Week 9

Question 1

What are the assumptions of regression models?

Answer 1

• use correct variables for the technique (for linear, we want interval level data, equal distances between each point of the scale)
• independence of data (and error terms)
• sample size and normality (of variables and residuals)
• linearity
• homoscedacity (of residuals)

Question 2

In regressions models, the ____ the sample size the better.

Answer 2

Larger

Question 3

You can check for non linearity using a:

Answer 3

plot. Select statistics for correlation values.

Question 4

What is residuals another way of saying?

Answer 4

Pretty much saying how crappy your data is.

Question 5

What does checking the pattern of model mis-fit check in linear regression?

Answer 5

Scedacity

Question 6

Under assumption checks, what do you tick if you are wanting to look at if your model is good at all values in a linear regression?

Answer 6

Tick residual plots, if your data is equally dispersed and shows no real pattern, this is good at predicting scores across the spectrum of scores of the predictor variable.

Question 7

What is homoscedacity?

Answer 7

It means that the error variance should be the same at each level of the predictor variable.

Question 8

Rather than eyeballing plots, how else can we test heteroscedacity?

Answer 8

Under assumption checks. If we have a significant value, this is BAD. WE have violated the assumption of homoscedacity.

Question 9

What type of designs do even a small amount of outliers have a huge effect for?

Answer 9

Linear regression models

Question 10

Can we ever remove unusual scores (outliers)?

Answer 10

people have strong opinions about this.

Question 11

how can we test to see if we have any unusual outliers? (2)

Answer 11

1. Using cooks distance (you can save this to the data set)

2. any residual value >3 SD’s from the mean manually

Question 12

Simultaneous multiple regression examines the ____ ability of a set of predictor variables in accounting for variability in a response variable

Answer 12

combined

Question 13

In a hierarchical multiple regression, what is in model 1?

Answer 13

Everything that was in block one.

Question 14

In hierarchical multiple regression, what is included in model 2?

Answer 14

Everything that was in block one, plus that which was i block two.

Question 15

What explains the percentage of the variation explained by the models in hierarchical multiple regression?

Answer 15

Adjusted R squared.

Question 16

In model comparisons, what demonstrates the percentage of variation ADDED to the model by block 2?

Answer 16

The triangle R squared.

Question 17

Hierarchical multiple regression is ____ driven:

Answer 17

hypothesis

Question 18

When we are trying to construct the optimal regression model, what are the 3 ways we could do this?

Answer 18

1. forced entry
2. forward stepwise entry
3. backward stepwise removal

Question 19

What is forced entry in a multiple regression?

Answer 19

Putting all predictors into the model at once, just like simultaneous multiple regression

Question 20

What is forward stepwise entry?

Answer 20

putting the best predictors into the model first, then only entering more predictors if they improve the quality of the predictive model (i.e., significantly increasing the R squared)

Question 21

What is backward stepwise removal?

Answer 21

starting with all the predictors in the model, then start throwing out the worst predictors until this has a negative impact on the quality of the predictive model (i.e., significantly reducing the R squared).

Question 22

Why are some people against stepwise methods?

Answer 22

Because they’re data driven, we can capitalise on the results, even if it makes no sense theoretically.

Question 23

After we put everything in the model at the same time in a stepwise multiple regression, what is the next step?

Answer 23

Order the t values (positive or negative) in how big the effect is. This will help to know how you should order the stepwise analysis.

Question 24

What does the t-test tell you in model coefficients (stepwise multiple linear regression)?

Answer 24

How certain you can be that the coefficient is different from 0. Enter them all in difference blocks one by one (1 IV in each block).

Question 25

What is the difference between hierarchical multiple regression and stepwise regression?

Answer 25

Hierarchical is theory driven whilst stepwise is data driven