Multiple Regression Practice

Question 1

What assumptions need to be checked before hand

Answer 1

Normality
Linearilty
Homoscedasity
Multicolineraity and tolerance

Question 2

How are assumptions for multiconlineraity evaluated

Answer 2

By VIF >10 and Tolerance <0.1 and no correlation between variables r

Question 3

By looking at the graph, how would you know that normality has been met

Answer 3

By the data following a symmetrical and bell shape curve

Question 4

What graph shows multicollinearity

Answer 4

Histogram

Question 5

What graph shows normality

Answer 5

P-P plot

Question 6

By looking at the P-P plot how do you know that normality is assumed

Answer 6

The dots lying almost exactly along the diagonal line throughout the plot

Question 7

What graph indicates homoscedasicity

Answer 7

Scatter

Question 8

By looking at the scatter plot how do you know homoscedasicity is assumed

Answer 8

Points being randomly and evenly dispersed throughout with Cooks distance <1

Question 9

What graph needs to be presented with all the variables and their relationships

Answer 9

Correlation Table

Question 10

What does the greatest correlation suggest

Answer 10

Likely that the highly correlated variable to the DV will be a predictor

Question 11

What does an r2 value indicate

Answer 11

The amount of variability in the model accounted for by the DV

Question 12

What does the r2 value need to be mutlipeld by to get percentage

Answer 12

x100

Question 13

What does an adjusted r2 value being close to the R2 value indicate

Answer 13

Good cross variability of the model

Question 14

After the R2 what needs to be conducted to ensure the model is significantly better than the mean

Answer 14

A ANOVA

Question 15

What does the table need to contain to establish the best predictors

Answer 15

Beta weights
std.error
t
Sig

Question 16

How do you identify the significant predictors from the beta table

Answer 16

The level of significance