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Shalabh, Univariate, Bivariate and Multivariate Statistics Using R: Quantitative Tools for Data Analysis and Data Science, Journal of the Royal Statistical Society Series A: Statistics in Society, Volume 185, Issue 2, April 2022, Pages 736–737, https://doi.org/10.1111/rssa.12791
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Developments in R software have created a legacy and have significantly impacted the teaching and research approaches in applied statistics and data science. The book presents an excellent collection of univariate to multivariate applied statistics topics and explains them well, demonstrating their implementation in R software. The collection of hand-picked popular statistical topics included in the book are commonly used in applied and social sciences. The book illustrates how results can be quickly obtained on such topics using R software without going into the deep theory of statistical methodologies. The book’s contents and collection of topics are like selecting beautiful and choicest flowers in creating a well-decorated flower bouquet.
The book has 13 chapters covering topics from univariate to multivariate statistics. Chapters 1 and 2 are introductory and briefly present various applied and computational statistics concepts, including some basics of R software for understanding the topics in further chapters. Chapter 3 presents some essential and popular graphics and their creation in R software. Chapters 4 and 5 cover some applications of basic statistical tools, including means, correlation, counts and power analysis. Chapter 6 concerns analysis of variance, whereas chapters 7 and 8 present simple and multiple regression, logistic regression and generalized linear model. Chapters 9, 10 and 11 cover topics of multivariate analysis and present the multivariate analysis of variance, discriminant analysis, principal component analysis and factor analysis. The last two chapters, 12 and 13, describe cluster analysis and non-parametric tests. The implementation of the tools is illustrated using data-based examples in R software with a detailed explanation of the process and results in all the chapters. Exercises supplement every chapter at the end.
Reading the book feels like an experience of attending a live workshop from an experienced academician explaining it in a simple language. The book is well suited for teaching senior undergraduate or fresh graduate students in statistics and allied subjects of the modern era. The topics from this book can supplement the teaching of courses in statistics as an excellent application-oriented deliverable material. The community of statisticians, applied workers, workshop trainers and students will undoubtedly find this book appealing and beneficial. The author must be complimented for the thoughtful creation of this book.