I continue reading / skimming my way through Gelman and Hill's Data Analysis Using Regression and Multilevel/Hierarchical Models. I very much like the tone of the book. It is practical... not doctrinaire. Plenty of examples. R code where you need it. Confidence intervals are plus or minus 2 standard errors, not 1.96 or whatever Student's t requires. Think about scales and units and log transformations... Don't just think about them: Try them out! Mess around with the data. Make some plots comparing your confidence intervals. Questions of causation are yet to come, but I anticipate that Gelman and Hill are not structural purists, nor identification cops. They want you to think about your problem, know your data, and especially be aware of other related results.
Everything has been comfortably familiar until the chapter on simulation of probability models. Toto, we're not in Kansas anymore... welcome to the land of Bayes.
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