STA 2101/442: Methods of Applied
Statistics
University of Toronto,
Fall 2018
Instructor:
Jerry Brunner
Lecture Friday
2:10-5:00 in Northrop Frye Hall (73 Queen's Park Crescent East), Room 003
Office Hours Wednesday 11:10-2:00 in Sidney Smith 2112.
Note: This page is under construction, and will be updated frequently
throughout the term.
Review materials for final exam
The formula sheet for the final exam is posted below.
Extra office hours for the final exam.
- Wed Dec 12th 11-2 in Sidney Smith 1086
- Tues Dec 18 2-4 in Sidney Smith 1074
- Wed Dec 19 1-4 in Sidney Smith 1084
Midterm solutions
Description of the project for graduate students is at the very bottom of this page.
- Course Outline
- Formula Sheet
- Computing Resources: Information and Links.
- Assignments
- Assignment One (Review)
- Assignment Two
- Assignment Three
- Assignment Four
- Assignment Five
- Assignment Six
- Assignment Seven
- Assignment Eight
- Assignment Nine
- Lecture Overheads
- A Frequentist Introduction
- The Zipper Example
- Large Sample Tools
- Random Vectors
- Testing a non-linear hypothesis in regression with R
- Some comments on linear regression
- Omitted variables and instrumental variables
- A Bayesian Introduction
- Likelihood Part One
- Likelihood Part Two
- Wald Tests with R
- Bayesian Computation: Markov chain Monte Carlo with rjags
- Logistic Regression
- Logistic Regression with R
- Poisson Regression
- Poisson Regression with R
- Multinomial Logit Models
- Multinomial Logit Models with R
- Large sample target of least-squares estimates in regression
- Experiments in imputation of missing values
- Prediction intervals
(Handwritten)
- Prediction intervals with R
- Regression and prediction using the Math data
- Machine learning basics
- k nearest neighbour regression on the Math data
- Interactions and factorial ANOVA
- Interactions in regression with R
- Factorial ANOVA with R
- Fractional factorials
- Analysis of normal within-cases data
- Within-cases normal data with R
- Analysis of binary within-cases data
- Within-cases binary data with R
- Textbooks: The texts are 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.
-
Linear models with R (2009) by J. Faraway.
- 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.
- Introductory movies (substitute for Lecture 1)
- About the course
- A frequentist introduction
- The zipper example
- Large sample tools, Part one
- Project for graduate students
All course materials prepared by Jerry are licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License. Source code is available above.