STA2101: Methods of Applied Statistics I

and

STA442: Methods of Applied Statistics

University of Toronto, Fall 2012

Lecture Friday 2:10-5:00 p.m. Sidney Smith 2117

Office Hours Wednesday 11-1 and Friday 10-11 in SS6025

Instructor: Jerry Brunner

Note: This page is under construction, and will be updated frequently throughout the term.

Formula sheet is posted below. This will be used on the final exam.

Random/nested effects slide show is posted below.

• Course Outline
• Formula Sheet: This will be provided with the final exam, and also with Quiz 11 if necessary.
• About the Final Exam
• Computing Resources: Information and Links.
• Assignments
  1. Assignment 1 (Last updated Sept. 4th): Quiz on Friday Sept. 21st
  2. Assignment 2: Quiz on Friday Sept. 28th
  3. Assignment 3: Quiz on Friday Oct. 5th
  4. Assignment 4: Quiz on Friday Oct. 12th
  5. Assignment 5: Quiz on Friday Oct. 19th
  6. Assignment 6: Quiz on Friday Oct. 26th
  7. Assignment 7: Quiz on Friday Nov. 2
  8. Assignment 8: Quiz on Friday Nov. 9th
  9. Assignment 9: Quiz on Friday Nov. 16th
  10. Assignment 10: Quiz on Friday Nov. 23d
  11. Assignment 11: Quiz on Friday Nov. 30th
• Lecture Overheads You might want to print these and bring them to class so you can write notes on them during lecture. Some of the lectures are available in a handout version that has the same content but perhaps uses less paper.
  1. Brief Introduction to R
  2. Statistical Introduction
     • Lecture slides
     • Handout version
  3. Large Sample Tools
     • Lecture slides: Revised Sept. 14 after class
     • Handout version: Not revised
  4. Random Vectors Part One
  5. Random Vectors Part Two
  6. Bootstrap
  7. Bootstrap with R
  8. Likelihood Part One
  9. Likelihood Part Two: Wald and score tests
  10. Wald Tests with R
  11. Regression Part One
  12. Regression With R Part One: The cars data
  13. Some unix and emacs commands. You might as well print this handout and keep it with you while you work, at first.
  14. Brief Introduction to SAS
  15. Missing values in R
  16. Little Tubes data with SAS: We will only look at the first 4 pages of this handout for now.
  17. Logistic Regression Part One
  18. Logistic Regression With R Part One: Revised Nov. 1st
  19. Poisson Regression
  20. Poisson Regression With R Revised Nov. 9th before class.
  21. Generalized linear models
  22. Regression Part 2 Revised Nov. 9th before class
  23. SENIC data with SAS: Read from Excel spreadsheet, do data transformations and basic tests.
  24. Residuals: A brief discussion
  25. Regression on the Cars data with SAS: Testing parallel planes, lsmeans, Studentized deleted residuals
  26. Factorial ANOVA
  27. Factorial ANOVA with SAS
  28. Power Part 1 for regression and analysis of variance
  29. Matrix Power calculations with R
  30. The sample variation method
  31. Power estimation: A disaster
  32. Random effects and nested models
  33. Random effects and nested models with SAS
  34. Permutation and randomization tests
• Data sets from homework and lectures.
• Readings: All available free online
  • Statistical models by A. C. Davison. Get access using your UTorID. This is the place to look if you want the real truth about almost any applied statistical topic.
  • The White Whale: A comprehensive undergraduate textbook on regression and experimental design. If you want a simple, clear explanation of almost any standard topic in regression or experimental design (with a numerical example), this is the place to look. This is a 15MB zip file. It's well worth downloading.
  • Introduction to R by Venables, Smith and others. This free 100 page document is very helpful if you plan to do any serious work with R.
  • Data analysis with SAS: A free, open source undergraduate text emphasizing verbal rather than mathematical reasoning, with lots of SAS examples. It's a draft, still in preparation but possibly useful. The goal of the book is to supply plain, commom-sense explanations of certain applied statistical tools, along with explanation of how to do it with SAS and how to interpret the output.
    • Chapter 1: Vocabulary and basic concepts useful for thinking about data anaysis and communication with clients in consulting situations.
• Source Code for lecture overheads, homework and other class materials. It's mostly in LaTeX and Openoffice.org format.

All course materials prepared by Jerry (lecture overheads, homework problems, etc.) are licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License. Source code is available. See the Source Code link above for more information.