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Introduction to Mathematical Statistics 6th Edition

2.8 out of 5 stars 59 customer reviews
ISBN-13: 978-0130085078
ISBN-10: 0130085073
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Editorial Reviews


"The writing style is exceptionally clear; also in the more advanced portion, I haven't any reservations about use. It is a much more professional and modern text than ours. I would seriously consider adopting the text if I teach it again and shall suggest it to my colleagues." — Walter Freiberger, Brown University

"The Hogg/McKean/Craig revision stands out as a modernized version of Hogg and Craig. This revision enhances considerably the statistical inference part with discussion of new procedures and methods, and puts in perspective a broad array of modern statistical methods." — Hosam Mahmoud, George Washington University

From the Publisher

Exceptionally clear and impeccably accurate, the fifth edition of this trusted text offers a careful presentation of the probability needed for mathematical statistics and the mathematics of statistical inference. Offering a strong background for those who wish to go on to study statistical applications or more advanced theory, this text presents the most thorough treatment of the mathematics of statistics of any competing text. --This text refers to an out of print or unavailable edition of this title.

Product Details

  • Paperback: 692 pages
  • Publisher: Pearson; 6th edition (June 27, 2004)
  • Language: English
  • ISBN-10: 0130085073
  • ISBN-13: 978-0130085078
  • Product Dimensions: 7 x 1.6 x 8.9 inches
  • Shipping Weight: 2.4 pounds
  • Average Customer Review: 2.8 out of 5 stars  See all reviews (59 customer reviews)
  • Amazon Best Sellers Rank: #749,790 in Books (See Top 100 in Books)

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Customer Reviews

Top Customer Reviews

By Michael R. Chernick on February 15, 2008
Format: Hardcover
Hogg and Craig is one of my favorite texts. It is an intermediate text in mathematical statistics similar to Mood, Graybill and Boes. I took qualifying exams in mathematics for my Masters Degree at the University of Maryland in the early 1970s. One of the exams I took was in statistics. I had little formal training in statistics at the time. Hogg and Craig was the recommended text for the statistics exam. So I bought it and studied out of it on my own. It was very clear with excellent coverage of methods for deriving distributions for random variables and transformations of random variables. I passed my exams and got my highest grades on the statistics exam even though I had more training in abstract algebra. Hogg and Craig really helped. It has been revised since then to maintain currency with statistical developments but it still has maintained its clarity and usefulness. Most of the other reviews that are critical of it are way off base. I am sure that efforts have been made with the numerous revisions to keep the material up to date. Perhaps some critics are correct that it comes up short on some modern advances in Bayesian statistics and other computer-intensive statistical methods. But that should not tarnish its reputation as a classic text in mathematical statistics.
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Format: Paperback
There muliple starting points that could generate your interest and need for this book. If you are a math undergrad major, and this is your required reading, stop here, go get it, use it and probably sell second hand -- you won't be doing that much statistics anyway. If you are a grad student with a major other than statistics, and this is a required reading for a class in statisitical inference you are taking at a local Stat department, stop here and go get it anyway; it won't hurt to have it. Everybody else, welcome to continue...

I am now teaching a semester class on introduction to probability theory (the first class in two semester sequence) using this book, and I don't like it very much. It has a little bit strange audience in mind: students who barely have enough math background to do statistics, just the standard 3 semester calculus sequence, but no real analysis and no complex analysis. If you do statistics for living, or consider doing that, you need something more serious measure-theory based (at least that's how I was taught in my grad program, and I see huge advantages in looking down at probability theory from the measure theory prospective).

In other words, it is one of the few books that fill in the gap between all those colorful but very limited and boring "Probability and Statistics for Housewives" the-only-math-class-for-my-general-college-requirement books that steer away from calculus and call a cdf "area under the curve", on one side; and Cramer's Mathematical Methods of Statistics or Kendall/Stuart's
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Format: Paperback
I am a bit puzzled by the numerous low reviews of this book. I suspect that these reviews may be more due to the fact that this book is being mis-used in courses than due to anything lacking in the book itself. This book requires a certain mathematical sophistication and background, and would be absolutely inappropriate for students without this background. My only criticism of this book is that it is strictly classical. It does not explore the Bayesian way of looking at things, does not make any connections to information theory. Although it covers a few nonparametric techniques, it does not integrate them into the discussion from the beginning. Also, there is not as much discussion of theory.

This book is a classic book on statistics. It has been updated with a new author, but the book is essentially the same as the original by Hogg and Craig. It is a good book, but it is hardly modern. Nowadays, this book is being replaced by books like the Casella and Berger. Honestly? I find the Casella and Berger to be inferior to this book. This book may come across as a bit more dry at first, but as one gets into the later chapters, it remains its clarity, whereas the Casella and Berger loses it. In addition, I think the Casella & Berger is more caught up in tedious manipulations than this book is. The coverage of topics is similar. I also find the exercises in this book to be better.

What does this book require? This is not an introductory book, even to a mathematician. I would recommend a prior course in probability, and a number of other rigorous math courses; a course in mathematical analysis/advanced calculus would be helpful.
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Format: Hardcover
I worked with an earlier version of this text. The text is
geared for math majors. There are many practical examples;
however, the more theoretical examples are elusive at best.
Coverage of the basic laws of probability is good. The examples
dealing with continuous random variables require some prior
knowledge of intermediate calculus which should be no problem
for math majors or engineers. The book benefited me when I
took and passed the Fundamentals of Engineering Examination.
In addition, I've taught statistics several times.
This work is geared for the above-average student. In some
areas, it could be supplemented with the Schaum's Statistics
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