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Intro
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Introduction to Machine Learning: Regression - Part 1
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- 1 Intro
- 2 CONTENTS
- 3 ABOUT COMPASS LEXECON
- 4 WHAT IS STATISTICAL LEARNING?
- 5 VARIOUS APPLICATION OF STATISTICAL LEARNING
- 6 HOW-(NON)PARAMETRIC METHODS
- 7 WHEN DO WE CARE MORE ABOUT INFERENCE THAN PREDICTION
- 8 LINEAR REGRESSION AT COMPASS LEXECON
- 9 SIMPLE REGRESSION: PRICE VS COST
- 10 WHAT IS THE IMPACT OF COST ON PRICE?
- 11 SIMPLE LINEAR REGRESSION MODEL
- 12 ESTIMATION OF THE PARAMETERS BY LEAST SQUARES
- 13 MULTIPLE REGRESSION: CARTEL EXAMPLE
- 14 MULTIPLE REGRESSION AND CARTEL EXAMPLE-DUMMY VARIAB
- 15 CARTEL OVERCHARGE-BEFORE-DURING-AFTER COMPARISON
- 16 OVERCHARGE-CROSS-SECTIONAL COMPARISON
- 17 OVERCHARGE-DIFFERENCE-IN-DIFFERENCES ANALYSIS
- 18 INTERPRETING REGRESSION RESULTS
- 19 OVERALL MODEL ACCURACY-GOODNESS OF FIT
- 20 CONCLUSION: WHAT IS A GOOD MODEL (ACCORDING TO US)?
