Reviewed by William M. Easley, Instructor (Business Statistics), University of New Orleans on 5/21/18

Comprehensiveness rating: 4 see less

How one assesses the comprehensiveness of this text depends on one’s purpose. It is purportedly designed for a one-semester course. For that (at least relative to business students at UNO), it is too long -- and too long on the mathematics. For a two-semester course, at least for our purposes, it is too short. For example, there is no discussion of 2-factor Anova, RBD, etc. However, there is much to admire about the way that the authors present the ideas.

Content Accuracy rating: 5

I spent about four hours reading various parts of the text and found no sign of bias or any gross errors. One can quibble over some of the definitions, e.g., that a discrete random variable must have only integral values. I saw a typo or two -- e.g., an SStotal that should have been an SSbetween. As with any book, there are probably others. But let me emphasize that I am not a professional statistician.

Relevance/Longevity rating: 3

Introductory statistics is a little bit like Latin, a ‘dead language’. The basics aren’t going to change. However, the “statistics education community” -- if there is such a thing -- seems to be in a tizzy these days over how to incorporate ‘big data’, etc. into such introductory courses. Some now use the term ‘data science.’ This text is definitely an old-fashioned and rather ‘mathy’ approach (not a bad thing in my eyes). Surprisingly, calculus techniques make an appearance toward the end of the book (the average business student will have little or no idea of that). But, aside from some instructions for using Excel for regression analysis (why not do this for Anova as well?), there is little guidance for technology. In the chapter on the F-distribution, where did those p-values come from? TI-83? Excel? StatTrek? Or did I miss something? Most of the current business stats texts give directions for using Excel, TI-83/4, Minitab, R or all of these. How is this text going to compete with those? I reckon that individual instructor/department could make amendments, but how many would be willing to?

Clarity rating: 4

Overall, I like the breezy writing style. But it is a bit bipolar, occasionally almost patronizing and then rather technical. Some terms are used which the student audience has virtually no chance of understanding. Better to omit those or provide explanation. On the other hand, since few students these days actually read books, particularly math books, anyway, why not let the authors express themselves in a way that they find logical and intellectually appealing? My students depend on me to explain the material, or, if I fail them, YouTube.

Consistency rating: 5

The text seemed terminologically consistent to me. I do recall a spot in the Anova section where the use of n (nT?) and nj may cause confusion.

Modularity rating: 3

Introductory statistics is not a very modular sort of subject -- it is more a continuous development. For example, the concept of p-value is introduced in chapter 9 of virtually every stats text. But the ‘p’ in p-value is for ‘probability’ and so the student needs to understand the material that chapter, 3 or 4 in virtually every text. Otherwise, I liked the presentation given here in ch. 4 on discrete distributions, but since the authors very nicely tie them together, that material is not presented in a ‘modular’ fashion at all.

Organization/Structure/Flow rating: 5

The topics of the text are presented in the normal progression. There is some possibility of changing the order of presentation after hypothesis testing (Ch.9), but not before that. Once again, this is how introductory stats works.

Interface rating: 5

Interface is fine, although I absolutely hate scrolling through a pdf. Students would definitely want a print version.

Grammatical Errors rating: 5

I ain’t found no grammar errors.

Cultural Relevance rating: 5

Found nothing culturally insensitive. Seems inclusive. All groups are subject to statistics.

Finally, the unscripted part of the review. There is a lot to like about this presentation of the subject. Some parts are quite enjoyable. Here are my criticisms, in order of my view of their increasing importance. 1. The type-setting (if that is the right term) of the formulas in generally pretty lousy. For example, x-bar is always shown with the bar about a mile above the x. The integral on p. 284 looks really bad. Those formulas for r are terrible-looking. I always use MathType for this stuff. On the subject of formulas, why the predilection for ‘computing’ formulas, rather than ‘definition’ formulas? Many authors do this. Who, except programmers, cares how the computer does it? The definition formula offers insight. 2. I like the chapters on discrete and continuous distributions. I think that those on confidence intervals and Anova are not well-written and will be confusing to students. With the chapter on regression, the authors are forced to yield to the complexity of the subject and necessarily trail off into territory that the typical business student has no chance to follow. 3. Ok, here is the 500-lb statistician in the room: My guess is that, except at some rather rarified and/or old-fashioned institutions, virtually everyone teaching introductory statistics now insists on a reliable automated homework/quiz course platform, like MyStatLab. The difficultly/impossibility of doing that is why I don’t write and provide my own free text. We use a Pearson e-book that provides that service to our two-semester sequence for $104.95.