Principles of Statistics (Dover Books on Mathematics) , Kindle Edition

3.9 out of 5 stars 42 customer reviews

ISBN-13: 978-0486637600
ISBN-10: 0486637603
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Product Details

  • File Size: 9326 KB
  • Print Length: 256 pages
  • Publisher: Dover Publications (April 26, 2012)
  • Publication Date: March 29, 2012
  • Sold by: Amazon Digital Services LLC
  • Language: English
  • Text-to-Speech: Enabled
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  • Word Wise: Not Enabled
  • Lending: Enabled
  • Enhanced Typesetting: Not Enabled
  • Amazon Best Sellers Rank: #336,924 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Customer Reviews

Top Customer Reviews

By Duwayne Anderson on May 5, 2000
Format: Paperback
I have six books on statistics in my personal library. All of them are bigger than Bulmer's book, but none have been read as many times, and none are as tattered, marked up, and cross-referenced. Simply put, Bulmer's book is the most useful and complete book on basic statistics that I have. It's a nice package in a reasonably sized book with all the most important stuff for dealing with basic statistical problems that many engineers are likely to encounter in a day's work.
Chapter 1 is a short blurb on the concept of probability. This is very useful because it places the rest of the text on a very specific and concise footing. Essentially there are two concepts of probability. One is the relative frequency with which an event occurs in the long run. An example of this is the tossing of a coin many times and counting the number of times it comes up heads. The author describes this as statistical probability.
The second concept of probability is what the author calls inductive probability. Inductive probability is "the degree of belief which it is reasonable to place on a proposition on given evidence." The essential difference between the two concepts of probability is that statistical probability is an empirical concept, while "inductive probability is a logical concept." Bulmer closes chapter 1 by saying, "It has been reluctantly concluded by most statisticians that inductive probability cannot in general be measured and, therefore, cannot be ............" Read chapter 1 to find some interesting arguments in support of this proposition - a proposition that may be surprising to some people. As a result (and as the book's title suggests) Bulmer keeps his book almost exclusively in the domain of statistical probability.
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Format: Paperback
I've learned probability and statistics from at least four other authors, and have constantly been browsing other textbooks that appear in the bookstore. I chanced upon Bulmer's 1965 book one fortunate day. It is still useful and relevant more than thirty years after its first printing. This clear and elegant book is also concise and straight-to-the-point, offering beautiful and brief developments of material that usually appears hopelessly muddled in many a reputable current statistics textbook (e.g., different notions of probability, the binomial, Poisson, normal distributions, and the Central Limit Theorem). Aside from the solid mathematics and many worked examples, the book includes a few entertaining digressions into the history of the subject.
In short, learn and review statistics from this classic. Thank you, Mr. Bulmer, and Dover Publications (for making this textbook available in a nice format at such a low price).
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By A Customer on September 23, 2002
Format: Paperback
This modest little book is both a masterpiece and a gem! I can't praise it enough! It is different from any other statistics book I have ever read in that it puts you in the place of famous historical figures in statistics and helps you rediscover their findings. His use of original source material is very well done. The book is self-contained and the author proves almost everything of importance(some of the proofs are more intuitive than rigorous at times, but that's the point). Bulmer has a knack of making the most difficult concepts (hyperspace, degrees of freedom) seem natural. He covers a very broad terrain from distributions, tests of significance, inference, Bayesian methods, etc. Written on many levels, this is useful for a novice or intermediate student but I suspect professional statisticians will find much to keep them thinking about. While reading through this book you will often say "aha, so that's why they do that". For the price it is the best value possible; you won't regret picking up a copy of this book and if you enjoy the inner workings of statistical theory you will refer to it again and again.
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Format: Paperback
The positive reviews for this book seem to be written by people with a previous background in statistics and/or strong math skills. Furthermore, they want to know the why behind every equation. I mostly fall into this category.

The negative reviews come from people who use this as their introduction to statistics, and who probably don't have a strong grasp of calculus or perhaps higher level math in general.

In my opinion this book offers something that no other statistics book has: clear derivations of all the fundamental and important equations and distributions in statistics; followed by lucid explanations. In other words this book unravels the mystery behind the equations. If you've thought about a statistics equation a lot and wondered, WHY? Then this is the book to read.

Here are 4 questions I had that Bulmer answered:
1) Why is the mean more commonly used than the median (and in which cases is the median better)? p.51-54
2) As a measure of variability why use a root-mean-square procedure (i.e. accepted def. of std deviation ) instead of mean deviation (i.e. take absolute value of deviations)? p.54-59
3) What is the logical error in the gambler's fallacy? p.87-88 (Note: many statistics books treat this, but I've found Bulmer's book to give the most satisfying answer.)
4) Why does the standard deviation of a sample have the n-1 term in the denominator instead of the n term like the stdev of the population? p.129-130

(Note that he answers questions 1, 2, and 4 more than once, but the pages listed are the first time the answer appears.)

Thus, I strongly recommend buying and reading this book if, like me, you have a burning desire to know why the equations are the way they are.
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