A 'Simple' Linear Regression is the next step on from a correlation and attempts to predict one variable (the Outcome Variable) using another variable (the Predictor Variable).
It is considered a parametric test and therefore 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 variable 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 our predictor, it also shows if the variable is significant (Sig.) and the 95% Confidence Interval (95.0% Confidence Interval for B)
A simple linear regression was used to predict a student's physics score using their mathematics score. Mathematics scores did explain a significant amount of the variance in the physics scores, F(1,167)= 39.92, p < .001, R^{2}=.19. The regression coefficient (B=0.51, 95% CI[0.35,0.67]) indicated that an increase in one point mathematics score, would correspond, on average, to an increase in physics score by 0.51 points.