In this lesson, we will learn about two important extensions to the standard linear regression model that we have discussed.
In the first part of the lesson, we will discuss the weighted least squares approach which would be useful in estimating regression parameters when heteroscedasticity is present.
In the second part of the lesson, we will turn to a class of regression models that we can use when our response variable is binary.
Lesson 13 Objectives
- Explain the idea behind weighted least squares.
- Apply weighted least squares to regression examples with nonconstant variance.
- Apply logistic regression techniques to datasets with a binary response variable.