Ch.16 Multiple Regression Complete Test Bank

Chapter 16: Multiple regression

Test Bank

Type: multiple choice question

Title: Chapter 16 Question 1

1) What does a β = -.17 indicate?

a. That a one point increase in our predictor is associated with a .17 decrease in our outcome

b. That a one point increase in our predictor is associated with a .17 increase in our outcome

c. That the finding is not significant

d. How much variance is explained by the predictor

Type: multiple choice question

Title: Chapter 16 Question 2

2) In a regression, what does the R² statistic tell us?

a. Whether the model is significant

b. The relationship that the predictors have with the outcome variable

c. If there is evidence of multicollinearity

d. The amount of variance explained by the model

Type: multiple choice question

Title: Chapter 16 Question 3

3) Multicollinearity is when…

a. there are very strong relationships between our predictor variables.

b. there are very strong relationships between our predictor variables and the outcome variable.

c. there are very weak relationships between our predictor variables.

d. there are very weak relationships between our predictor variables and the outcome variable.

Type: multiple choice question

Title: Chapter 16 Question 4

4) How many outliers are acceptable in a regression model?

a. None

b. Up to 5%

c. Up to 10%

d. Up to 1%

Type: multiple choice question

Title: Chapter 16 Question 5

05) If our histogram of residuals shows a positive skew, then this means that the model is…

a. overestimating.

b. underestimating.

c. neither over or underestimating.

d. a good predictive model.

Type: multiple choice question

Title: Chapter 16 Question 6

6) In an ideal regression model, we want our tolerance and VIF to be…

a. tolerance greater than 2 and VIF less than 10.

b. tolerance greater than 0.2 and VIF less than 10.

c. tolerance less than 2 and VIF greater than 10.

d. tolerance less than 0.2 and VIF greater than 10.

Type: multiple choice question

Title: Chapter 16 Question 7

7) If we find gender (0 = Male; 1 = Female) to be a significant predictor of aggression with a β of -.82, then how would we interpret this?

a. A one unit increase in gender is associated with a .82 decrease in aggression.

b. Being female is associated with a .82 decrease in aggression.

c. A .82 decrease in gender is associated with a one unit increase in aggression.

d. Being male is associated with a .82 decrease in aggression.

Type: multiple choice question

Title: Chapter 16 Question 8

8) To interpret whether there is evidence of homoscedasticity we should look at…

a. the histogram.

b. the scatterplot.

c. VIF.

d. tolerance.

Type: multiple choice question

Title: Chapter 16 Question 9

9) A researcher wants to explore the relationship between hair colour (blonde, brunette, auburn), salary, and empathy on amount of money donated to charity. He plans to use a regression analysis, what is wrong with this?

a. You cannot use a categorical variable (hair colour) in a regression analysis.

b. There are too many predictor variables.

c. There are too few predictor variables.

d. You cannot have a continuous outcome variable.

Type: multiple choice question

Title: Chapter 16 Question 10

10) If a researcher found that being a smoker was associated with an increase in the risk of heart disease, then how should they graph this finding?

a. Scatterplot

b. Histogram

c. Pie chart

d. Bar chart

Type: multiple choice question

Title: Chapter 16 Question 11

11) In the worksheet analysis, which of the assumptions of multiple regression have been violated?

a. Normal distribution of residuals

b. Homoscedasticity

c. No multicollinearity

d. None of the assumptions have been violated.

Type: multiple choice question

Title: Chapter 16 Question 12

12) How much of the variance (as a %) in the outcome variable can be explained by the predictors entered into the model?

a. 49.9%

b.11.5%

c. 24.0%

d. 4.99%

Type: multiple choice question

Title: Chapter 16 Question 13

13) What is the significance of the overall model?

a. p = .037

b. p = .003

c. p = .044

d. p < .001

Type: multiple choice question

Title: Chapter 16 Question 14

14) How would you interpret the β for the “location” predictor variable?

a. Children living in rural environments have a score 3.027 higher than children living in urban environments.

b. Children living in rural environments have a score 3.027 lower than children living in urban environments.

c. Children living in rural environments have a score 4.481 higher than children living in urban environments.

d. Children living in rural environments have a score 4.481 lower than children living in urban environments.

Type: multiple choice question

Title: Chapter 16 Question 15

15) Which variable was the most highly significant predictor of the outcome variable?

a. Attention (n back)

b. Hours spent in nature

c. Connectedness to nature

d. Living environment (urban/rural)