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What the dense version becomes

Before: Heteroscedasticity, wherein the variance of residuals is non-constant across levels of the predictor, violates the homoscedasticity assumption of ordinary least squares.

  • Residuals are the gaps between predictions and reality.
  • Heteroscedasticity: those gaps change size as the predictor grows.
  • Ordinary least squares assumes the gaps stay the same size.