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Essential Statistics, Regression, and Econometrics

Gary Smith
Academic Press
Publication Date: 
Number of Pages: 
[Reviewed by
Peter Rabinovitch
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I am frequently asked to recommend statistics books to beginners, so I was very eager to see what was in this book.

First the good. It is very well written, and covers the basics clearly and understandably to someone with only high school algebra — nothing more advanced is needed. And in addition to the basics, the author discusses p-hacking, misleading plots, and other important topics that are frequently left out of introductory texts.

The examples are primarily from economics, as befits the book’s title. The book is not aimed at math students, so there are no proofs and no derivations. They can be found in other texts or in a further course. Also, the range of statistics covered is narrow, mostly about normal distributions and linear regression, but in my opinion suitable for such a first course.

However nice, for me the book has one large flaw. I cannot understand why a stats book today would not have R code to demonstrate the various topics. The book has 50 or so exercises in each chapter, and many are repetitive. That space could have been used for simple R scripts, or they could have been put online (there are, as yet, no online resources for students). Without the derivations, without proofs, without code to illustrate what is going on, I think a huge opportunity has been wasted to help the student understand, rather than memorize.

I do think the explanations are very clear, so perhaps the book could be further refined into a “Statistics for Managers” type book, but as a text for beginning students, for me, it falls short.

Peter Rabinovitch is a Senior Performance Engineer at Akamai, and has been doing data science since long before “data science” was a thing.

Chapter 1 Data, Data, Data
Chapter 2 Displaying Data
Chapter 3 Descriptive Statistics
Chapter 4 Probability
Chapter 5 Sampling
Chapter 6 Estimation
Chapter 7 Hypothesis Testing
Chapter 8 Simple Regression
Chapter 9 The Art of Regression Analysis
Chapter 10 Multiple Regression
Chapter 11 Modeling