This is the first text in a generation to re-examine the purpose of the mathematical statistics course. The book's approach interweaves traditional topics with data analysis and reflects the use of the computer with close ties to the practice of statistics. The author stresses analysis of data, examines real problems with real data, and motivates the theory. The book's descriptive statistics, graphical displays, and realistic applications stand in strong contrast to traditional texts that are set in abstract settings.Important Media content referenced within the product description or the product text may not be available in the ebook version.
I felt like I'd been using "advanced" stats for ages without really knowing some fundamentals, so this was a great way to more intimately review things like Markov's inequality, Chebyshev's, the Law of Large Numbers (means are consistent...), the Central Limit Theorem (sums tend to a normal distribution), moment generating functions, maximum likelihood, Bayes, hypothesis testing as well as comparing multiple samples, t/F/Chi-squared distributions, and how to use Chi-squared to test for independence across categorical variables.
A bit dated: you'll find statements like (paraphrasing) "with the advent of computers simulation bootstrap is an exciting area", tables of values, and low-dimensional small scale scenarios. That said, still useful as a foundation.
This was my favourite math stats text at university. Covers a lot of material. I just used other texts if the proofs I needed weren't here... Still, this book does go through lot of proofs where needed. My 'go to' text. Brilliant!
Very nice text book that is good for both beginners and intermediate level. The amount of maths is just right that you can grasp the concept without being overwhelmed. There are lots of nice examples and good exercises too. If you come for statistics knowledge, you've come to the right place. Definitely would read again.
A readable introduction to the field of statistics and a great reference. However, the author's treatment, at least in the edition I have, is somewhat perfunctory. For instance, he seems to spend too much time on ANOVA's at the expense of the analysis of categorical data and multiple regression.
Good content, understandable, and plenty of examples. Organization of topics left something to be desired. Used for the first half of "Statistical Learning and Data Mining" course at the graduate level.