User Guide: Regression Output Interpreter

Welcome to the Regression Output Interpreter. Once you have verified your assumptions are clean (using the Regression Doctor AI), this tool helps you dive deep into your results, extracting the true predictive power of your variables.

1. Why look at "Adjusted" R Square?

In the Model Summary table, SPSS gives you both R Square and Adjusted R Square. When running a Multiple Linear Regression (more than 1 independent variable), you must report the Adjusted version.

Reason: Regular R² will artificially increase every time you add a new variable to your model, even if that variable is complete nonsense. Adjusted R² mathematically penalizes you for adding useless variables, providing a much more accurate reflection of how much variance your model truly explains.

2. ANOVA vs. Coefficients

These two tables answer different questions:

ANOVA: Asks, "Does the model as a whole work?" It looks at all your Independent Variables combined. If the ANOVA Sig. (p-value) is > .05, the entire model fails, and you stop there.
Coefficients: Asks, "Which specific pieces of the model work?" If the overall model works, this table breaks down each individual variable to see which ones are significantly driving the outcome.

3. Unstandardized (B) vs. Standardized (β)

This is the most common point of confusion for MA students:

Unstandardized B (The Equation Maker): This is in the original units of measurement. It means: "For every 1 unit increase in Motivation, Writing Score goes up by exactly B points." You use this column to write the mathematical prediction equation.

Standardized Beta (The Strength Tester): This converts everything to the same statistical scale (Z-scores). If you want to answer the question, "Which variable is the strongest predictor?" you look at Standardized Beta. Whichever variable has the largest absolute Beta value is the strongest.

4. Extracting Data from SPSS

Run: Analyze > Regression > Linear...
Copy the Model Summary, ANOVA, and Coefficients tables directly into the input fields in the app. Ensure you include the significance (Sig.) columns for all tables.