A Multiple Linear Regression is an expansion of a Simple Linear Regression allowing multiple predictors to be incorporated into a single model.
It is considered a parametric test and is only suitable for parametric data. To check if your data is parametric, please check out the dedicated guide: Parametric or Not Guide (PDF)
Click Analyze > Regression > Linear
Within the 'Linear Regression' window, select your Outcome variable and move it to the 'Dependent' box, select your Predicitor variables and move to to the 'Block 1 of 1' box.
Select Statistics and click Estimates, Confidence Intervals, Model Fit, R Squared Change, and Descriptives
Click Continue, click OK
SPSS will generate a large number of tables, for this test we need the final three, the Model Summary, the ANOVA and the Coefficients.
This table shows a selection of descriptive statistics about the model/regression overall: the R-value (R), the R-Squared Statistic (R Square), the F statistic measuring change (F Change) and the p-value associated with the F stat change (Sig. F Change)
This table shows a further selection of descriptive statistics about the model/regression overall: two different Degrees of Freedom (df) , the F statistic measuring change (F Change) and the p-value associated with the F stat change (Sig. F Change)
This final table shows the exact values of the constant and all of our predictors, it also shows if the variables are significant (Sig.) and the 95% Confidence intervals (95.0% Confidence Interval for B)
A multiple linear regression was used to predict a student's physics score using their mathematics and English scores. The models did explain a significant amount of the variance in the physics scores, F ( 2,168) = 20.72, p < .001, R^{2 }= .20 R^{2}_{adjusted }= .19. Mathematics score was a significant predictor of physics score (B = 0.43, t(168) = 6.22, p < .001) with an increase of one point in mathematics score, would correspond, on average, an increase in physics score by 0.49 points 95% CI [0.34,0.66]. English score was not a significant predictor of physics score (B = -0.08, t (168) = -1.19, p = .234, 95% CI [-.26, .064]).