The statistics with R course is going on full at this point. And by that, I mean I’m just spending most of the day reading and working through the examples in the book. In the first week (plus a couple days because I started early), I’ve worked my way through 12 of the 19 chapters. It’s not really “hard” material (at least, not yet). And it helps that the book has a companion website where you can download chunks of code and data sets instead of having to manually type stuff in.

A fair amount of the material is new to me. I have some vague senses of some of these ideas, but I’ve never really formally worked with them or had to think about them. The book is very much a practitioner’s book and not a theorist’s book, and so a lot of mathematical details are either touched upon lightly or not at all. For example, I can see hints of linear algebra concepts in the use of contrasts (concepts like the orthogonality of contrasts being important to the proper interpretation the partition of the sum of squares errors, which leads to the proper interpretation of the F-ratios), but I’m kind of left to speculate on those things.

But I do like the book in that this will be a good reference to have around. The examples he uses are simple enough to be understood, but he also takes the time to walk through the details of the analysis in a way that I think would be relatively easy to extend to other situations. I’m taking in a lot of this stuff because these are the types of tools I would want to have to do the analysis of the ATMI data.

But for now, all of that is on hold while I try to absorb all of this information. I hope to have gone through the whole book by the end of next week.