Simple and multiple regression can be used to predict outcomes from one or more predictor variables. Simple regression uses a single predictor while multiple regression uses two or more. Regression establishes the strength and direction of the linear relationship between variables. It can be conducted in SPSS and results include coefficients, R-square values, and whether predictors significantly contribute to the model. The assumptions of regression like linearity and normality should be checked.

1. SIMPLE AND MULTIPLE REGRESSION Chris Stiff [email_address]

7. LINE OF BEST FIT Amount of alcohol Stupid behaviour

8. LINE OF BEST FIT – POOR EXAMPLE Stupid behaviour Number of pairs of socks ?

11. SPSS OUTPUT

12. SPSS OUTPUT R = correlation between amount drunk and stupid behaviour R square = proportion of variance in outcome (behaviour) accounted for by the predictor (amount drunk) Adjusted R square = takes into account the sample size and the number of predictor variables

14. SPSS OUTPUT

15. SPSS OUTPUT Beta = standardised regression coefficient and shows the degree to which a unit increase in the predictor variable produces a standard deviation change in the outcome variable with all other things constant

22. MULTIPLE REGRESSION - EXAMPLE Attendance at lectures Books read Motivation Exam Performance (Grade) What might predict exam performance?

25. MULTIPLE REGRESSION: SPSS OUTPUT

26. MULTIPLE REGRESSION: SPSS OUTPUT

27. MULTIPLE REGRESSION: SPSS OUTPUT For overall model: F(2, 42) = 12.153, p<.001

28. MULTIPLE REGRESSION: SPSS OUTPUT Number of books read is significant predictor b=.33, t(42) = 2.24, p<.05 Lectures attended is a significant predictor b=.36, t(42) = 2.41, p<.05

32. ENTER OUTPUT

33. ENTER OUTPUT R square = proportion of variance in outcome accounted for by the predictor variables Adjusted R square = takes into account the sample size and the number of predictor variables

34. ENTER OUTPUT

35. ENTER OUTPUT Beta = standardised regression coefficient and shows the degree to which the predictor variable predicts the outcome variable with all other things constant

38. Don’t forget to choose the r-square change option from the Statistics menu

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