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Equal variance assumption

As explained in Statistics Slam Dunk

The equal-variance assumption states that the response variance is the same across the groups defined by a categorical predictor.

The equal-variance assumption requires response values to show similar variability across every group of a categorical predictor. In one-way ANOVA, diagnostic plots help assess whether this condition is reasonable. section 14.5.2

NBA wins by scoring category

Fit an ANOVA model with `aov(z_wins ~ o_pts_cat, data = nba_stats)`, then inspect Residuals vs. Fitted. Equal variance would produce similarly dispersed residuals at each fitted-value level. Here, residuals are less dispersed at the highest fitted values, suggesting a likely—but not statistically significant—violation. section 14.5.2

How Statistics Slam Dunk covers it

How deep it goes
Explains the visual criterion and applies it through Residuals vs. Fitted diagnostics, stopping short of formal variance tests.
Examples
Uses NBA regular-season wins grouped by scoring-category factor levels and compares residual dispersion visually.
What it assumes
Assumes familiarity with categorical predictors, residuals, fitted values, and one-way ANOVA.
What you can do after
Diagnose potential heteroscedasticity by plotting ANOVA residuals against fitted values.
How it connects
Connects one-way ANOVA with residuals, fitted values, diagnostic plots, and normality.

This explainer was generated by Manning's knowledge graph from the text of Statistics Slam Dunk. The section links point to the parts of the book it draws on.