Probability and Statistics

by Morris H. DeGroot, Mark J. Schervish

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.

Genres: Mathematics, Nonfiction, Textbooks, Reference, Academic, Finance, Economics

912 pages, Hardcover

First published December 1, 1975

Reviews

[Dmitri · Rating 5 out of 5]

I've actually gone through several mediocre Statistics books before settling on this one. They all seem to be identical from the outset, but this one explains things a little bit more clearly than the others.

The only thing I didn't like, apart from the huge-page format and the annoying "International Edition" crap on the front was the fact that certain key ideas were explained on the basis of examples and even exercises that were seen previously. This required a lot of back-and-forth flicker, which I found to be annoying. (And impossible if you are on an e-reader.)

Overall, I would recommend this as a general-purpose beginner stats book.

[Henne · Rating 3 out of 5]

The only reason to read this in [insert current year] is if you have to do so. I honestly don't think anyone would read this by personal choice.

Eventually, someone will write the Great American Computational Statistics Book*; until then students will be forced to learn the basics as our elders did many decades ago and then unlearn all of it as soon as they meet modern machine learning and Bayesian data analysis.

I should say at least something positive about the book: DeGroot does win some points over e.g. Wasserman for actually understanding what Bayesian inference is good for. Oh and also the cover of this edition is pretty cool looking.

(*The best contender for a GACSB is probably James et al, An Introduction to Statistical Learning , but it loses marks from me for not having both R and Python in the text and exercises. Alternatively there's Gelman et al which, while great, suffers from the same problem.)

[Moon Na · Rating 4 out of 5]

I used this book while attending the introduction to statistical method for economists course in my bachelor, however, just finished it recently. De Groot wrote it in detailed and clear way. Among hundred books in this field, it's the best choice to start struggling with prob and of course, stat.
However, personally, some parts, especially proofs and descriptions, are unnecessarily intuitive and pretty lengthy. It seems the only point of this book I don't like.

[Emma Bossuyt]

The only subject of 11 I passed this year (first year civil engineering) lol

[Bubu · Rating 3 out of 5]

for Statistics in Applications 2021 Fall

[Dr_Hope · Rating 4 out of 5]

This book has a very coherent set of examples and homework questions so that even if you don't go to lectures, you are still able to read by yourself and solve the homework questions from the book.

However, it doesn't cover all the necessary aspects on every topic and it doesn't have a very clear structure of the content inside each chapter.

Using it as a dictionary is a good idea. But you have to digest and reorganize everything by yourself

[Bob · Rating 4 out of 5]

Used as textbook by Robert Redinger, Lecturer, Graduate School of Industrial Administration, Carnegie Mellon University for Probability and Statistics course sequence for first year MSIA (MBA) students, 1980-81.

[Ben · Rating 5 out of 5]

Best book I have read on both subjects. Just the right amount of math for someone that knows calc (doesn't need a hand held), but isn't a mathematicians (very simple proofs).

[Dayton Outar · Rating 3 out of 5]

This can be a difficult read. The exercises are pretty good. However, I think more work is required from the Author and the Publishers to make the information easier to understand.