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The revision of this well-respected text presents a balanced approach of the classical and Bayesian methods and now includes a chapter on simulation (including Markov chain Monte Carlo and the Bootstrap), coverage of residual analysis in linear models, and many examples using real data. Calculus is assumed as a prerequisite, and a familiarity with the concepts and elementary properties of vectors and matrices is a plus.
The revision of this well-respected text presents a balanced approach of the classical and Bayesian methods and now includes a chapter on simulation (including Markov chain Monte Carlo and the Bootstrap), coverage of residual analysis in linear models, and many examples using real data. Calculus is assumed as a prerequisite, and a familiarity with the concepts and elementary properties of vectors and matrices is a plus.
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The 4th Edition of Probability and Statistics by Morris H. DeGroot, Mark J. Schervish Review
This is an outstanding book for those with a strong math background (some calculus might be enough to get by, but more is definitely better). It covers everything that one would learn in a one-year statistics course and more, including lots of sections on Bayesian methods. I have compared this book to many others and again and again this one comes out best. Theory is rigorous, but there are lots of practical examples too. Answers to odd numbered exercises are supplied. The only shortcoming I've found has also been pointed out in other reviews: there are too many references to previous sections and examples. You often have to go back and reread previous material to refresh your memory in order to understand a section or work on an exercise.