Principles of Statistics

By M.G. Bulmer

Book Overview

Concise description of classical statistics, from basic dice probabilities to modern regression analysis. Equal stress on theory and applications. Moderate difficulty; only basic calculus required.... This description may be from another edition of this product.

• Format: Paperback
• Language: English
• ISBN: 0486637603
• ISBN13: 9780486637600
• Release Date: March 1979
• Publisher: Dover Publications
• Length: 256 Pages
• Weight: 1.04 lbs.
• Dimensions: 0.5" x 5.6" x 8.2"

Customer Reviews

Succinct but Complete

In amazingly succinct language, this book covers a wide range of topics on probabilities and statistics with crystal clarity. It serves well as an introduction to the topic, but unlike many introductory books, it by no means lack mathematical depth. This tiny but rich book is a real gem in today's book market where the emptiest topic can take more than 500 pages.

Great book, easy to read and complete too

Why I think this is a great book: 1) It is easy to read and understand. Author has a great aproach and examples all around. 2) At the same time nothing is left not proven. Prior to this, I purchased couple of books that just give you this or that for granted as a formula (for example they just "anounce" what the standard deviation is for the binomial distribution without proving it or explaining why). I despised that, as I believe you either understand and learn something completely, or you don't. 3) The book even went further than I expected - not only that everything mentioned in the book is proven, it is even explained why some choices in the statistics are made in such a way that they are. Ever wondered why quadratic mean is used as a measure of deviation instead of arithmetic mean of absolute values? This is just one example of a fine job author did in making audience UNDERSTAND statistics.

Rich in Insight

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.

A classic textbook; probably the best introduction around

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).

An excellent reference and self-study guide

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.Chapter 2 introduces two simple law of probability. The first relates to the addition of probabilities of mutually exclusive events. The second relates to the multiplication of probabilities. Simple in concept, Bulmer illustrates these two laws by several examples including tables of measurements made on real experiments, and some from Mendel's laws of heredity.Chapter 3 is pivotal. It develops the mathematical expressions for random variables and probability distributions. Chapter 3 is relatively short, but lays the groundwork for chapter 4, which describes the properties of distributions. Chapter 4 has many useful equations, including those for the mean, variance, measures of dispersion, moments, etc.Chapter 5 introduces the notion of expected values for both discrete and continuous variables. These are determined not only for single distributions, but also for distributions that are combined in algebraic ways through multiplication, addition, division, etc., which also leads (naturally enough) to the moment-generating function.Chapter 6 highlights some important distributions (the Binomial, Poisson, and Exponential) and discusses their statistical properties (mean, varian