In this video, I’ll show you how to check and control the 3 fundamental ANOVA assumptions 📊 using Python: normality of residuals, homogeneity of variances, and independence of observations. I’ll guide you step by step, and you can use the same simple coding techniques!
WATCH THIS VIDEO TO LEARN:
- How to create qq-plots and perform a Shapiro-Wilk test to assess normality.
- How to create residual vs. fitted plots to assess homogeneity of variances (=homoscedasticity).
- How to create sequence plots to assess independence of observations.
- Explore solutions if any of the above assumptions aren't met.
🤖 GET ACCESS TO MY AI ASSISTANT: https://www.experimentaldesignhub.com/downloads/p/your-ai-assistant-for-doe
📂 GET ACCESS TO THE CODE: https://www.experimentaldesignhub.com/downloads/p/how-to-control-anova-assumptions-jupyter-file
📖 READ THE BLOG POST: https://www.experimentaldesignhub.com/blog/how-to-control-anova-assumptions
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