The syllabus gives the course description, contact information, grading scheme, and other course information.
Fitting a linear model to data by least squares. (R code). (Annotations).
Confidence intervals and hypothesis testing. (R code). (Annotations).
Factors and the ANOVA F-test. (R code). (Annotations).
Discussion: STATS 401 and the future of undergraduate data science. (Feedback).
Additional topics in linear modeling. (R code). (Annotations).
Homework 0. Setting up your laptop. Due in lab on 9/7.
Lab 3. Basic Matrix Computations and the Linear Model. Slides for 9/21.
Lab 5. Quiz 1.
The style and format of the final exam will be based on the practice final exam below. The exam will work through a data analysis, asking questions to requiring understanding of the statistical techniques used, how they are computed, and how they are interpreted.
Practice final exam. Solutions. This is a past final exam that includes a question on collinearity which we may or may not cover.
The final exam will cover all material in Chapters 1-8 of the notes, as well as all homeworks and labs. The material will focus on material after Chapter 5, but this builds closely on Chapters 1-5. The first four topics from Chapter 9 will be included: review of random variables; fitting polynomial relationships using linear models; \(R^2\) and adjusted \(R^2\); collinearity.
The remaining three topics in Chapter 9 (interactions; model selection; more discussion of causation, observational studies and designed experiments) will not be directly tested. However, this material reinforces topics from earlier in the class and so may come in useful for the final exam. You are advised to follow this material to the extent it is discussed in lectures.