Question 1

When determining the correlation coefficient, r, it does matter which variable is considered to be the dependent variable and which is the independent.

Accepted Answer

A) True 
 B)False

Question 2

A correlation matrix indicates the coefficient of correlation between each pair of variables.

Accepted Answer

A) True 
 B)False

Question 3

What is the bivariate regression equation if sample observations are used to predict Y?

Accepted Answer

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

Question 4

The relationship between X and Y is spurious if ________.

Accepted Answer

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

Question 5

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

Accepted Answer

A) True 
 B)False

Question 6

The general form of the multiple regression model is: Y = β0 + β1 X1 + β2 X2 + β3X3 + ....+ βkXk + e

Accepted Answer

A) True 
 B)False

Question 7

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

Accepted Answer

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

Question 8

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

Accepted Answer

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

Question 9

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

Question 10

Given the multiple regression equation, Ŷ = a + b1X1 + b2X2, and the bivariate equation Ŷ = 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 X₁ and X₂ are usually

Question 11

Which statement is not correct about the partial correlation coefficient?

Accepted Answer

A) Partial correlations can be helpful for detecting spurious relationships. 
B) The partial correlation coefficient is generally viewed as more important than the part correlation coefficient. 
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 can be calculated by a knowledge of the simple correlations alone. 
A) Partial correlations can be helpful for detecting spurious relationships. 
B) The partial correlation coefficient is generally viewed as more important than the part correlation coefficient. 
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 can be calculated by a knowledge of the simple correlations alone.

Question 12

In regression with dummy variables, the predicted Ŷ for each category is the mean of Y for each category.

Accepted Answer

A) True 
 B)False

Question 13

________ is a statistical technique that simultaneously develops a mathematical relationship between two or more independent variables and an interval-scaled dependent variable.

Accepted Answer

A) Chi-square 
B) The least-squares procedure 
C) Multiple regression 
D) Bivariate regression 
A) Chi-square 
B) The least-squares procedure 
C) Multiple regression 
D) Bivariate regression

Question 14

A technique for fitting a straight line to a scattergram by minimizing the square of the vertical distances of all the points from the line is known as the ________.

Accepted Answer

A) least-square procedure 
B) scatter diagram plot 
C) sum of square errors procedure 
D) maximum residual procedure 
A) least-square procedure 
B) scatter diagram plot 
C) sum of square errors procedure 
D) maximum residual procedure

Question 15

________ 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) T statistic 
C) Z statistic 
D) ω2  
A) F statistic 
B) T statistic 
C) Z statistic 
D) ω2

Question 16

________ variables may be used as predictors or independent variables by coding them as dummy variables.

Accepted Answer

A) Interval 
B) Categorical 
C) Ratio 
D) All of the above 
A) Interval 
B) Categorical 
C) Ratio 
D) All of the above

Question 17

When there are a large number of independent variables and the researcher suspects that not all of them are significant, stepwise regression should be used.

Accepted Answer

A) True 
 B)False

Question 18

The standard error of estimate, SEE, is the standard deviation of the actual Y values from the predicted Ŷ values.

Accepted Answer

A) True 
 B)False

Question 19

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

Accepted Answer

A) True 
 B)False

Question 20

The covariance may be either positive or negative.

Accepted Answer

A) True 
 B)False

Exam 17: Correlation and Regression

When determining the correlation coefficient, r, it does matter which variable is considered to be the dependent variable and which is the independent.

A correlation matrix indicates the coefficient of correlation between each pair of variables.

What is the bivariate regression equation if sample observations are used to predict Y?

The relationship between X and Y is spurious if ________.

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

The general form of the multiple regression model is: Y = β0 + β1 X1 + β2 X2 + β3X3 + ....+ βkXk + e

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

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

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

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

Which statement is not correct about the partial correlation coefficient?

In regression with dummy variables, the predicted Ŷ for each category is the mean of Y for each category.

________ is a statistical technique that simultaneously develops a mathematical relationship between two or more independent variables and an interval-scaled dependent variable.

A technique for fitting a straight line to a scattergram by minimizing the square of the vertical distances of all the points from the line is known as the ________.

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

________ variables may be used as predictors or independent variables by coding them as dummy variables.

When there are a large number of independent variables and the researcher suspects that not all of them are significant, stepwise regression should be used.

The standard error of estimate, SEE, is the standard deviation of the actual Y values from the predicted Ŷ values.

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

The covariance may be either positive or negative.

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

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

The general form of the multiple regression model is: Y= β₀+ β₁ X₁+β₂ X₂+β₃X₃+ ....+ β_kX_{k +}e

Given the multiple regression equation, Ŷ = a + b₁X₁+ b₂X_2,and the bivariate equation Ŷ = a + bX, why is the partial regression coefficient, b₁, different from the regression coefficient, b, obtained by regressing Y on only X₁?