Reasoning with Data is a quantitative methods textbook that puts simulations, hands-on examples, and conceptual reasoning first. That approach is made possible in part thanks to the widespread availability of the free and open-source R platform for data analysis and graphics (R Core Team, 2016). R is often cited as the language of the emerging area known as “data science” and is immensely popular with academic researchers, professional analysts, and learners. The examples in the book use R to generate graphs, data, simulations, and scenarios, and provides all of the commands that teachers and students need to do the same themselves. The use of R also facilitates demonstrations of alternatives to the oft-criticized Null Hypothesis Significance Test, such as Bayes factors. One definitely does not need to be an R user or a programmer to use this book effectively. The examples start slowly and introduce R commands and data structures gradually. The complexity of commands and code sequences is limited to the minimum needed to explain and explore the statistical concepts. Those who go through the whole book will feel competent in using R and will have new problem-solving capabilities for data analysis. The author has taught semester-long classes using initial drafts of this textbook, and students have arrived at their final projects with substantial mastery of both statistical inference techniques and the use of R for data analysis. Thanks to the extensive use of simulations and graphics to demonstrate methods of inferential reasoning, the book is suitable for undergraduate and graduate students who lack a deep background in mathematics. Students whose mathematical preparation was limited to high school algebra have used the book successfully. With 13 chapters and three appendices geared for novice R users, the book includes more than enough content for a 14-15 week semester. Each chapter concludes with a selection of exercises that reinforce students' understanding of chapter concepts. Explore this site for example code, in-class exercises, student learning assessments, lesson plans, and editable Powerpoint slides. -Jeff Stanton, Syracuse University |