Flashcards in An Introduction to Multiple Regression Deck (24)

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1

## How do you calculate a residual?

### It is the observed value - predicted value.

2

## What is a Partial Correlation?

### It is the correlation between two variables while controlling for a third.

3

## What is a Semi-Partial Correlation?

### It is the correlation between two variables while looking at the correlation between the third variable and one of those variables.

4

## What are the 3 main things we can predict from a Multiple Regression model?

###
How well the model explains the outcome.

How much variance in the outcome our model explains.

The importance of each individual predictor.

5

## What are the 3 main types of Multiple Regression?

###
Forced entry (all data in at once).

Hierarchical (researcher decides variable order).

Stepwise (SPSS decides variables order).

6

## What program should you use to determine the sample size needed (which depends on the effect size)?

### G*Power.

7

## What is R-Squared?

### It is the variance accounted for by the model (the amount of variance in the DV the model explains).

8

## How do we know if our model generalises well?

### The closer R-Squared is to the Adjusted R-Squared the more accurate our model is likely to be for other samples.

9

## Why is R not useful?

### This is because in Multiple Regression we have several variables.

10

## Why is the Standardised Coefficients Beta important?

### It allows us to compare predictors to decide which are the most important. The higher the number the more important the variable as a predictor.

11

## When reporting the regression equation what are the coefficients also known as in SPSS?

### Unstandardised B.

12

## What are the three assumptions of Multiple Regression pre-experiment?

###
The outcome variable should be continuous.

The predictor variable should be continuous or dichotomous.

There should be reasonable theoretical ground for including variables.

13

## What are the four assumptions of Multiple Regression post-experiment?

###
Linearity.

Homoscedascity.

Normal distribution of residuals.

No multicollinearity.

14

## What is meant by linearity?

### There should be a linear relationship between each predictor and the outcome. Partial plots should be checked for this.

15

## What is meant by homoscedascity?

### The variance of the residuals should be constant for all values of the predicted values.

16

## What shape indicates heteroscedasticity?

### Funnel/cone shape.

17

## What graph should be looked at when checking for homoscedascity?

### Graph of standardised residuals by standardised predicted values (ZRESID by ZPRED).

18

## What two graphs should be looked at when checking for normal distribution of residuals?

### Histogram (should be bell shaped) + normal probability plot (points should be close to the diagonal).

19

## What two statistics should you look at to check for no multicollinearity?

### Tolerance + VIF statistic.

20

## What are the tolerance + VIF statistic rules in order for there to be no multicollinearity?

###
VIF value should not be larger than 10.

Tolerance value should not be less than 0.1 (although 0.2 is already a concern).

21

## Why is multicollinearity an issue?

###
A good predictor might be rejected.

It may lead to errors in estimation of regression coefficients.

22

## What are two possible solutions for multicollinearity?

###
Combine predictors.

Remove one of the variables.

23

## What is an alternative indication of multicollinearity (not including the VIF + tolerance statistics)?

### A high R-Squared with non-significant beta coefficients.

24