Note added August 2013:
Data Analysis for Politics and Policy is now available as an .pdf ebook here.
Here is an a review of the book:
My book Data Analysis for Politics and Policy was published by Prentice-Hall in 1974; after 15 printings, it is still in print, barely. It is available at amazon.com. There was a fairly detailed review of the book around 1975 or 1976 in the Journal of the American Statistical Association; the book was widely used as a college text.
The 179-page book is for courses in applied statistics, particularly for policy making and the social sciences. It deals with making causal inferences from statistical evidence, research designs, predictions and projections, linear and multiple regression. All the examples are real, involving serious questions (no regressions of height on weight that are found in some statistics texts). The technical material is at the level of one or two classes in college math. There is one somewhat more technical part, on logarithmic scale transformations and their interpretation in regression and in graphics. The book was written very much under the influence of Frederick Mosteller, John Tukey, and my professor of statistics at Stanford, Lincoln Moses.
I used the book for years at Princeton and Yale in teaching both undergraduate and graduate classes in Evidence for Policy, Basic Statistics, Introduction to Social Science Methods, and the like.
"Data Analysis" means that the book is about how to examine data to reach sensible conclusions, figure out causality, and make decisions; there is virtually nothing in the book about probability models and significance testing, issues that are studied in "Statistics" courses.
The style of the book is somewhat like the chapter on the cholera epidemic and the space shuttle Challenger in my Visual Explanations, except there are equations and lots of regression analysis in Data Analysis for Politics and Policy. I have not revised the book since it was published in 1974; some examples are antique but the ideas in the book are surely timeless.
-- Edward Tufte