Ken Butler 2019-10-11
This book will hold a collection of problems, and my solutions to them, in applied statistics with R. These come from my courses STAC32, STAC33 and STAD29 at the University of Toronto Scarborough.
The problems were originally written in Sweave (that is, LaTeX with R
code chunks), using the exam document class, using data sets stolen
from numerous places (textbooks, websites etc). I wrote a Perl
program
to strip out the LaTeX and turn each problem into R Markdown for this
book. You will undoubtedly see bits of LaTeX still embedded in the text.
I am trying to update my program to catch them, but I am sure to miss
some.
I just figured out that you can convert LaTeX “label” and “ref” pairs into HTML “a name” and “a href=‘#’”, which R Markdown can handle. I am ludicrously pleased with myself. To that effect, you will occasionally see question parts beginning with a *; this means that other question parts refer back to this one. (One of my favourite question strategies is to ask how two different approaches lead to the same answer, or more generally to demonstrate that there are different ways to see the same thing.)
If you see anything, file an issue on the Github page. I will take pull requests and will acknowledge all the people who catch things. Likely problems include:
- some LaTeX construction that I didn’t catch (eg. block quotes)
- disappeared footnotes (that will show up as an apparently missing sentence in the text)
- references to “in class” or a lecture or a course by course number, which need to be eliminated (in favour of wording like “a previous course”)
- references to other questions or question parts that are wrong (likely caused by not being “labels” or “refs” in the original LaTeX)
- my contorted English that is difficult to understand.
As I read through looking for problems like these, I realize that there ought to be a textbook that reflects my way of doing things. There isn’t one (yet), though there are lecture notes. Reasonably recent versions of these are at:
A little background:
STAC32 is an introduction to R (and also SAS) as applied to statistical methods that have (mostly) been learned in previous courses, for example a non-mathematical applied course like this. The idea is that students have already seen a little of regression and analysis of variance (and the things that precede them), and need only an introduction of how to run them in R.
STAC33 covers similar ground to STAC32. It is intended for students with a more mathematical background, and will include some extra material beyond what is in STAC32 (eg., bootstrapping, Bayesian statistics with Stan).
STAD29 is an overview of a number of advanced statistical methods. I start from regression and proceed to some regression-like methods (logistic regression, survival analysis, log-linear frequency table analysis), then I go a little further with analysis of variance and proceed with MANOVA and repeated measures. I finish with a look at classical multivariate methods such as discriminant analysis, cluster analysis, principal components and factor analysis. I cover a number of methods in no great depth; my aim is to convey an understanding of what these methods are for, how to run them and how to interpret the results. Statistics majors and specialists cannot take this course for credit (they have separate courses covering this material with the proper mathematical background). D29 is intended for students in other disciplines who find themselves wanting to learn more statistics; we have an Applied Statistics Minor program for which C32 and D29 are two of the last courses.
- bring in the D29 questions
- working on A4 D29
- look for multiple regression Qs in A6 or wherever