Question 1

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

Accepted Answer

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 
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 D

Question 2

How would you interpret the β for the "location" predictor variable?

Accepted Answer

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. 
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. C

Question 3

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

Accepted Answer

A)  overestimating. 
B)  underestimating. 
C)  neither over or underestimating. 
D)  a good predictive model. 
A)  overestimating. 
B)  underestimating. 
C)  neither over or underestimating. 
D)  a good predictive model. A

Question 4

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

Accepted Answer

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. 
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.

Question 5

Multicollinearity is when…

Accepted Answer

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. 
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.

Question 6

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

Accepted Answer

A)  Normal distribution of residuals 
B)  Homoscedasticity 
C)  No multicollinearity 
D)  None of the assumptions have been violated. 
A)  Normal distribution of residuals 
B)  Homoscedasticity 
C)  No multicollinearity 
D)  None of the assumptions have been violated.

Question 7

What does a β = -.17 indicate?

Accepted Answer

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 
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

Question 8

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?

Accepted Answer

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. 
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.

Question 9

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

Accepted Answer

A)  Attention (n back) 
B)  Hours spent in nature 
C)  Connectedness to nature 
D)  Living environment (urban/rural) 
A)  Attention (n back) 
B)  Hours spent in nature 
C)  Connectedness to nature 
D)  Living environment (urban/rural)

Question 10

What is the significance of the overall model?

Accepted Answer

A)  p = .037 
B)  p = .003 
C)  p = .044 
D)  p < .001 
A)  p = .037 
B)  p = .003 
C)  p = .044 
D)  p < .001

Question 11

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?

Accepted Answer

A)  Scatterplot 
B)  Histogram 
C)  Pie chart 
D)  Bar chart 
A)  Scatterplot 
B)  Histogram 
C)  Pie chart 
D)  Bar chart

Question 12

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?

Accepted Answer

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. 
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.

Question 13

How many outliers are acceptable in a regression model?

Accepted Answer

A)  None 
B)  Up to 5% 
C)  Up to 10% 
D)  Up to 1% 
A)  None 
B)  Up to 5% 
C)  Up to 10% 
D)  Up to 1%

Question 14

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

Accepted Answer

A)  49.9% 
B)  11.5% 
C)  24.0% 
D)  4.99% 
A)  49.9% 
B)  11.5% 
C)  24.0% 
D)  4.99%

Question 15

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

Accepted Answer

A)  the histogram. 
B)  the scatterplot. 
C)  VIF. 
D)  tolerance. 
A)  the histogram. 
B)  the scatterplot. 
C)  VIF. 
D)  tolerance.

Exam 16: Multiple Regression

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

How would you interpret the β for the "location" predictor variable?

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

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

Multicollinearity is when…

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

What does a β = -.17 indicate?

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?

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

What is the significance of the overall model?

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 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?

How many outliers are acceptable in a regression model?

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

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

The Basics of Research Design

Questionnaire Design

Analysing Data for Psychological Research

Introducing Spss

Experimental Design

T Tests

One-Way Independent Measures Anova

One-Way Repeated Measures Anova

Ancova

Two-Way Factorial Independent Anova

Two-Way Factorial Repeated Anova

Two-Way Factorial Mixed Design Anova

Presenting Experimental Studies

Correlational Design

Correlations

Hierarchical Regression

Logistic Regression

Factor Analysis

Presenting Correlational Studies

Qualitative Design

Designing Interviews and Focus Groups

Content Analysis and Thematic Analysis

Grounded Theory

Interpretative Phenomenological Analysis

Discourse Analysis

Presenting Qualitative Studies

How Do I Decide What to Do With My Data

Mixed Methods

Broadening Your Research Horizons

Filters

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