🤓Multiple Regression in Python Part 2: Influential Observations - Cook's D, DFBETAS & High Leverage

🌟if you missed Part 1: Outlier Detection & Data Cleaning, watch that first. 🔯While concluding that part, I mentioned 'harmless' and 'harmful' observations. This part 2 will check that with 'high leverage observations' being harmful and if not high leverage observation but only influential and outlier observation as 'harmless'. The harmful observations are presented in a table titled High Leverage Observations- the last table of this lesson. 🌟This is Part 2 of my Multiple Regression in Python series. In this lesson, I focus on influential observations using Cook's D, DFBETAS, and high leverage diagnostics, with practical decision rules in Python. This topic is especially important for econometrics students and applied analysts, because influential-point diagnostics are often under-emphasized in many standard learning paths. A raw outlier is not automatically harmful data, and this video shows how to separate outlier detection from influence diagnosis correctly. What you will learn: 💠How Cook's D, DFBETAS, and leverage each measure different risk 💠How to combine them into a defensible decision rule 💠How to interpret flagged observations before deciding whether to keep, investigate, or remove them 🐍 Libraries: pandas, numpy, statsmodels, matplotlib, seaborn 🔔 Subscribe for more statistics done properly in Python. 🔔like and comment to tell what is clicking and what is not indicating the level of technical details. Tags: multiple regression python, influential observations, cook's distance python, DFBETAS regression, high leverage points, statsmodels OLS, regression diagnostics python, outlier vs influential observation.