Regression Assumption Checker
This free tool helps psychology students understand and evaluate the five core assumptions of linear regression.
Linearity
The relationship between independent and dependent variables should be linear.
- How to check: Use scatterplots and residual plots.
- Signs of violation: Curved or wave-shaped patterns in the plots.
- If violated: Try log, square root, or polynomial transformations.
Independence of Residuals
Residuals should not be correlated — especially important for time-series or clustered data.
- How to check: Durbin-Watson test or plot residuals over time/order.
- If violated: Use mixed-effects models or time-series analysis.
Homoscedasticity
Residuals should have constant variance across predicted values — not a “fan” shape.
- How to check: Residual vs fitted value plots.
- If violated: Use transformations or robust standard errors.
Normality of Residuals
Residuals (not the outcome) should be roughly normally distributed for valid inference.
- How to check: Histograms, Q-Q plots, or Shapiro-Wilk test.
- If violated: Try transformations or use GLMs where appropriate.
Multicollinearity
Predictors should not be highly correlated with each other.
- How to check: Examine correlation matrix and VIF scores.
- If violated: Remove or combine predictors, or center interaction terms.
Understand Regression Assumptions — The Easy Way
If you're a psychology student struggling to understand the assumptions of linear regression, you're not alone. This free tool was designed to help students like you get clear, concise guidance on:
Linearity
Independence of residuals
Homoscedasticity
Normality of residuals
Multicollinearity
Each assumption is explained in plain language, with practical tips on how to check it, what to look for in your plots, and what to do if an assumption is violated.
🎓 Perfect for Psychology and Social Science Students
Whether you’re working on a dissertation, lab report, or stats assignment, understanding these assumptions is critical to interpreting your results accurately. This guide is ideal for:
Undergraduate psychology students
MSc and PhD researchers
Anyone using SPSS, JASP, R, or Python for regression analysis
🔍 Why Assumptions Matter
Violating regression assumptions can lead to misleading p-values, biased coefficients, and invalid conclusions. By reviewing each assumption, you'll improve the reliability and credibility of your research — something every supervisor or journal reviewer expects.
🚀 Want Interactive Help and an APA-Style Write-Up?
Looking for a smarter way to learn? The premium version includes:
Step-by-step checks
Violation alerts
A custom-generated APA-style results paragraph you can copy straight into your paper
👉 Upgrade to the Interactive Version
| Feature | Free Version | Pro Version |
|---|---|---|
| Static Explanation of Assumptions | ✔ | ✔ |
| Step-by-Step Interactive Guidance | ✘ | ✔ |
| Assumption Violation Warnings | ✘ | ✔ |
| Personalized Summary Section | ✘ | ✔ |
| APA-Style Results Paragraph | ✘ | ✔ |
| Copy-to-Clipboard Function | ✘ | ✔ |
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