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Miller and Freund's Probability and Statistics for Engineers

Richard A. Johnson
Publication Date: 
Number of Pages: 
[Reviewed by
Robert W. Hayden
, on

Joint review of

“Statistics for Engineers” courses are common at universities with engineering programs; today such courses might better be called “Statistics for STEM.” To mathematics faculty they are often known as the “calculus-based intro stats” course.

At hand are two classic texts aimed at this audience, both now in their ninth editions. Like any textbook aimed at a particular discipline, they feature problems and examples from the target discipline, and perhaps some additional topics widely used in the discipline. The “engineering” flavor has some additional differences compared to those aimed at other disciplines. They typically contain a lot more probability and a lot more algebra. Some would say they also contain proofs, but most of those might well be called derivations — they just “do the algebra.” Usually these books have at least one statistician on the writing team and are reliable on statistical matters.

Both of these texts are designed to suffice for a year-long course, so the list of topics is fairly long. Topics covered by both that would not be in most non-calculus courses include multiple regression (including fitting curves to data), Analysis of Variance, experimental design, quality control, and non-parametric statistics. Both mention the inclusion of computer output from Minitab and SAS, but that’s barely detectable in both books. There are comments like “from a computer program we got \(s=21.77\)” and some graphs not credited as to source. Miller et al. has an appendix on using the statistical programing language R, but it’s really not well integrated into the text. Both books devote far more page space to grinding out arithmetic than to anything dealing with computers. Walpole et al. appears to have more probability plus a slightly higher mathematical and reading level. Miller et al. has more attention to graphical displays and assumption-checking, but Walpole et al. seem to have more data from published studies. Walpole also has a chapter on Bayesian methods.

The biggest weakness of both textbooks is the exercises. Despite all the space devoted to derivations, the exercises are generally computational, though some involve algebra. There are none of the thought-provoking questions made popular by David Moore’s books, starting with Statistics: Concepts and Controversies in 1979, and now common in college texts. In this regard the exercises are below the level of high school AP Statistics courses. Still, either of these books could be suitable for their intended audience if supplemented by the Moore book already mentioned.


  1. Introduction

  2. Organization and Description of Data

  3. Probability

  4. Probability Distributions

  5. Probability Densities

  6. Sampling Distributions

  7. Inferences Concerning a Mean

  8. Comparing Two Treatments

  9. Inferences Concerning Variances

  10. Inferences Concerning Proportions

  11. Regression Analysis

  12. Analysis of Variance

  13. Factorial Experimentation

  14. Nonparametric Tests

  15. The Statistical Content of Quality Improvement Programs

  16. Application to Reliability and Life Testing