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Introduction to Probability and Statistics for Engineers and Scientists

Sheldon M. Ross
Academic Press
Publication Date: 
Number of Pages: 
Hardcover with CDROM
[Reviewed by
Miklós Bóna
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The title of the book is mostly accurate, with one exception. The book is certainly an introduction, in the sense that readers do not need more background than a good knowledge of second semester calculus. The "for Engineers and Scientists" part is also accurate, since the book is centered around applications, not proofs, so it would not be appropriate for a course intended for mathematics majors. However, the order of the words "Probability" and "Statistics" is misleading, since the book is much more about statistics than about probability. It has 15 chapters, 12 of which focus on statistics while the remaining three focus on probability.

The text is written in a very down-to-earth, no-nonsense style. The examples are well-chosen in several aspects. First, they explain how to interpret numerical data to reach conclusions and answer questions you may actually care to answer. Second, just as in real life, the examples often come with a crucial piece of information missing, but the solutions explain how to fill that hole.

For instance, on page 264, we read about a New York Times report that claims that a certain opinion poll in October 2003, the percentage of the population approving President Bush's performance was 52. The margin of error was four percent. Can we infer how many people were questioned? The answer is that just from the given data, we cannot. However, as the author tells us in the solution, it has become common practice for the news media to use 95 percent confidence intervals. With this extra piece of information, the question can be answered easily. This reviewer appreciates these kind of examples because they teach the student what could be done when a piece of the puzzle is missing.

Instructors who have used previous editions may want to know what is new in this new edition. There is an extra chapter on bootstrapping, permutation tests, and Monte-Carlo Simulation. Exercises and real-data examples have been updated.

There are plenty of exercises at the end of each chapter. None come with solutions, or even hints or numerical answers. This reviewer realizes that the book comes with a CD-ROM whose software helps with computations, and that the book has a separate student solution manual available for purchase. Still, in his experience, students will be critical of this feature. Other than this drawback, if you are going to teach a course focused on statistics to non-mathematics majors with a good calculus background, considering this book is well worth your time.

Miklós Bóna is Associate Professor of Mathematics at the University of Florida.


Ch1 Introduction to Statistics
Ch2 Descriptive Statistics
Ch3 Elements of Probability
Ch4 Random Variables and Expectation
Ch5 Special Random Variables
Ch6 Distributions of Sampling Statistics
Ch7 Parameter Estimation
Ch8 Hypothesis Testing
Ch9 Regression
Ch10 Analysis of Variance
Ch11 Goodness of Fit Tests and Categorical Data Analysis
Ch12 NonparametricHypothesisTests
Ch13 Quality Control
Ch14 LifeTesting
Ch15 Simulation, Bootstrap Statistical Methods, and Permutation Tests
Appendix of Tables