multiple regression

• multiple regression

  • predict outcomes form several predictors. it is a hypothetical model of the relationship between several variables
• y = b0 + b1x1+ b2x2...+ bnxn
• Standard multiple regression/ Forced entry where all predictors entered at once
• Stepwise Multiple Regression  where mathematical criteria determine the order in which predictors are entered  - Predictors are selected using their semi-partial correlation with the outcome.
• Hierarchical Multiple Regression where the predictors are selected based on past work and the experimenter decides which order to enter predictors into the model
• R2

  The proportion of variance accounted for by the model.
• ANOVA table tells you whether the whole model is statistically significant. It tells us whether using the regression model is significantly better at predicting values of the outcome than using the mean.
• Beta values: the change in the outcome associated with a unit change in the predictor.

  Standardised beta values: tell us the same but expressed as standard deviations.
• regressions are not symmetric