Question 1

Which form of regression is useful when the researcher has many independent variables and wants to narrow the set down to a smaller number?

Accepted Answer

A)  multiple component reduction 
B)  stepwise multiple regression 
C)  variance deflation regression 
D)  variance inflation regression 
E)  narrow regression 
A)  multiple component reduction 
B)  stepwise multiple regression 
C)  variance deflation regression 
D)  variance inflation regression 
E)  narrow regression

Question 2

We can sometimes improve a regression analysis finding by removing outliers and rerunning the regression analysis.

Accepted Answer

A) True 
 B)False

Question 3

Sometimes a researcher will find that the ANOVA F is not significant in regression analysis or if the F is significant, the R square is lower than desired. It is appropriate in these cases to:

Accepted Answer

A)  examine the data using another stat package other than SPSS 
B)  change the scaling assumptions from ratio or interval to ordinal and rerun the analysis 
C)  run a confidence interval around the predicted values and then make the interval narrower 
D)  run a confidence interval around the predicted values and then make the interval wider 
E)  run a scatter diagram, search for outliers, and remove them and rerun the regression 
A)  examine the data using another stat package other than SPSS 
B)  change the scaling assumptions from ratio or interval to ordinal and rerun the analysis 
C)  run a confidence interval around the predicted values and then make the interval narrower 
D)  run a confidence interval around the predicted values and then make the interval wider 
E)  run a scatter diagram, search for outliers, and remove them and rerun the regression

Question 4

What criterion is used to establish the best "fit" of a straight line through the points on a scatter diagram?

Accepted Answer

A)  the plum line criterion 
B)  the least squares criterion 
C)  the bearing line criterion 
D)  the b slope criterion 
E)  the right angle criterion 
A)  the plum line criterion 
B)  the least squares criterion 
C)  the bearing line criterion 
D)  the b slope criterion 
E)  the right angle criterion

Question 5

Which statistic is used to determine whether or not multicollinearity is a concern in multiple regression?

Accepted Answer

A)  coefficient of determination 
B)  multicol Z 
C)  multicol R 
D)  VIF (variance inflation factor) 
E)  Q 
A)  coefficient of determination 
B)  multicol Z 
C)  multicol R 
D)  VIF (variance inflation factor) 
E)  Q

Question 6

A regression line using the "least squares criterion" will result in high residuals.

Accepted Answer

A) True 
 B)False

Question 7

When you compare how far the predicted values are from the actual or observed values, you are performing:

Accepted Answer

A)  analysis of prediction 
B)  control 
C)  analysis of residuals 
D)  analysis of variance 
E)  analysis of values 
A)  analysis of prediction 
B)  control 
C)  analysis of residuals 
D)  analysis of variance 
E)  analysis of values

Question 8

In bivariate regression, if the F value is not significant (say .051), then:

Accepted Answer

A)  we accept the null hypothesis that a straight-line model fits our data 
B)  we reject the null hypothesis that a straight-line model does not fit our data 
C)  we abandon our efforts to analyze the two variables 
D)  check for outliers 
E)  rerun the regression 
A)  we accept the null hypothesis that a straight-line model fits our data 
B)  we reject the null hypothesis that a straight-line model does not fit our data 
C)  we abandon our efforts to analyze the two variables 
D)  check for outliers 
E)  rerun the regression

Question 9

The two ways of making a prediction are extension analysis and astrological modeling.

Accepted Answer

A) True 
 B)False

Question 10

A good way to assess a predictive model's predictive accuracy is through:

Accepted Answer

A)  measuring its predictive accuracy 
B)  measuring its analysis of residuals 
C)  measuring its analysis of variance 
D)  measuring its reliability 
E)  measuring its consistency 
A)  measuring its predictive accuracy 
B)  measuring its analysis of residuals 
C)  measuring its analysis of variance 
D)  measuring its reliability 
E)  measuring its consistency

Question 11

You run bivariate regression analysis and you find that the ANOVA results indicate that you have Sig. value for your F of .05. Now, looking under your Coefficients output, you have an intercept value and a slope value. You should use these values only when:

Accepted Answer

A)  they are both significant, i.e., have Sig. values above 1.00 
B)  they are both significant, i.e., have Sig. values below 1.00 
C)  they are both significant, i.e., have Sig. values equal to or below .05 
D)  when at least one is significant, i.e., has a Sig. value above .80. 
E)  None of the above; because the F value in the ANOVA was not significant, you should not have even looked at the Coefficients output. 
A)  they are both significant, i.e., have Sig. values above 1.00 
B)  they are both significant, i.e., have Sig. values below 1.00 
C)  they are both significant, i.e., have Sig. values equal to or below .05 
D)  when at least one is significant, i.e., has a Sig. value above .80. 
E)  None of the above; because the F value in the ANOVA was not significant, you should not have even looked at the Coefficients output.

Question 12

The "goodness" of a prediction means its reliability.

Accepted Answer

A) True 
 B)False

Question 13

A graph of the dependent variable in multiple regression analysis is referred to as:

Accepted Answer

A)  confidence intervals 
B)  multiple regression 
C)  multiple scatter plots 
D)  regression plane 
E)  a multi-scatter plot 
A)  confidence intervals 
B)  multiple regression 
C)  multiple scatter plots 
D)  regression plane 
E)  a multi-scatter plot

Question 14

In regression, the line that runs through the points on a scatter diagram is positioned to minimize the vertical distances away from the line of the various points because of the "least squares criterion."

Accepted Answer

A) True 
 B)False

Question 15

In the formula for bivariate regression analysis, the change in y for each one-unit change in x is known as the slope.

Accepted Answer

A) True 
 B)False

Question 16

When we want to use one variable to predict another and use the equation: y = a + bx, we use the technique known as multiple regression.

Accepted Answer

A) True 
 B)False

Question 17

In dummy coding, the 0-versus-1 code is traditional, but any two adjacent numbers could be used, such as 1 versus 2.

Accepted Answer

A) True 
 B)False

Question 18

A weather forecaster studies relationships among phenomena such as wind direction, barometric pressure, humidity, jet stream flow, and temperature. Based upon his or her knowledge of the relationships between these variables and the weather, the forecaster predicts there is an 80 percent chance of rain tomorrow. Which method of prediction have you used?

Accepted Answer

A)  guessing 
B)  statistics 
C)  extrapolation 
D)  predictive model 
E)  forecasting 
A)  guessing 
B)  statistics 
C)  extrapolation 
D)  predictive model 
E)  forecasting

Question 19

Multicollinearity refers to correlations among the dependent variables and makes predictions much more accurate because predicting one variable also allows you to predict the correlated variable(s).

Accepted Answer

A) True 
 B)False

Question 20

If the ANOVA F test is not significant in bivariate regression analysis, we must trim the model by eliminating the insignificant dependent variable(s).

Accepted Answer

A) True 
 B)False

Which form of regression is useful when the researcher has many independent variables and wants to narrow the set down to a smaller number?

We can sometimes improve a regression analysis finding by removing outliers and rerunning the regression analysis.

Sometimes a researcher will find that the ANOVA F is not significant in regression analysis or if the F is significant, the R square is lower than desired. It is appropriate in these cases to:

What criterion is used to establish the best "fit" of a straight line through the points on a scatter diagram?

Which statistic is used to determine whether or not multicollinearity is a concern in multiple regression?

A regression line using the "least squares criterion" will result in high residuals.

When you compare how far the predicted values are from the actual or observed values, you are performing:

In bivariate regression, if the F value is not significant (say .051), then:

The two ways of making a prediction are extension analysis and astrological modeling.

A good way to assess a predictive model's predictive accuracy is through:

You run bivariate regression analysis and you find that the ANOVA results indicate that you have Sig. value for your F of .05. Now, looking under your Coefficients output, you have an intercept value and a slope value. You should use these values only when:

The "goodness" of a prediction means its reliability.

A graph of the dependent variable in multiple regression analysis is referred to as:

In regression, the line that runs through the points on a scatter diagram is positioned to minimize the vertical distances away from the line of the various points because of the "least squares criterion."

In the formula for bivariate regression analysis, the change in y for each one-unit change in x is known as the slope.

When we want to use one variable to predict another and use the equation: y = a + bx, we use the technique known as multiple regression.

In dummy coding, the 0-versus-1 code is traditional, but any two adjacent numbers could be used, such as 1 versus 2.

Multicollinearity refers to correlations among the dependent variables and makes predictions much more accurate because predicting one variable also allows you to predict the correlated variable(s).

If the ANOVA F test is not significant in bivariate regression analysis, we must trim the model by eliminating the insignificant dependent variable(s).

Introduction to Marketing Research

The Marketing Research Process

The Marketing Research Industry

Defining the Problem and Determining Research Objectives

Research Design

Using Secondary Data and Online Information Databases

Standardized Information Sources

Observation, Focus Groups, and Other Qualitative Methods

Survey Data-Collection Methods

Measurement in Marketing Research

Designing the Questionnaire

Determining How to Select the Sample

Determining the Size of a Sample

Data Collection in the Field, Nonresponse Error, and Questionnaire Screening

Basic Data Analysis: Descriptive Statistics

Generalizing a Sample's Findings to its Population and Testing Hypotheses About Percents and Means

Testing for Differences Between Two Groups or Among More Than

Determining and Interpreting Associations Among Variables

The Marketing Research Report: Preparation and Presentation

Filters

Exam 19: Regression Analysis in Marketing Research

Which form of regression is useful when the researcher has many independent variables and wants to narrow the set down to a smaller number?

We can sometimes improve a regression analysis finding by removing outliers and rerunning the regression analysis.

Sometimes a researcher will find that the ANOVA F is not significant in regression analysis or if the F is significant, the R square is lower than desired. It is appropriate in these cases to:

What criterion is used to establish the best "fit" of a straight line through the points on a scatter diagram?

Which statistic is used to determine whether or not multicollinearity is a concern in multiple regression?

A regression line using the "least squares criterion" will result in high residuals.

When you compare how far the predicted values are from the actual or observed values, you are performing:

In bivariate regression, if the F value is not significant (say .051), then:

The two ways of making a prediction are extension analysis and astrological modeling.

A good way to assess a predictive model's predictive accuracy is through:

You run bivariate regression analysis and you find that the ANOVA results indicate that you have Sig. value for your F of .05. Now, looking under your Coefficients output, you have an intercept value and a slope value. You should use these values only when:

The "goodness" of a prediction means its reliability.

A graph of the dependent variable in multiple regression analysis is referred to as:

In regression, the line that runs through the points on a scatter diagram is positioned to minimize the vertical distances away from the line of the various points because of the "least squares criterion."

In the formula for bivariate regression analysis, the change in y for each one-unit change in x is known as the slope.

When we want to use one variable to predict another and use the equation: y = a + bx, we use the technique known as multiple regression.

In dummy coding, the 0-versus-1 code is traditional, but any two adjacent numbers could be used, such as 1 versus 2.

Multicollinearity refers to correlations among the dependent variables and makes predictions much more accurate because predicting one variable also allows you to predict the correlated variable(s).

If the ANOVA F test is not significant in bivariate regression analysis, we must trim the model by eliminating the insignificant dependent variable(s).

Introduction to Marketing Research

The Marketing Research Process

The Marketing Research Industry

Defining the Problem and Determining Research Objectives

Research Design

Using Secondary Data and Online Information Databases

Standardized Information Sources

Observation, Focus Groups, and Other Qualitative Methods

Survey Data-Collection Methods

Measurement in Marketing Research

Designing the Questionnaire

Determining How to Select the Sample

Determining the Size of a Sample

Data Collection in the Field, Nonresponse Error, and Questionnaire Screening

Basic Data Analysis: Descriptive Statistics

Generalizing a Sample's Findings to its Population and Testing Hypotheses About Percents and Means

Testing for Differences Between Two Groups or Among More Than

Determining and Interpreting Associations Among Variables

The Marketing Research Report: Preparation and Presentation

Filters