Quiz 2

Regression Diagnostics

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Python Week 1: the first filter for runtime behavior
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# Regression Diagnostics ## 5.1 Residual Analysis **Residuals:** $e_i = y_i - \hat{y}_i$ ### Key Plots - **Residuals vs. fitted:** Should show random scatter (check homoscedasticity, linearity) - **QQ plot:** Check normality of residuals - **Residuals vs.

Regression Diagnostics

5.1 Residual Analysis

Residuals: ei=yiy^ie_i = y_i - \hat{y}_i

Key Plots

  • Residuals vs. fitted: Should show random scatter (check homoscedasticity, linearity)
  • QQ plot: Check normality of residuals
  • Residuals vs. order: Check independence

5.2 Influence Measures

  • Leverage: hiih_{ii} — how far xix_i is from other xx's
  • Cook's distance: DiD_i — influence of ii-th observation on all fitted values
  • DFFITS: Change in fitted values when observation ii is deleted

Outlier Detection

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