The exercises at the end of each section are balanced, starting with direct testing of the methods to application of statistics to real-life data. Content is comprehensive and its sequencing is logical. This is a very good introductory and intermediate statistics one-semester course. No issues with the interface and no issues with images. The sequencing and the organization of the text is clear and logical. One can use independently a portion of the textbook easily as it used standard notation and widely used terminology. The sequencing of the chapters and the sections of each chapters work well. The text is clear, examples are well designed, and the graphics provided in the text are of very good quality. The contents is overall up-to-date, but the trend of using technology is increasing and application of statistics to real-life data is increasingly incorporated in statistics courses, including technology component (R is an open and free statistical software used by many data scientists, and life scientists) seem to be inevitable in order for the textbook to remain relevant. Moreover, in many real-life data examples, the sample size are slightly higher than 30, but not large enough where using the normal distribution provides precision (instead of using the t-distribution). Also, the pooled variance is used for the testing hypothesis for the difference in means, which doesn't match the results that one can obtain using a statistical software (R), where the unpooled variance is used, and the degrees of freedom for the t-curve is not the typical approximation (sum of the sample sizes minus 2). Therefore, the inferential statistics portion of the textbook relies heavily on the use of tables and on the rejection regions instead of the p-value. The textbook is suited for a statistics course for a general audience and without statistical software (like R). I am using this textbook as a second resource for an applied and computational statistics course (mainly for life sciences) with the use of technology (R). The text covers all material needed for an introduction and intermediate statistics course: starting with descriptive statistics, then the elements of probability theory needed for statistics, and finishing with a large portion dedicated to inferential statistics, where all topics of hypothesis testing and regression are covered. Reviewed by Nabil Kahouadji, Associate Professor of Mathematics, Northeastern Illinois University on 4/30/23
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