1. Summary
Congratulations on making it to the end of the course! Let's summarize what you've learned.
2. Linear regression as model
As seen in other courses, linear regression is a modeling technique. And the model you estimate is one that describes a population. To have confidence in your estimate, the technical conditions need to be checked, and any other variable relationships that might impact your conclusions should also be considered.
3. Linear regression as an inferential technique
As with the other inferential methods covered in this course sequence, the inferential analysis of a linear model can be approached as a hypothesis test or a confidence interval. The mathematical model provides one framework for the analysis and computational modeling (that is, randomization tests and bootstrapping) give an alternative way of producing inferential analyses.
All are valid methods to use, and you should feel comfortable at this point using all of them.
4. Let's practice!
Thank you for spending your time working through ideas of inference on linear models. I hope you've had as much fun as I have working through the examples. I'll see you next time!