Regression can be used to predict variables:

- IV: x axis (predictor)
- DV: y axis (criterion / outcome)

Analyze -> Regression -> Linear

- move variables into corresponding boxes
- ensure “Method = Enter”
- in SPSS
- raw equation: DV = (B [slope] x IV) + constant [intercept]
- standardised: Z
_{DV}= β [beta] x Z_{IV}(standardised variables to Z scores) - standardised is a better indicator of strength and measure independent of units

- in multiple regression (more than one IV) β measures unique effect of IV – no shared variance
- look in last table for β value (the correlation) and B and constant
- look in ‘model summary’ table for R
^{2}(proportion of variance explained by all IVs) - look in ‘ANOVA’ table for F value (to check if R
^{2}predicts DV better than chance – if F is significant then R^{2}is better) - R
^{2}: need to multiply by 100 to get %