Tutorials Thursday 6:10-7:00 p.m. in DV 2105F
Note: This page is under construction, and will be updated frequently throughout the term.
Information about the final exam including a link to the data set.
Assignment 10 Solutions are posted. The assignment itself is significantly cleaned up too.
- Syllabus
- Computing Resources: Information and Links. Go here to download R.
- Formula Sheet
- Current Formula Sheet (for Quiz 9): PDF View LaTeX Download LaTeX
- Assignments
- Assignment One (all review): Quiz on Thursday Sept. 14th in tutorial.
- Assignment Two: Quiz on Thursday Sept. 21st in tutorial.
- Assignment Three: Quiz on Thursday Oct. 5th in tutorial.
- Assignment Four: Quiz on Thursday Oct. 19th in tutorial.
- Assignment Five: Quiz on Thursday Oct. 26th in tutorial.
- Assignment Six: Quiz on Thursday Nov. 2nd in tutorial.
- Assignment Seven: Quiz on Thursday Nov. 9th in tutorial.
- Assignment Eight: Quiz on Thursday Nov. 16th in tutorial.
- Assignment Nine: Quiz on Thursday Nov. 23d in tutorial.
- Assignment Ten: Quiz on Thursday Nov. 30th in tutorial.
- Lectures Parts of the lectures are on overheads; the rest will be hand-written on the board.
- Statistical introduction.
- Introduction to R: See Appendix B of Linear models with R for another introduction.
- Overview of Chapter 1 of Regression Analysis
- Moment-generating functions: See your STA256 text.
- More linear algebra. See Chapter Two of Linear models in statistics for more detail. Please notice the slide entitled "Three mistakes that will get you a zero."
- Random vectors. See Sections 1 and 2 of Appendix B in Regression Analysis, the main text. Chapter 3 in the supplementary text Linear models in statistics is better, but all you really need is the lecture material.
- Multiple Regression: Least squares and the Gauss-Markov Theorem. Handwritten. This unit corresponds to Chapter 2 in the text.
- 2016 version: This is what was posted previously.
- 2017 version: Better in some ways, worse in others.
- Least squares with R
- The multivariate normal distribution
- Inference Part One: Distribution theory for tests and confidence intervals. This corresponds to the first part of Chapter 3 in the text.
- Interpretation: What it means and how to talk about it
- Inference with R
- Categorical independent variables
- Categorical independent variables with R
- Prediction Intervals: Handwritten on board
- Prediction Intervals and confidence intervals with R
- Analysis of Residuals
- Residual diagnostics with R
- PDF OpenOffice.org format Download (including graphics files)
- Weighted and Generalized least squares: Something similar to this was handwritten on the board
- Weighted least squares with R
- Influential observations: Handwritten on board
- Detecting influential observations with R
- Centered IVs and Polynomial Regression: More or less what was handwritten on the board.
- Centered independent variables with R: A quick demonstration
- Random Independent Variables
- Omitted Variables and Instrumental Variables
- Stepwise variable selection with R
- Secondary (optional) Textbooks: These are available free in pdf format through the U of T library one chapter at a time. Full copies may also be downloaded below.
- Linear models with R by J. Faraway.
- Linear models in statistics by A. C. Renscher and B. G. Schaalje.
All course materials prepared by Jerry are licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License. Links to the source code appear above.