Today, Touro asks, “Prof. Metric, I heard that the linear regression technique only requires a model to be linear in parameters. What does that mean?” Booka then says, “I also have a question. In the previous chapters, we assumed that the data did not have any problems. What will happen if they violate one of the classic assumptions?” Prof. Metric praises them for raising good questions and tells us that several of these issues will be discussed this week. We learn that once we finish with the chapter, we will be able to:
1. Master simple model issues in regressions;
2. Explain the nature and consequences of the heteroscedasticity problem;
3. Obtain the corrected standard errors and the ...