Regression Analysis in SPSS – How to Interpret Results

What is Regression Analysis

Regression analysis allows:

• prediction of dependent variable values
• estimation of predictor effects
• testing model significance

Key SPSS Output Tables

• Model Summary
• ANOVA
• Coefficients

Model Summary

Contains:

• R
• R²
• Adjusted R²

Interpretation:

• R² = 0.25 → 25% explained variance

ANOVA

Tests overall model significance.

• p < 0.05 → significant
• p < 0.001 → highly significant

Coefficients

Includes:

• B
• Beta
• t
• p

B coefficient

Change in dependent variable per unit change in predictor.

Beta

Standardized effect size → comparison of predictors

p-value

• p < 0.001 → highly significant
• p < 0.05 → significant
• p > 0.05 → not significant

Multicollinearity

• VIF
• Tolerance

Interpretation:

• VIF < 5 → acceptable
• VIF > 10 → problematic

Assumptions

• normality
• linearity
• homoscedasticity
• no multicollinearity

Example Interpretation

The regression model is statistically significant (F = 15.32, p < 0.001) and explains 25% of variance (R² = 0.25). The predictor shows a significant positive effect (β = 0.32, p < 0.001).

The author remains responsible for the final interpretation of results.
I will prepare statistical results and propose how to present them.

Common Mistakes

• misinterpreting R²
• ignoring assumptions
• focusing only on p-values

Conclusion

Regression analysis is a powerful method but requires correct interpretation.

How to Cite (APA)

Miljko, L. (2025). Regression Analysis in SPSS – How to Interpret Results. Statistische Beratung.
Retrieved from: https://statistischeberatung.de

Keywords

regression analysis SPSS, linear regression, interpretation SPSS, R squared, beta coefficient

Meta Text

Learn how to interpret regression analysis in SPSS. Practical guide with R², ANOVA, and coefficients explained.