**Click here to read the chapter (link works only for UC affiliates)**

**Lecture Slides: **Powerpoint PDF

**Learning Objectives**

- Derive the OLS estimator of the regression coefficients when there are two or more right-hand variables in the model
- Fit a multiple regression model using the least-squares criterion
- Identify the conditions under which a multiple regression estimate is the same as the simple regression estimate
- Interpret a multiple regression coefficient

**Examples**

- 20 California Schools (multiple regression)

**What We Learned**

- How to solve the least-squares problem to fit a multiple regression model.
- Multiple regression estimates differ from simple regression estimates if the right-hand-side variables are correlated with each other.
- How to apply the multiple regression least-squares formula using a spreadsheet.
- How to interpret a multiple regression coefficient
- R-squared increases when you add an X variable to a model