Question 1

In multiple regression, the criterion variable is predicted by more than one independent variable.

Accepted Answer

A) True 
 B)False  True

Question 2

Given the following regression equation ( Ŷ  = 3.5 X 1 + 2 X 2 + 12), the coefficient for X 1 would mean that

Accepted Answer

A)  two people who differ by one point on X 1 would differ by 3.5 points on Ŷ . 
B)  two people who differ by one point on X 1 would differ by 3.5 points on Ŷ , assuming that they did not differ on X 2. 
C)  X 1 causes a 3.5 unit change in the dependent variable. 
D)  X 1 is more important than X 2. 
A)  two people who differ by one point on X 1 would differ by 3.5 points on Ŷ . 
B)  two people who differ by one point on X 1 would differ by 3.5 points on Ŷ , assuming that they did not differ on X 2. 
C)  X 1 causes a 3.5 unit change in the dependent variable. 
D)  X 1 is more important than X 2. B

Question 3

In an example in Chapter 10 we found that the relationship between how a student evaluated a course, and that student's expected grade was significant. In this chapter Grade was not a significant predictor. The difference is

Accepted Answer

A)  we had a new set of data. 
B)  grade did not predict significantly once the other predictors were taken into account. 
C)  the other predictors were correlated with grade. 
D)  both b and c 
A)  we had a new set of data. 
B)  grade did not predict significantly once the other predictors were taken into account. 
C)  the other predictors were correlated with grade. 
D)  both b and c D

Question 4

When testing null hypotheses about multiple regression we

Accepted Answer

A)  only look at the significance test on the overall multiple correlation. 
B)  have a separate significance test for each predictor and for overall significance. 
C)  don't have to worry about significance testing. 
D)  know that if one predictor is significant, the others won't be. 
A)  only look at the significance test on the overall multiple correlation. 
B)  have a separate significance test for each predictor and for overall significance. 
C)  don't have to worry about significance testing. 
D)  know that if one predictor is significant, the others won't be.

Question 5

If the overall analysis of variance is NOT significant

Accepted Answer

A)  we need to look particularly closely at the tests on the individual variables. 
B)  it probably doesn't make much sense to look at the individual variables. 
C)  the multiple correlation is too large to worry about. 
D)  none of the above 
A)  we need to look particularly closely at the tests on the individual variables. 
B)  it probably doesn't make much sense to look at the individual variables. 
C)  the multiple correlation is too large to worry about. 
D)  none of the above

Question 6

The example in the text predicting distress in cancer patients used distress at an earlier time as one of the predictors. This was done

Accepted Answer

A)  because the authors wanted to be able to report a large correlation. 
B)  because the authors wanted to see what effect earlier distress had. 
C)  because the authors wanted to look at the effects of self-blame after controlling for initial differences in distress. 
D)  because the authors didn't care about self-blame, but wanted to control for it. 
A)  because the authors wanted to be able to report a large correlation. 
B)  because the authors wanted to see what effect earlier distress had. 
C)  because the authors wanted to look at the effects of self-blame after controlling for initial differences in distress. 
D)  because the authors didn't care about self-blame, but wanted to control for it.

Question 7

Given the information in the following table, create the corresponding regression equation.

Accepted Answer

Cancer Anxiety = -.2

Question 8

Multiple regression examines the degree of association between any predictor and the criterion variable controlling for other predictors in the equation.

Accepted Answer

A) True 
 B)False

Question 9

Multicollinearity occurs when the predictor variables are highly correlated with one another.

Accepted Answer

A) True 
 B)False

Question 10

How do the regression results vary from the simple correlations presented below? 
Explain why this may be the case.

Accepted Answer

Difficult temperament is associated with

Question 11

A multiple regression analysis was used to test the values of visual acuity, swing power, and cost of clubs for predicting golf scores. The regression analysis showed that visual acuity and swing power predicted significant amounts of the variability in golf scores, but cost of clubs did not. What can be concluded from these results?

Accepted Answer

A)  Cost of clubs and golf scores are not correlated. 
B)  Cost of clubs adds predictive value above and beyond the predictive value of visual acuity and swing power. 
C)  The regression coefficient of cost of clubs is equal to zero. 
D)  Removing cost of clubs from the overall model will not reduce the model's R2 value significantly. 
A)  Cost of clubs and golf scores are not correlated. 
B)  Cost of clubs adds predictive value above and beyond the predictive value of visual acuity and swing power. 
C)  The regression coefficient of cost of clubs is equal to zero. 
D)  Removing cost of clubs from the overall model will not reduce the model's R2 value significantly.

Question 12

Multiple regression means there is more than one criterion variable.

Accepted Answer

A) True 
 B)False

Question 13

We want to predict a person's happiness from the following variables: degree of optimism, success in school, and number of close friends. What type of statistical test can tell us whether these variables predict a person's happiness?

Accepted Answer

A)  factorial ANOVA 
B)  multiple comparison 
C)  regression 
D)  multiple regression 
A)  factorial ANOVA 
B)  multiple comparison 
C)  regression 
D)  multiple regression

Question 14

If we have three predictors and they are all individually correlated with the dependent variable, we know that

Accepted Answer

A)  each of them will play a significant role in the regression equation. 
B)  each of them must be correlated with each other. 
C)  each regression coefficient will be significantly different from zero. 
D)  none of the above 
A)  each of them will play a significant role in the regression equation. 
B)  each of them must be correlated with each other. 
C)  each regression coefficient will be significantly different from zero. 
D)  none of the above

Question 15

Stepwise regression procedures capitalize on chance.

Accepted Answer

A) True 
 B)False

Question 16

In multiple regression an outlier is one that

Accepted Answer

A)  is reasonably close to the regression surface. 
B)  is far from the regression surface. 
C)  is extreme on at least one variable. 
D)  will necessarily influence the final result in an important way. 
A)  is reasonably close to the regression surface. 
B)  is far from the regression surface. 
C)  is extreme on at least one variable. 
D)  will necessarily influence the final result in an important way.

Question 17

Multiple regression allows you to examine the degree of association between individual independent variables and the criterion variable AND the degree of association between the set of independent variables and the criterion variable.

Accepted Answer

A) True 
 B)False

Question 18

If we want to compare the contribution of several predictors to the prediction of a dependent variable, we can get at least a rough idea by comparing

Accepted Answer

A)  the regression coefficients. 
B)  the standardized regression coefficients. 
C)  the variances of the several variables. 
D)  the simple Pearson correlations of each variable with the dependent variable. 
A)  the regression coefficients. 
B)  the standardized regression coefficients. 
C)  the variances of the several variables. 
D)  the simple Pearson correlations of each variable with the dependent variable.

Question 19

Before running a multiple regression, it is smart to look at the distribution of each variable. We do this because

Accepted Answer

A)  we want to see that the distributions are not very badly skewed. 
B)  we want to look for extreme scores. 
C)  we want to pick up obvious coding errors. 
D)  all of the above 
A)  we want to see that the distributions are not very badly skewed. 
B)  we want to look for extreme scores. 
C)  we want to pick up obvious coding errors. 
D)  all of the above

Question 20

In the previous question, a student who scored 0 on both X 1 and X 2 would be expected to have a dependent variable score of

Accepted Answer

A)  0. 
B)  3.5. 
C)  12. 
D)  the mean of Y . 
A)  0. 
B)  3.5. 
C)  12. 
D)  the mean of Y .

In multiple regression, the criterion variable is predicted by more than one independent variable.

Given the following regression equation ( Ŷ = 3.5 X ₁ + 2 X ₂ + 12), the coefficient for X ₁ would mean that

In an example in Chapter 10 we found that the relationship between how a student evaluated a course, and that student's expected grade was significant. In this chapter Grade was not a significant predictor. The difference is

When testing null hypotheses about multiple regression we

If the overall analysis of variance is NOT significant

The example in the text predicting distress in cancer patients used distress at an earlier time as one of the predictors. This was done

Given the information in the following table, create the corresponding regression equation.

Multiple regression examines the degree of association between any predictor and the criterion variable controlling for other predictors in the equation.

Multicollinearity occurs when the predictor variables are highly correlated with one another.

How do the regression results vary from the simple correlations presented below? Explain why this may be the case.

Multiple regression means there is more than one criterion variable.

We want to predict a person's happiness from the following variables: degree of optimism, success in school, and number of close friends. What type of statistical test can tell us whether these variables predict a person's happiness?

If we have three predictors and they are all individually correlated with the dependent variable, we know that

Stepwise regression procedures capitalize on chance.

In multiple regression an outlier is one that

Multiple regression allows you to examine the degree of association between individual independent variables and the criterion variable AND the degree of association between the set of independent variables and the criterion variable.

If we want to compare the contribution of several predictors to the prediction of a dependent variable, we can get at least a rough idea by comparing

Before running a multiple regression, it is smart to look at the distribution of each variable. We do this because

In the previous question, a student who scored 0 on both X ₁ and X ₂ would be expected to have a dependent variable score of

Introduction

Basic Concepts

Displaying Data

Measures of Central Tendency

Measures of Variability

The Normal Distribution

Basic Concepts of Probability

Sampling Distributions and Hypothesis Testing

Correlation

Regression

Hypothesis Tests Applied to Means: One Sample

Hypothesis Tests Applied to Means: Two Related Samples

Hypothesis Tests Applied to Means: Two Independent Samples

Power

One-Way Analysis of Variance

Factorial Analysis of Variance

Repeated-Measures Analysis of Variance

Chi-Square

Nonparametric and Resampling Statistical Tests

Meta-Analysis

Filters

Exam 11: Multiple Regression

In multiple regression, the criterion variable is predicted by more than one independent variable.

Given the following regression equation ( Ŷ = 3.5 X 1 + 2 X 2 + 12), the coefficient for X 1 would mean that

In an example in Chapter 10 we found that the relationship between how a student evaluated a course, and that student's expected grade was significant. In this chapter Grade was not a significant predictor. The difference is

When testing null hypotheses about multiple regression we

If the overall analysis of variance is NOT significant

The example in the text predicting distress in cancer patients used distress at an earlier time as one of the predictors. This was done

Given the information in the following table, create the corresponding regression equation.

Multiple regression examines the degree of association between any predictor and the criterion variable controlling for other predictors in the equation.

Multicollinearity occurs when the predictor variables are highly correlated with one another.

How do the regression results vary from the simple correlations presented below? Explain why this may be the case.

Multiple regression means there is more than one criterion variable.

We want to predict a person's happiness from the following variables: degree of optimism, success in school, and number of close friends. What type of statistical test can tell us whether these variables predict a person's happiness?

If we have three predictors and they are all individually correlated with the dependent variable, we know that

Stepwise regression procedures capitalize on chance.

In multiple regression an outlier is one that

Multiple regression allows you to examine the degree of association between individual independent variables and the criterion variable AND the degree of association between the set of independent variables and the criterion variable.

If we want to compare the contribution of several predictors to the prediction of a dependent variable, we can get at least a rough idea by comparing

Before running a multiple regression, it is smart to look at the distribution of each variable. We do this because

In the previous question, a student who scored 0 on both X 1 and X 2 would be expected to have a dependent variable score of

Introduction

Basic Concepts

Displaying Data

Measures of Central Tendency

Measures of Variability

The Normal Distribution

Basic Concepts of Probability

Sampling Distributions and Hypothesis Testing

Correlation

Regression

Hypothesis Tests Applied to Means: One Sample

Hypothesis Tests Applied to Means: Two Related Samples

Hypothesis Tests Applied to Means: Two Independent Samples

Power

One-Way Analysis of Variance

Factorial Analysis of Variance

Repeated-Measures Analysis of Variance

Chi-Square

Nonparametric and Resampling Statistical Tests

Meta-Analysis

Filters

Given the following regression equation ( Ŷ = 3.5 X ₁ + 2 X ₂ + 12), the coefficient for X ₁ would mean that

In the previous question, a student who scored 0 on both X ₁ and X ₂ would be expected to have a dependent variable score of