🔯Multiple Regression in Python Part 3: Color-Coded Influence Plots (Cook’s D + Leverage + DFBETAS)

📖In Part 3 of this multiple regression series, we focus on color-coded scatter plots to detect and interpret influential observations and to find if any observations are distortionary which I call them monsters. This monstrosity will require greater scrutiny including deletion. You will learn how to read: 💠Residual vs Fitted scatter plots •Leverage vs Studentized Residual scatter plots •Bubble size mapped to Cook's D •Color flags for influential vs non-influential observations This lesson builds directly on: 〰️Part 1: raw outlier detection Part 2: Cook's D, DFBETAS, and leverage cutoff diagnostics If you want practical regression diagnostics in Python, this walkthrough shows how to move from metrics to visual judgment. 🙏Subscribe for Part 4, where the full comparison and final decision workflow will be completed. Hashtags: #Python #DataScience #MultipleRegression #RegressionDiagnostics #Statistics #CooksDistance #DFBETAS