Linear Regression with Multiple Regressors (FRM Part 1 2023 – Book 2 – Chapter 8)

Master Multiple Linear Regression for FRM Part 1 Book 2 Chapter 8 with Prof. Forjan. We move from simple to multiple regression and cover key exam topics: omitted variable bias, OLS assumptions, homoskedasticity vs heteroskedasticity, multicollinearity, and the difference between R-squared and Adjusted R-squared. We also touch on the Ramsey RESET test and a clear GDP example.

You will learn to:
Build and interpret multiple regression models
Detect omitted variable bias and multicollinearity
Read OLS output and explain coefficients
Judge fit with R-squared and Adjusted R-squared

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After completing this reading you should be able to:
Define and interpret omitted variable bias, and describe the methods for addressing this bias.
Distinguish between single and multiple regression.
Interpret the slope coefficient in a multiple regression.
Describe homoskedasticity and heteroskedasticity in a multiple regression.
Describe the OLS estimator in a multiple regression.
Calculate and interpret measures of fit in multiple regression.
Explain the assumptions of the multiple linear regression model.
Explain the concepts of imperfect and perfect multicollinearity and their implications.

0:00 Introduction
1:03 Learning Objectives
3:55 Addressing Omitted Variable Bias
4:53 Single Vs. Multiple Regression
5:59 Multiple Linear Regression
6:57 OLS Estimators in Multiple Regression
7:47 Assumptions of Multiple Regression
9:22 Multiple Regression Model An Example
10:43 Homoskedasticity vs. Heteroskesdasticity
13:35 Adjusted R2
16:27 Perfect Multicollinearity
18:42 Detecting Multicollinearity
19:27 Consequences of Multicollinearity
20:32 Book 2 - Quantitative Analysis Chapter 8

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