Teaching Statistics
19 Jul 2009 14:25
Doing this is now, officially, what I am paid for. I am basically unembarrassed about doing this while never having taken a statistics class --- after all, I do statistical research, so it's not exactly like a celibate man offering advice on marriage --- but I do want to do it better.
One thing which particularly concerns me is that almost all the introductory textbooks I run across seem like either cookbooks, or lower and distorted forms of Cramér's Mathematical Methods of Statistics. Cramér's book is great, but giving a debased version of it to engineers or social scientists doesn't seem all that effective. So I'm interested in good approaches to teaching statistics as a way of learning about the world from data, not a set of rituals or a calculational exercise in basic probability theory. If they do a good job of teaching about computer-intensive methods and applied probability, so much the better.
In fact, what I'd really like is for somebody to write a popular book on "better living through data analysis". I wish I could say that Freakonomics was that book, but it isn't.
- Recommended:
- Larry Gonick and Woollcott Smith, The Cartoon Guide to Statistics
- D. Huff, How to Lie with Statistics
- Larry Wasserman, All of Statistics
- Recommended second or secondary books (i.e., ones with too few
technicalities to be self-contained, first-reading texts):
- Robert P. Abelson, Statistics as Principled Argument ["Author's note: There is a Robert P. Abelson who sings in the Yiddish theater in New York. Although theatrically inclined, I cannot (alas) claim to be that person also."]
- Richard A. Berk, Regression Analysis: A Constructive Critique [My comments]
- Recommended, misc.:
- Nathan Moore, Nicole Schoolmeesters, "Computational Physics and Reality: Looking for Some Overlap at the Blacksmith Shop", arxiv:0904.3960 [This sounds like it might also work for a course in stochastics...]
- To read:
- Murray Aitkin, Brian Francis, John Hinde and Ross Darnell, Statistical Modelling in R [Blurb]
- Benjamin M. Bolker, Ecological Models and Data in R [blurb, intro]
- F. M. Dekking, C. Kraaikamp, H. P. Lopuhaä and L. E. Meester, A Modern Introduction to Probability and Statistics: Understanding How and Why [Blurb]
- Finkelstein, Smith and Levin, Statistics for Lawyers ["Despite its pedestrian title, it is not a routine statistics text with legal examples tossed in. The selection of topics and examples, as well as the exposition of statistics and law, is erudite, informed, and even entertaining." --- or so says the review quoted by Springer Verlag]
- Andrew Gelman and and Deborah Nolan, Teaching Statistics: A Bag of Tricks
- Phillip I. Good, Resampling Methods: A Practical Guide to Data Analysis
- Phillip I. Good and James W. Hardin, Common Errors in Statistics (and How to Avoid Them)
- Dana K. Keller, The Tao of Statistics: A Path to Understanding (With No Math)
- Gary King, Robert O. Keohane and Sidney Verba, Designing Social Inquiry: Scientific Inference in Qualitative Research [Blurb, preface, ch. 1]
- Ben Klemens, Modeling with Data [website with draft text. Looks interesting and I like the idea of integrating it with computing, and with databases. (But I've forogtten almost everything I knew about databases.)]
- Neil J. Salking, Statistics for People Who (Think They) Hate Statistics
- Aris Spanos, Probability Theory and Statistical Inference
- Jefferson Hane Weaver, Conquering Statistics: Numbers Without the Crunch
