In the first chapter, you will understand how and why to perform inferential (instead of descriptive only) analysis on a regression model.
In this chapter you will learn about the ideas of the sampling distribution using simulation methods for regression models.
In this chapter you will learn about how to use the t-distribution to perform inference in linear regression models. You will also learn about how to create prediction intervals for the response variable.
Additionally, you will consider the technical conditions that are important when using linear models to make claims about a larger population.
This chapter covers topics that build on the basic ideas of inference in linear models, including multicollinearity and inference for multiple regression models.