Office Hours Wednesday 12:10-2:00 in 69 Wetmore Hall (300 Huron Street) and Friday 11:10 - 12:00 in Sidney Smith 2119.
Note: This page is under construction, and will be updated frequently
throughout the term.
The last lecture slide set is now posted. Have a good holiday!
Some time in the new year, this page will move down a level, to a directory called oldclass. The address will be
http://www.utstat.toronto.edu/~brunner/oldclass/2101f19
- Course Outline
- Formula Sheet
- Computing Resources: Information and Links.
- Assignments
- Assignment One (Review)
- Assignment Two (Linear algebra review and some large-sample material)
- Assignment Three (Univariate delta method, random vectors, regression)
- Assignment Four (Applied regression, maximum likelihood)
- Assignment Five (Logistic regression random explanatory variables)
- Assignment Six (Omitted variables and measurement error)
- Assignment Seven (Double measurement regression)
- Assignment Eight (Instrumental variables, General model)
- Assignment Nine (General Model, Factor analysis)
- Lecture Overheads
- Introduction
- The Zipper Example
- Large Sample Tools
- Random Vectors
- Testing a non-linear hypothesis with R
- Regression Part 1: Quick and very applied
- Regression with R
- Likelihood Part One
- Likelihood Part Two
- Logistic Regression
- Logistic Regression with R
- Random Explanatory Variables
- Omitted Variables and Instrumental Variables
- Studying mis-specified regression models
- A first try at including measurement error
- Measurement error in the response variable
- Double Measurement Regression: Part One
- Simple Double Measurement Regression with R
- Double Measurement Regression: Part Two
- Double Measurement Regression on the BMI data with R
- Instrumental Variables Again
- The General Structural Equation Model
- The Brand Awareness Study
- Exploratory Factor Analysis
- Exploratory Factor Analysis with SAS
- Confirmatory Factor Analysis: Part 1
- Confirmatory Factor Analysis: Part 2
- Identifiability Rules
- Identifiability Rules in outline form
- Apply Rules
- Textbooks: The texts are optional, and mostly for background reading in case you need some review, or you want to go beyond what is covered in lecture. They are available in PDF format free of charge.
- Structural equation models: An open textbook. This is a textbook I'm working on. It has substantial overlap with course material.
Here are some parts of the book, in the order we will use them.
- Linear models in statistics
(2008) by A. C. Renscher and B. G. Schaalje. A strong masters level regression text with a good linear algebra review.
- Statistical
models (2003) by A. C. Davison. This is the place to look if you want
the real truth about almost any applied statistical topic.
-
Introduction to R by Venables, Smith and
others. This free 100 page document is very helpful if you
plan to do serious work with R.
All course materials prepared by Jerry are licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License. Links to the source code are given above.