Multiple Regression in Python | Part 1: Outlier Detection & Data Cleaning (IQR + Boxplots)

🛳️ Unlock the power of Python in Data Science — Step 1 of building a statistically sound regression model. This is Part 1 of a 4-part Multiple Regression series. By the end of this series you'll have a complete, defensible model from which theoretically plausible and statistically valid hypotheses can be tested and predictions made. In this video you will learn: 💠 Data loading & wrangling — renaming variables, reading data tables 💠 Initial data inspection — spotting erroneous records and potential outliers 💠 Systematic outlier detection using the IQR (Inter-Quartile Range) rule 💠 Pinpointing exactly which raw observations are flagged as outliers 💠 Confirming outliers visually with Box Plots (one per variable/column) What's coming next: 📌 Part 2 — Regression & influence diagnostics: Cook's D and DFBETA in a table 📌 Part 3 — Visualization: fitted values vs. residuals, leverage vs. studentized residuals 📌 Part 4 — Conclusion: why model diagnostics matter as much as variable selection & functional form 🙏 If this helped you, please like, comment & subscribe — it directly supports more lessons on Python, Data Science, Econometrics, and objective policy evaluation. 〰️ Your questions and suggestions drive this channel. I read and reply to every comment. Drop yours below! #Python #DataScience #Regression #Outliers #IQR #Econometrics #MachineLearning #Statistics