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Home  arrow Multiple choice questions  arrow Part 5 - More advanced correctional statistics  arrow Chapter 36 - Hierarchical multiple regression

Chapter 36 - Hierarchical multiple regression

 
Attempt these questions after you have read chapters 32, 33 and 35

Try the multiple choice questions below to test your knowledge of this chapter. Once you have completed the test, click on 'Submit Answers for Grading' to get your results.

Please refer to the following outputs when answering the questions

Variables Entered/Removed

Model

Variables Entered

Variables Removed

Method

1

Gender, Age,
Car value,
Years driving

.

Enter

a. All requested variables entered.

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.563a

.317

.313

.989

a. Predictors: (Constant), Gender, Age, Car value, Years driving

ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

306.947

4

76.737

78.501

.000a

Residual

662.766

678

.978

 

 

Total

969.713

682

 

 

 

a. Predictors: (Constant), Gender, Age, Car value, Years driving

b. Dependent Variable: Risk

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

2.347

.247

 

9.495

.000

Years driving

-.131

.026

-.192

-5.130

.000

Car value

.604

.041

.574

14.830

.000

Age

.081

.038

.078

2.097

.036

Gender

.142

.080

.058

1.778

.076

a. Dependent Variable: Risk



This activity contains 10 questions.

Question 1.
Multiple regression:

 
End of Question 1


Question 2.
Multiple regression can be used to:

 
End of Question 2


Question 3.
The multiple regression equation consists of:

 
End of Question 3


Question 4.
The standardised partial regression coefficient varies between:

 
End of Question 4


Question 5.
The number of regression equations with four predictors is:

 
End of Question 5


Question 6.
An insurance company analyses the risk of a car driver having an accident. How much of the variance in the risk ratings can be accounted for by the independent variables used?

 
End of Question 6


Question 7.
An insurance company analyses the risk of a car driver having an accident. How many predictors have they used?

 
End of Question 7


Question 8.
An insurance company analyses the risk of a car driver having an accident. Can these predictors account for a significant amount of the variance in the risk ratings?

 
End of Question 8


Question 9.
An insurance company analyses the risk of a car driver having an accident. Where does the regression line cross the y axis?

 
End of Question 9


Question 10.
An insurance company analyses the risk of a car driver having an accident. Which predictor has the largest unstandardised weight?

 
End of Question 10





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