Standard Multiple Regression

Question 1

rules of standard multiple regression

Answer 1

all predictors entered simultaneously

use if have a set of variances and want to know variance in a criterion variable they can explain together and unique variance of predictors

Question 2

when to use standard MR

Answer 2

use if have a set of variances and want to know variance in a criterion variable they can explain together and unique variance of predictors

Question 3

steps in Standard MR

Answer 3

1. check assumptions are met
2. Assess the regression model overall
3. Evaluate predictors

formally report and interpret results

Question 4

check assumptions stage

Answer 4

check:
multicollinearity
linearity
normality
homoscedasticity
sample size

Question 5

assess overall model stage

Answer 5

variables entered/removed
(shows which predictors are in the model)

model summary table
(R=multiple regression coefficient between C&P)
(R square = how much variability is accounted for by predictors)
(adj. R square = how well our model generalises)

ANOVA table
(significant model, p<0.001)
(whether or not model is significant predictor of the outcome variable)

Question 6

evaluate the predictors stage

Answer 6

coefficients
(use standardised coefficients beta to compare predictors)
(significant if p<0.05, the variable makes a significant unique contribution to outcome (DV) prediction

Question 7

formally reporting the results

Answer 7

A standard multiple regression was used to assess the ability of the predictor variables (name) to predict the criterion variable (name). All predictor variables were entered simultaneously.

Preliminary analyses were conducted to ensure no violation of the assumptions of normality, linearity, multicollinearity, homoscedasticity and sample size. (state whether the assumptions were met or violated (explain if violated))

(adj. Rsquare)% of the variation in (criterion variable) could be explained by the variation in (predictor variables)
F( , ) = ,p<0.001

(predictor variable) made a significant contribution to the model (P< ). (Explain the significance/contributions of all factors on criterion).

Question 8

where is the information for this found?

F( , ) = ,p<0.001

Answer 8

in the model summary and ANOVA tables