The statistical terms, definitions, and equation notations are consistent throughout the text. No issues with consistency in that text are found. Each chapter starts with a very interesting paragraph or introduction that explains the idea of the chapter and what will be covered and why. Overall, the text is well-written and explained along with real-world data examples. Although it covers almost all the basic topics for an introductory course, it has some advanced topics which make it a candidate for more advanced courses as well and I believe this will help with longevity. Labs are available in many modern software: R, Stata, SAS, and others. The resources, such as labs, lecture notes, and videos are good resources for instructors and students as well.

The real data sets examples cover different topics, such as politics, medicine, … etc. I use this book in teaching and I did not find any issues with accuracy, inconsistency, or biasness. The chapters are well organized and many real data sets are analyzed. If the volunteer sample is covered also that would be great because it is very common nowadays. One of the good topics is the random sampling methods, such as simple sample, stratified, cluster, and multistage random sampling methods. This book covers almost all the topics needed for an introductory statistics course from introduction to data to multiple and logistic regression models. Reviewed by Hamdy Mahmoud, Collegiate Assistant Professor, Virginia Tech on 5/16/22

Journalism, Media Studies & Communications +.

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