Mathematical Statistics and Data Analysis

I work in a large corporation in a technical job. My primary motivation for buying this text book was to apply statistics to practical work-related problems. I loved this book. I was also amazed to see all the negative reviews. My theory for the negative reviews is that many students have no conception what a real world application of statistics is all about. They think that if they get a job in industry; the statistics problems they encounter involves nothing beyond picking up their favorite statistics book, finding an exact solved example of the problem in the text, copy the formual from the example and then plug in some numbers and you are done!

This is probably the best textbook on mathematical statistics for students with a good lower-division background in mathematics (calculus, multivariable calculus, linear algebra). Once the course is over, it makes for an excellent reference. I refer to it often for maximum likelihood, non-parametric tests, least squares theory, etc. Although the title contains the word "mathematical," the book has an eminently practical orientation. There is even an entire chapter devoted to descriptive statistics and graphical tools. A course making good use of this book will give the student a solid introduction to the art of data analysis. This book may be too advanced for students without sufficient mathematical preparation. Such students might warm up first with Friedman et al. or Moore and McCabe. The chief difference between the second and third editions seems to be that the latter gives somewhat more space to the Bayesian approach.

This book is truly excellent. Don't believe the bad reviews. This book provides, by far, the fastest way to obtain a solid foundation in elementary statistics. It is well-written and very clear. It is written for those who need to know enough about statistics to be able to use it for simple data analysis.

This book is an introductory-level statistics textbook for people who are learning statistics for the first time, and who know some math but are not math wizards. I am outright shocked at the low reviews given to this book. I tend to be highly critical of statistics textbooks, especially ones at this level. In my opinion, this book does an outstanding job. It balances making the material accessible with going into considerable depth, and it integrates mathematical theory with an emphasis on data analysis. The title of this book says it all: "Mathematical Statistics and Data Analysis". There are few other books that emphasize both theory and practice. This book certainly lives up to its title in this respect. Perhaps some of the bad reviews are from frustrated students who are in classes that are moving too quickly for their level. Just because this is an excellent book doesn't mean that every professor will use it in an appropriate way. The exercises in this book can be tough and I think it would be possible to easily overwhelm students by assigning too many problems or problems that are too difficult. This book is only really useful if you are able to explore it at a slower pace, reading it and reflecting on it. --- This book is not concise but it is not overly wordy. Rice is an excellent writer. The expanded discussion communicates aspects of the subject that are often overlooked, and helps build the students' intuition. This book is always readable and it is clearly written to be read and understood. In addition to communicating basic concepts, the book also explores numerous practical and philosophical considerations. For a book at such an introductory level, this text is remarkably deep. One of the most attractive aspects of this book is that it is relatively easy to skip around in it. More advanced students or students with some exposure to probability will necessarily want to skip many of the early chapters. Unlike some of the more advanced texts (such as the Casella and Berger) where the chapters depend on and reference each other in a rather rigid fashion, this book is more flexible and thus can appeal to students with diverse backgrounds. My last bit of praise is the integration of data analysis with mathematical theory. Distributions are introduced with a rich discussion of where, how, and why they arise in practice. Data analysis techniques, tests, and abstract definitions alike are introduced in such a way that they are rooted both to the abstract theory and to consideration of applications: this book is one of the rare texts that fully bridges the gap between the real world and the abstract models used to describe it. I would recommend this book for a wide variety of different uses. This would make an outstanding textbook and it could easily be used for more than one course, or for courses at more than one level. It is a useful book to have on the shelf as a basic reference, and it is also very useful for