Linear Regression with One Regressor (FRM Part 1 2023 – Book 2 – Chapter 6)

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After completing this reading, you should be able to:
- Explain how regression analysis in econometrics measures the relationship between dependent and independent variables.
- Interpret a population regression function, regression coefficients, parameters, slope, intercept, and the error term.
- Interpret a sample regression function, regression coefficients, parameters, slope, intercept, and the error term.
- Describe the key properties of a linear regression.
- Define an ordinary least squares (OLS) regression and calculate the intercept and slope of the regression.
- Describe the method and three key assumptions of OLS for estimation of parameters.
- Summarize the benefits of using OLS estimators.
- Describe the properties of OLS estimators and their sampling distributions, and explain the properties of consistent estimators in general.
- Interpret the explained sum of squares, the total sum of squares, the residual sum of squares, the standard error of the regression, and the regression R2.
- Interpret the results of an OLS regression.

0:00 Introduction
1:22 Learning Objectives
2:29 What is Regression Analysis?
3:53 Scatter Plot
5:31 Simple Linear Regression Model
8:15 Error Term
9:31 Ordinary Least Squares (OLS) Intercept and Slope of the Regression
10:48 OLS Assumptions
12:53 Ordinary Least Squares (OLS) An Example
14:22 Benefits of using OLS estimators
20:25 Coefficient of Correlation
22:04 Standard Error of the Regressor
23:05 Book 2 - Quantitative Analysis Chapter 5