answer

A) True 
 B)False

Question 12

In what ways can regression analysis be used?

Accepted Answer

1.Determine whether the independent vari

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

r = 0 indicates ________.

The ________ denotes the change in the predicted value, Ŷ, per unit change in X₁ when the other independent variables, X₂ to X_k_, are held constant.

The standard error of estimate, SEE, may be interpreted as a kind of average residual or average error in predicting Y from the regression equation.

The order associated with a partial correlation indicates how many variables are being adjusted or controlled.

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

Which statement is not true about regression analysis?

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

If a variable explains a significant proportion of the residual variation, it should be considered for inclusion in the regression equation.

The standard deviation of b, or the standard error, is denoted as ________.

Multicollinearity arises when intercorrelations among the predictors are very low.

In what ways can regression analysis be used?

Introduction to Marketing Research

Defining the Marketing Research Problem and Developing an Approach

Research Design

Exploratory Research Design: Secondary Data

Exploratory Research Design: Qualitative Research

Descriptive Research Design: Survey and Observation

Causal Research Design: Experimentation

Measurement and Scaling: Fundamentals and Comparative Scaling

Measurement and Scaling: Noncomparative Scaling Techniques

Questionnaire and Form Design

Sampling: Design and Procedures

Sampling: Final and Initial Sample Size Determination

Fieldwork

Data Preparation

Frequency Distribution, Cross-Tabulation, and Hypothesis Testing

Analysis of Variance and Covariance

Discriminant and Logit Analysis

Factor Analysis

Cluster Analysis

Multidimensional Scaling and Conjoint Analysis

Structural Equation Modeling and Path Analysis

Report Preparation and Presentation

International Marketing Research

Filters

Exam 17: Correlation and Regression

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

r = 0 indicates ________.

The ________ denotes the change in the predicted value, Ŷ, per unit change in X1 when the other independent variables, X2 to Xk, are held constant.

The standard error of estimate, SEE, may be interpreted as a kind of average residual or average error in predicting Y from the regression equation.

The order associated with a partial correlation indicates how many variables are being adjusted or controlled.

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

Which statement is not true about regression analysis?

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

If a variable explains a significant proportion of the residual variation, it should be considered for inclusion in the regression equation.

The standard deviation of b, or the standard error, is denoted as ________.

Multicollinearity arises when intercorrelations among the predictors are very low.

In what ways can regression analysis be used?

Introduction to Marketing Research

Defining the Marketing Research Problem and Developing an Approach

Research Design

Exploratory Research Design: Secondary Data

Exploratory Research Design: Qualitative Research

Descriptive Research Design: Survey and Observation

Causal Research Design: Experimentation

Measurement and Scaling: Fundamentals and Comparative Scaling

Measurement and Scaling: Noncomparative Scaling Techniques

Questionnaire and Form Design

Sampling: Design and Procedures

Sampling: Final and Initial Sample Size Determination

Fieldwork

Data Preparation

Frequency Distribution, Cross-Tabulation, and Hypothesis Testing

Analysis of Variance and Covariance

Discriminant and Logit Analysis

Factor Analysis

Cluster Analysis

Multidimensional Scaling and Conjoint Analysis

Structural Equation Modeling and Path Analysis

Report Preparation and Presentation

International Marketing Research

Filters

The ________ denotes the change in the predicted value, Ŷ, per unit change in X₁ when the other independent variables, X₂ to X_k_, are held constant.