🛳️ 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
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#Python #DataScience #Regression #Outliers #IQR #Econometrics #MachineLearning #Statistics
