Question 1

In multiple regression the intercept is usually denoted as

Accepted Answer

A)  a 
B)  b1 
C)  b0 
D)  0 
A)  a 
B)  b1 
C)  b0 
D)  0

Question 2

Individual predictors cannot be individually associated with the criterion variable if R is not different from 0 (i.e., if the entire model is not significant).

Accepted Answer

A) True 
 B)False

Question 3

Any association that was significant as a simple correlation will be significant in a multiple regression equation predicting the same criterion variable.

Accepted Answer

A) True 
 B)False

Question 4

Are the set of predictors significantly associated with maternal sensitivity?

Accepted Answer

The answer of Are the set of predictors significantly associated...

Question 5

Assume that we generated a prediction just by adding together the number of stressful events you report experiencing over the last month, the number of close friends you have, and your score on a measure assessing how much control you feel you have over events in your life (i.e., prediction = stress + friends + control). The regression coefficient for stressful events would be

Accepted Answer

A)  1.0 
B)  4.0 
C)  0.0 
D)  There is no way to know. 
A)  1.0 
B)  4.0 
C)  0.0 
D)  There is no way to know.

Question 6

If one independent variable has a larger coefficient than another, this means

Accepted Answer

A)  that the variable with the larger coefficient is a more important predictor. 
B)  that the variable with the larger coefficient is a more statistically significant predictor. 
C)  that the variable with the larger coefficient contributes more to predicting the variability in the criterion. 
D)  We can't say anything about relative importance or significance from what is given here. 
A)  that the variable with the larger coefficient is a more important predictor. 
B)  that the variable with the larger coefficient is a more statistically significant predictor. 
C)  that the variable with the larger coefficient contributes more to predicting the variability in the criterion. 
D)  We can't say anything about relative importance or significance from what is given here.

Question 7

The example in Chapter 11 of predicting weight from height and sex showed that

Accepted Answer

A)  adding sex as a predictor accounted for an important source of variability. 
B)  there is a much stronger relationship between height and weight in males than in females. 
C)  sex is not a useful predictor in this situation. 
D)  we cannot predict very well, even with two predictors. 
A)  adding sex as a predictor accounted for an important source of variability. 
B)  there is a much stronger relationship between height and weight in males than in females. 
C)  sex is not a useful predictor in this situation. 
D)  we cannot predict very well, even with two predictors.

Question 8

If our regression equation is Ŷ = 0.75 × age 0.50 × experience - 0.10 × grade point average - 2.0, and if our first subject had scores of 16, 4, and 3.0 on those three variables, respectively, then that subject's predicted score would be

Accepted Answer

A)  11.7 
B)  10 
C)  16 
D)  -3 
A)  11.7 
B)  10 
C)  16 
D)  -3

Question 9

Multiple regression analysis yielded the following regression equation:
Predicted Happiness = .36 × friends - .13 × s tress + 1.23
Which of the following is true?

Accepted Answer

A)  Happiness increases as Friends increase. 
B)  Happiness increases as Stress increases. 
C)  Happiness decreases as Friends and Stress increase. 
D)  none of the above 
A)  Happiness increases as Friends increase. 
B)  Happiness increases as Stress increases. 
C)  Happiness decreases as Friends and Stress increase. 
D)  none of the above

Question 10

In the previous question the intercept would be

Accepted Answer

A)  1.0 
B)  0.0 
C)  3.0 
D)  There would be no way to know. 
A)  1.0 
B)  0.0 
C)  3.0 
D)  There would be no way to know.

Question 11

The multiple correlation of several variables with a dependent variable is

Accepted Answer

A)  less than the largest individual correlation. 
B)  equal to the correlation of the dependent variable to the values predicted by the regression equation. 
C)  noticeably less than the correlation of the dependent variable to the values predicted by the regression equation. 
D)  It could take on any value. 
A)  less than the largest individual correlation. 
B)  equal to the correlation of the dependent variable to the values predicted by the regression equation. 
C)  noticeably less than the correlation of the dependent variable to the values predicted by the regression equation. 
D)  It could take on any value.

Question 12

Many of the procedures for finding an optimal regression equation (whatever that means) are known as

Accepted Answer

A)  hunting procedures. 
B)  trial and error procedures. 
C)  trialwise procedures. 
D)  stepwise procedures. 
A)  hunting procedures. 
B)  trial and error procedures. 
C)  trialwise procedures. 
D)  stepwise procedures.

Question 13

Suppose that in the previous question another subject had a predicted score of 10.3, and actually obtained a score of 12.4. For this subject the residual score would be

Accepted Answer

A)  2.1 
B)  -0.7 
C)  12.4 
D)  0.0 
A)  2.1 
B)  -0.7 
C)  12.4 
D)  0.0

Question 14

If we find all of the residuals when predicting our obtained values of Y from the regression equation, the sum of squared residuals would be expected to be _______ the sum of the squared residuals for a new set of data.

Accepted Answer

A)  less than 
B)  greater than 
C)  the same as 
D)  We can't tell. 
A)  less than 
B)  greater than 
C)  the same as 
D)  We can't tell.

Question 15

The text generally recommended against formal procedures for finding an optimal regression procedure because

Accepted Answer

A)  those procedures don't work. 
B)  those procedures pay too much attention to chance differences. 
C)  the statistical software won't handle those procedures. 
D)  all of the above 
A)  those procedures don't work. 
B)  those procedures pay too much attention to chance differences. 
C)  the statistical software won't handle those procedures. 
D)  all of the above

Question 16

If you drop a predictor from the regression equation

Accepted Answer

A)  the correlation could increase. 
B)  the correlation will probably go down. 
C)  the correlation could stay the same. 
D)  both b and c 
A)  the correlation could increase. 
B)  the correlation will probably go down. 
C)  the correlation could stay the same. 
D)  both b and c

Question 17

The following regression equation was found for a sample of college students. predicted happiness = 32.8 GPA + 17.3 × pocket money + 7.4
Which of the following can be concluded?

Accepted Answer

A)  The correlation between pocket money and happiness is larger than the correlation between GPA and happiness. 
B)  GPA is less useful than pocket money in predicting happiness. 
C)  For students with no pocket money, a one-unit increase in GPA will increase the value of predicted happiness by 32.8 units. 
D)  The r-squared value for GPA must be greater than the r-squared value for pocket money. 
A)  The correlation between pocket money and happiness is larger than the correlation between GPA and happiness. 
B)  GPA is less useful than pocket money in predicting happiness. 
C)  For students with no pocket money, a one-unit increase in GPA will increase the value of predicted happiness by 32.8 units. 
D)  The r-squared value for GPA must be greater than the r-squared value for pocket money.

Question 18

Write a sentence explaining the analysis presented in the following table (i.e., what are the predictor variables, what is the criterion variable).

Accepted Answer

In this example, maternal sens

Question 19

The Analysis of Variance section in computer results for multiple regression

Accepted Answer

A)  compares the means of several variables. 
B)  tests the overall significance of the regression. 
C)  tests the significance of each predictor. 
D)  compares the variances of the variables. 
A)  compares the means of several variables. 
B)  tests the overall significance of the regression. 
C)  tests the significance of each predictor. 
D)  compares the variances of the variables.

Question 20

If two variables are each correlated significantly with the dependent variable, then the multiple correlation will be

Accepted Answer

A)  the sum of the two correlations. 
B)  the sum of the two correlations squared. 
C)  no less than the larger of the two individual correlations. 
D)  It could take on any value. 
A)  the sum of the two correlations. 
B)  the sum of the two correlations squared. 
C)  no less than the larger of the two individual correlations. 
D)  It could take on any value.

In multiple regression the intercept is usually denoted as

Individual predictors cannot be individually associated with the criterion variable if R is not different from 0 (i.e., if the entire model is not significant).

Any association that was significant as a simple correlation will be significant in a multiple regression equation predicting the same criterion variable.

Are the set of predictors significantly associated with maternal sensitivity?

If one independent variable has a larger coefficient than another, this means

The example in Chapter 11 of predicting weight from height and sex showed that

If our regression equation is Ŷ = 0.75 × age 0.50 × experience - 0.10 × grade point average - 2.0, and if our first subject had scores of 16, 4, and 3.0 on those three variables, respectively, then that subject's predicted score would be

Multiple regression analysis yielded the following regression equation: Predicted Happiness = .36 × friends - .13 × s tress + 1.23 Which of the following is true?

In the previous question the intercept would be

The multiple correlation of several variables with a dependent variable is

Many of the procedures for finding an optimal regression equation (whatever that means) are known as

Suppose that in the previous question another subject had a predicted score of 10.3, and actually obtained a score of 12.4. For this subject the residual score would be

If we find all of the residuals when predicting our obtained values of Y from the regression equation, the sum of squared residuals would be expected to be _______ the sum of the squared residuals for a new set of data.

The text generally recommended against formal procedures for finding an optimal regression procedure because

If you drop a predictor from the regression equation

The following regression equation was found for a sample of college students. predicted happiness = 32.8 GPA + 17.3 × pocket money + 7.4 Which of the following can be concluded?

Write a sentence explaining the analysis presented in the following table (i.e., what are the predictor variables, what is the criterion variable).

The Analysis of Variance section in computer results for multiple regression

If two variables are each correlated significantly with the dependent variable, then the multiple correlation will be

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 intercept is usually denoted as

Individual predictors cannot be individually associated with the criterion variable if R is not different from 0 (i.e., if the entire model is not significant).

Any association that was significant as a simple correlation will be significant in a multiple regression equation predicting the same criterion variable.

Are the set of predictors significantly associated with maternal sensitivity?

If one independent variable has a larger coefficient than another, this means

The example in Chapter 11 of predicting weight from height and sex showed that

If our regression equation is Ŷ = 0.75 × age 0.50 × experience - 0.10 × grade point average - 2.0, and if our first subject had scores of 16, 4, and 3.0 on those three variables, respectively, then that subject's predicted score would be

Multiple regression analysis yielded the following regression equation: Predicted Happiness = .36 × friends - .13 × s tress + 1.23 Which of the following is true?

In the previous question the intercept would be

The multiple correlation of several variables with a dependent variable is

Many of the procedures for finding an optimal regression equation (whatever that means) are known as

Suppose that in the previous question another subject had a predicted score of 10.3, and actually obtained a score of 12.4. For this subject the residual score would be

If we find all of the residuals when predicting our obtained values of Y from the regression equation, the sum of squared residuals would be expected to be _______ the sum of the squared residuals for a new set of data.

The text generally recommended against formal procedures for finding an optimal regression procedure because

If you drop a predictor from the regression equation

The following regression equation was found for a sample of college students. predicted happiness = 32.8 GPA + 17.3 × pocket money + 7.4 Which of the following can be concluded?

Write a sentence explaining the analysis presented in the following table (i.e., what are the predictor variables, what is the criterion variable).

The Analysis of Variance section in computer results for multiple regression

If two variables are each correlated significantly with the dependent variable, then the multiple correlation will be

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