Simple Linear Regression Assignment Help — SPSS Output Explained

What Is Simple Linear Regression and When Do You Use It?

Simple linear regression predicts one continuous outcome from one predictor variable. Where Pearson correlation describes whether two variables relate, regression goes further — it produces an equation that predicts the outcome and tells you how much it changes per unit of the predictor.

Assumptions You Must Check Before Running It in SPSS

Linearity, Independence, and Homoscedasticity

Check linearity with a scatterplot of the predictor against the outcome. Check independence of residuals with the Durbin-Watson statistic — values near 2 indicate independence; values well outside 1.5–2.5 suggest a problem. Check homoscedasticity with a residual scatterplot, which should show an even, random spread rather than a funnel shape.

Normally Distributed Residuals

Check this with a P-P plot of the regression standardised residuals — points should sit close to the diagonal line — or a histogram of the residuals.

How to Run Simple Linear Regression in SPSS (Step by Step)

1. Go to Analyze > Regression > Linear.
2. Move your outcome variable into Dependent.
3. Move your predictor into Independent(s).
4. Click Statistics, then tick Durbin-Watson and Confidence intervals.
5. Click Plots, add ZRESID against ZPRED to check homoscedasticity, and tick Normal probability plot.
6. Click Continue, then OK.

How to Interpret Simple Linear Regression Output

Model Summary Table — R and R²

R² is the proportion of variance in the outcome explained by the predictor, expressed as a percentage.

ANOVA Table — Is the Model Significant Overall?

Check the F-statistic and its p-value first — this tells you whether the model explains a significant amount of variance before you look at the individual coefficient.

Coefficients Table — Reading B and Its Significance

B is the raw-unit change in the outcome for each 1-unit increase in the predictor. Its t-value and p-value test whether that slope differs significantly from zero.

How to Report Simple Linear Regression Results in APA Format

The model significantly predicted exam score, F(1, 58) = 14.2, p < .001, R² = .20, b = 0.45, t(58) = 3.77, p < .001.

What If You Have More Than One Predictor?

Simple Regression vs Correlation vs Multiple Regression

If your research question involves several predictors at once, you need multiple linear regression, which extends everything on this page and adds a multicollinearity check. If you only need to describe a relationship rather than predict a value, Pearson correlation is the simpler and correct tool.

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