Question 1

Stepwise procedures result in regression equations that are optimal, in the sense of producing the largest R2, for a given number of predictors.

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

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 4

The product moment correlation, r, is the most widely used statistic summarizing the strength of association between two metric (interval or ordinal scaled) variables, say X and Y.

Accepted Answer

A) True 
 B)False

Question 5

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

Question 6

is a regression procedure in which the predictor variables enter or leave the regression equation one at a time.

Accepted Answer

A)  Dummy variable regression 
B)  Multiple regression 
C)  Stepwise regression 
D)  Bivariate regression 
A)  Dummy variable regression 
B)  Multiple regression 
C)  Stepwise regression 
D)  Bivariate regression

Question 7

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

Accepted Answer

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

Question 8

A residual is the difference between the observed value of Yi and the value predicted by the regression equation, Y^ i.

Accepted Answer

A) True 
 B)False

Question 9

In bivariate regression, the null hypothesis is that no linear relationship exists between X and Y, or H0: fi1 = 0.

Accepted Answer

A) True 
 B)False

Question 10

In multiple regression, if the overall null hypothesis is rejected, which statement is true?

Accepted Answer

A)  We know which specific þs are nonzero. 
B)  We can use t = b/SEb to determine which þs are nonzero. 
C)  We do not know which þs are nonzero. 
D)  Both B and C are correct. 
A)  We know which specific þs are nonzero. 
B)  We can use t = b/SEb to determine which þs are nonzero. 
C)  We do not know which þs are nonzero. 
D)  Both B and C are correct.

Question 11

The general form of the multiple regression model is estimated by which equation?

Accepted Answer

A)  Y^ i = a + bXi  
B)  Y^ i = þ0 + þ1 Xi + ei 
C)  Y^ = a + b1X1 + b2X2  
D)  Y^ = a + b1 X1 + b2 X2 + b3X3 + ... + bkXk 
A)  Y^ i = a + bXi  
B)  Y^ i = þ0 + þ1 Xi + ei 
C)  Y^ = a + b1X1 + b2X2  
D)  Y^ = a + b1 X1 + b2 X2 + b3X3 + ... + bkXk

Stepwise procedures result in regression equations that are optimal, in the sense of producing the largest R², for a given number of predictors.

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

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 product moment correlation, r, is the most widely used statistic summarizing the strength of association between two metric (interval or ordinal scaled) variables, say X and Y.

r = 0 indicates .

is a regression procedure in which the predictor variables enter or leave the regression equation one at a time.

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

A residual is the difference between the observed value of Y_iand the value predicted by the regression equation, Y^{^}_i.

In bivariate regression, the null hypothesis is that no linear relationship exists between X and Y, or H₀: fi₁= 0.

In multiple regression, if the overall null hypothesis is rejected, which statement is true?

The general form of the multiple regression model is estimated by which equation?

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

Exam 17: Correlation and Regression

Stepwise procedures result in regression equations that are optimal, in the sense of producing the largest R2, for a given number of predictors.

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

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 product moment correlation, r, is the most widely used statistic summarizing the strength of association between two metric (interval or ordinal scaled) variables, say X and Y.

r = 0 indicates .

is a regression procedure in which the predictor variables enter or leave the regression equation one at a time.

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

A residual is the difference between the observed value of Yi and the value predicted by the regression equation, Y^ i.

In bivariate regression, the null hypothesis is that no linear relationship exists between X and Y, or H0: fi1 = 0.

In multiple regression, if the overall null hypothesis is rejected, which statement is true?

The general form of the multiple regression model is estimated by which equation?

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

Stepwise procedures result in regression equations that are optimal, in the sense of producing the largest R², for a given number of predictors.

A residual is the difference between the observed value of Y_iand the value predicted by the regression equation, Y^{^}_i.

In bivariate regression, the null hypothesis is that no linear relationship exists between X and Y, or H₀: fi₁= 0.