Coefficients in Logistic Regression Explained Simply: How Changing One Variable Changes Everything

🎓 Full Course HERE 👉: https://sds.courses/ds-az In this video, we take a deep dive into one of the most important ideas in logistic regression: how changing one factor can affect your chances or odds of something happening. You’ll learn how small changes in your data — like a person being one year older — can shift the odds in a predictable way. We’ll show you how to understand the impact of each variable in your model and how to tell which ones make the biggest difference.

You’ll learn:
✓ How to derive the odds ratio from logistic regression
✓ What it means to increase an independent variable by one unit
✓ How e^B shows the multiplicative change in odds
✓ A real example using the "age" variable from a churn model
✓ This is foundational for understanding how variables influence probability in logistic models.

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📌 Chapters:
00:00 - Introduction: Odds, Probability & Coefficients
00:32 - Recap of Odds Ratio Formula
01:07 - The Logistic Regression Equation
01:44 - From Log Odds to Linear Coefficients
02:21 - Exponentiating the Equation
03:29 - The Impact of a One-Unit Increase in X
04:47 - Final Conclusion: e^B Interpretation
05:23 - Real Example: Age and Churn Odds
06:42 - Summary: Quantifying Variable Impact

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#LogisticRegression #OddsRatio #DataScience #expB #MachineLearning #RegressionModel #StatsTutorial #Probability #LogOdds #eToThePower #ChurnPrediction #MathInDataScience #QuantifyOdds #ModelInterpretation #PredictiveAnalytics