# 113 Best Books on Statistics

We have compiled a list of the Best Reference Books on Statistics, which are used by students of top universities, and colleges. This will help you choose the right book depending on if you are a beginner or an expert. Here is the complete list of Statistics Books with their authors, publishers, and an unbiased review of them as well as links to the Amazon website to directly purchase them. If permissible, you can also download the free PDF books on Statistics below.

## 3. Introduction to Mathematical Statistics

 1."Statistical Inference" by G Casella and B L Berger “Statistical Inference” Book Review: This book covers both the theory and practical aspects of statistics. It explains topics such as ancillarity, invariance, Bayesian methods, pivots, Stein estimation, errors in variables, and inequalities in detail. The book emphasizes the practical applications of statistical theory, helping readers develop useful statistical procedures for solving different problems. It also includes the latest developments in statistics. This book will benefit both students and professionals in the field of mathematics. 2."Probability and Statistics" by M H DeGroot “Probability and Statistics” Book Review: The book covers various aspects and uses of probability and statistics, along with basic calculus, vectors, and matrices. It includes chapters on classical and Bayesian methods, simulation, Markov chain, Monte Carlo, Bootstrap, and residual analysis in linear models. The book has numerous examples to help readers understand the concepts better. It is useful for students studying mathematics, probability, and statistics. 3."Theory of Point Estimation" by E L Lehmann and G Casella “Theory of Point Estimation” Book Review: This revised book covers contemporary topics and recent developments in point estimation. It discusses the major topics of preparations, unbiasedness, equivariance, average risk optimality, minimaxity, admissibility, and asymptotic optimality. It provides many problems and examples for self-study and practice. The book discusses Bayesian and hierarchical Bayesian approaches along with simultaneous estimation in detail. It also includes suggestions and scopes for further study. This book will be useful for those interested in mathematics and statistics. 4."Introduction to Mathematical Statistics" by Robert V Hogg 5."Introduction to Mathematical Statistics and Its Applications" by Richard J Larsen and Morris L Marx “Introduction to Mathematical Statistics and Its Applications” Book Review: The book provides a well-organized and comprehensive study of theoretical aspects of mathematical statistics. It covers the basic topics such as probability, random variables, special distribution, estimation, hypothesis testing, and normal distribution, followed by advanced topics like types of data, two-sample interferences, goodness-of-fit tests, regression, analysis of variance, randomized block design, and nonparametric statistics. Each chapter has numerous examples to aid in understanding. The book emphasizes the practical application of statistical methods while reinforcing calculus. Real-world data and case studies are included to provide readers with practical knowledge and relatable content. 6."An Introduction to Statistical Learning: with Applications in R" by Gareth James and Daniela Witten “An Introduction to Statistical Learning: with Applications in R” Book Review: The book provides a comprehensive overview of statistical learning, covering various modeling and prediction techniques and their practical applications. It includes detailed explanations of linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering. The book presents the implementation and use of these methods in a clear and accessible way, and is supported by illustrations, color graphics, and real-world examples. It will be valuable for professionals in science and industry, as well as statisticians and non-statisticians seeking a deeper understanding of statistical learning techniques. 7."Introduction to Mathematical Statistics" by Paul G Hoel “Introduction to Mathematical Statistics” Book Review: This book combines theoretical concepts and practical applications of statistics effectively. It includes chapters on important topics such as probability, probability distributions, sampling theory, correlation and regression, statistical inference, testing goodness of fit, small sample distributions, and statistical design. Nonparametric methods, empirical methods, and statistical methods are also introduced. The book contains numerous problem sets to help readers understand the concepts better. 8."Statistical Inference" by George Casella “Statistical Inference” Book Review: The book aims to provide an understanding of statistical concepts and their practical applications in solving various problems. It covers important topics such as random number generation, simulation methods, bootstrapping, EM algorithm, p-values, and advanced topics like logistic regression and robust regression. The book explains the techniques, definitions, and concepts required for statistical inference development. Numerous examples are used to explain the concepts throughout the book. It is a valuable resource for first-year graduate students who are interested in statistics. 9."Introduction to the Practice of Statistics" by David S Moore and George P McCabe “Introduction to the Practice of Statistics” Book Review: The book is a newly updated and revised edition that covers the latest topics and recent research in statistics. It provides a step-by-step description of each concept in an efficient manner. The book emphasizes critical thinking and data analysis, and it highlights the applications of statistics in various professions and fields. It also focuses on the use of computers and calculators for problem-solving. The book is a valuable resource for students of mathematics and statisticians alike. 10."Introduction to Probability and Mathematical Statistics" by Lee J Bain and Max Engelhardt “Introduction to Probability and Mathematical Statistics” Book Review: The book offers a well-organized and theoretical introduction to mathematical statistics and probability. It covers all the essential topics and major aspects of probability and mathematical statistics, and each chapter is self-contained. To help readers gain a better understanding, the book provides many realistic exercises and examples. Topics such as sequential tests, sampling distributions, regression, and linear models are covered in detail. This book is suitable for students of mathematics and those interested in probability and statistics. 11."Mathematical Statistics" by T S Ferguson

## 4. Nonparametric Statistics

 1."Applied Nonparametric Statistics" by W W Daniel “Applied Nonparametric Statistics” Book Review: This textbook is designed for students in the field of mathematics. It consists of ten chapters and appendices at the end. The first chapter provides an introduction to hypothesis testing, nonparametric statistics, and format and organization. The following chapters cover procedures for utilizing data from a single sample as well as different numbers of independent and related samples. The book also explains the Chi-Square test and goodness-of-fit test and their mathematical properties, along with a description of rank correlation and association measures and simple linear regression analysis. Each chapter concludes with review exercises and references to assist with understanding the concepts. 2."Nonparametric Statistical Methods" by M Hollandor “Nonparametric Statistical Methods” Book Review: This book is designed for upper-level undergraduate or first-year graduate students and provides a comprehensive coverage of nonparametric regression methods and the bootstrap. The book features detailed explanations of contingency tables, odds ratio, life distributions, and survival analysis. In addition, the book explores nonparametric methods for experimental designs, presents real-world datasets, and provides in-depth discussions of various procedures. To aid understanding, the book includes illustrated examples that use Minitab and StatXact. Furthermore, the book includes detailed solutions to all problems. 3."Nonparametric Statistical Methods Based on Ranks" by E L Lehmann “Nonparametric Statistical Methods Based on Ranks” Book Review: The target audience of this book is graduate students. The book provides an introduction to nonparametric methods for analyzing and planning comparative studies. It extensively covers rank tests, including their estimating procedures and detailed descriptions. Each chapter concludes with mathematical exercises, and the book provides illustrative examples for better understanding. 4."Textbook of Parametric and Nonparametric Statistics" by Vimala Veeraraghavan and Suhas Shetgovekar “Textbook of Parametric and Nonparametric Statistics” Book Review: This book is intended for students and researchers in the social sciences. This book covers both parametric and nonparametric statistical methods. It provides a detailed explanation of correlation and regression, analysis of variance, and test construction. The book also includes comprehensive information on SPSS and Excel-based statistical analysis of data. Additionally, the book discusses the use of statistics in psychology and the construction of psychological tests. Worked-out examples are included throughout the book to aid in understanding. 5."Nonparametric Statistics for Stochastic Processes: Estimation and Prediction (Lecture Notes in Statistics)" by D Bosq “Nonparametric Statistics for Stochastic Processes: Estimation and Prediction (Lecture Notes in Statistics)” Book Review: This book provides an in-depth exploration of the theory of nonparametric estimation and prediction for stochastic processes, encompassing discrete and continuous time models, catering to experts in mathematical statistics. The book offers detailed coverage of kernel methods, optimal, and super optimal convergence rates. It also includes numerous examples to assist with the comprehension of the concepts presented. 6."Nonparametric Statistics for Behavioural Science" by Sidney Siegel and Castellan 7."The Oxford Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics (Oxford Handbooks)" by Jeffrey Racine and Liangjun Su “The Oxford Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics (Oxford Handbooks)” Book Review: This book is geared towards researchers in the field of Statistics, and features chapters written by prominent international econometricians and statisticians. It offers detailed information on statistical methods for nonparametric and semiparametric procedures, covering a wide range of topics, including modeling of cross-section, time series, panel, and spatial data. The book also includes discussions on the methodology of semiparametric models, special regressor methods, true error of competing approximate models, support vector machines, and their modeling of default probability. In addition, this book introduces some new approaches to the analysis of nonparametric models with exogenous treatment assignment. 8."Nonparametric Statistics (The Six Sigma Research Institute Series)" by David C Sutor and Donald B White 9."All of Nonparametric Statistics: A Concise Course in Nonparametric Statistical Inference (Springer Texts in Statistics)" by Larry Wasserman “All of Nonparametric Statistics: A Concise Course in Nonparametric Statistical Inference (Springer Texts in Statistics)” Book Review: This book is intended for graduate students pursuing a Master’s or Ph.D. degree. It provides a comprehensive overview of nonparametric delta method and regression, as well as an explanation of minimax estimation and density estimation. The book delves into topics such as nonparametric confidence sets and wavelets in great detail. Additionally, the book includes a discussion on orthogonal function methods and the bootstrap. The book provides both theory and methodology, which are helpful for a better understanding of the concepts presented. 10."Nonparametric Statistics for Behavioural Science (Psychology)" by Sidney Siegel 11."Statistics for Experimentors" by W G Hunter and J S Hunter “Statistics for Experimenters” Book Review: The book covers a range of topics including innovation catalysis, problem-solving, and discovery. It also provides a comprehensive description of scientific experimentation and the tools required to maximize knowledge gained from research data. The author explains how these tools can be used during every stage of the investigation process, starting with problem identification and followed by an examination of statistical methods for design and analysis. The book covers various topics such as graphical analysis of variance, computer analysis of complex designs, simplification through transformation, basics of process control, and much more.

## 5. Statistical Methods

 1."Statistical Methods (Combined Volume)" by Das N “Statistical Methods (Combined Volume)” Book Review: The intended audience for this book are students of biomedical engineering, researchers, and scientists who want to learn about statistical methods. The book provides a comprehensive coverage of statistical methods, including formulas, results, theorems, and proofs. Each chapter is supplemented with solved examples, exercises, and short questions to reinforce understanding. 2."Statistical Methods" by S P Gupta “Statistical Methods” Book Review: The intended audience for this book includes students, researchers, and scientists who are interested in the applications of statistical methods in fields such as biology, demographics, economics, health, and physics. The book is divided into two parts, with the first part discussing statistical methodology and the second part covering more complex approaches to statistical data analysis. It also provides solved examples, exercises, and short questions at the end of each chapter to aid in understanding the concepts discussed. 3."Statistical Methods: An Introductory Text" by J Medhi “Statistical Methods: An Introductory Text” Book Review: This book is aimed at students, researchers, and scientists seeking to understand and apply statistical methods. It covers the theorems, results, and proofs of statistical methods, as well as statistical inference and tests of significance. The book includes solved examples, exercises, and short questions at the end of each chapter. 4."Statistical Methods: Concepts, Application and Computation" by Y P Aggarwal “Statistical Methods: Concepts, Application and Computation” Book Review: This book is intended for students, researchers, and scientists who are interested in learning about statistical analysis concepts, applications, and procedures. It explains the multivariate analysis information and covers both parametric and nonparametric tests. The book emphasizes the assumptions of homogeneity and normality. In addition, the book contains solved examples, exercises, and short questions at the end of each chapter to aid in understanding. 5."Statistical Methods for Engineering and Sciences" by Asad U Khan “Statistical Methods for Engineering and Sciences” Book Review: This book is intended for mathematicians, engineers, researchers, and scientists. It is organized into twelve chapters and focuses on statistical methods and their applications. The book includes numerous solved examples, exercises, and short questions at the end of each chapter. 6."Statistical Methods in Geographical Studies: Student Edition" by Aslam Mahmood “Statistical Methods in Geographical Studies: Student Edition” Book Review: This book is intended for social scientists, geographers, and researchers interested in statistical analysis. The book discusses the graphical representation of data and theories of correlation and network analysis. It covers regression, nearest neighbor analysis, the rank-size rule, gravity and potential models, principal component analysis, and discriminant analysis. The book also includes solved examples, exercises, and short questions at the end of each chapter. 7."Statistical Methods for Quality Improvement" by Hitoshi Kume “Statistical Methods for Quality Improvement” Book Review: This book is aimed at students, researchers, and social scientists who want to apply statistical methods to real-world problems. It focuses on the concepts, principles, and techniques of statistical methods and covers topics related to the understanding of production processes. The book provides solved examples, exercises, and short questions at the end of each chapter to help readers reinforce their understanding of the material. 8."Statistical Methods for Research" by K Kalyanaraman “Statistical Methods for Research” Book Review: This book is intended for students, researchers, and scientists, and aims to provide an understanding of various statistical tools and their applications. The book utilizes diagrams and figures to explain concepts. Topics include statistical reasoning, probability, and sampling, as well as two-variable analysis, such as correlation and regression. The book also includes solved examples, exercises, and short questions at the end of each chapter. 9."Biostatistics a Manual of Statistical Methods for Use in Health, Nutrition and Anthropology" by Rao “Biostatistics a Manual of Statistical Methods for Use in Health, Nutrition and Anthropology” Book Review: This book is intended for students, researchers, chemists, and scientists in the fields of medicine, health, and nutrition. It is divided into nine sections that cover statistical data and analysis in these areas. The book explains the concepts and applications of various statistical methods, including biostatistics, analysis of covariance, and analysis of variance. It also includes solved examples, exercises, and short questions at the end of each chapter. 10."An Introduction to Statistical Methods" by C B Gupta “An Introduction to Statistical Methods” Book Review: This book is intended for students, researchers, chemists, and scientists. Its primary focus is on the application of statistical methods in business, commerce, and other social sciences. The book also covers the theory of games, providing a comprehensive understanding of the subject. It delves into various statistical techniques and explains their applications. It emphasizes the importance of various statistical concepts and their significance in the real world. Additionally, the book includes solved examples, exercises, and short questions at the end of each chapter.

## 7. Statistical Inference

 1."Statistical Inference" by Rajagopalan “Statistical Inference” Book Review: The book provides a comprehensive introduction to probability theory, covering topics such as first principles, statistical inference, techniques, and consequences. It is well-organized and emphasizes practical applications of statistical theory. The book is a useful reference for postgraduate students and statisticians alike. However, the author recommends that readers have a strong mathematical background before delving into this book. 2."Statistical Inference: Theory of Estimation" by Kumar S M “Statistical Inference: Theory of Estimation” Book Review: The book begins by explaining how to summarize data and introduces concepts like minimal sufficient statistics, Blackwell theorem, and others. Students can practice their knowledge by answering questions at the end of each chapter. The book also covers advanced topics such as Cramer-Rao, Bhattacharyya variance lower bounds, and Robbins and Kiefer variance lower bounds for Pitman models, with solved examples provided to help understand these concepts. Additionally, the book has separate chapters on essential topics such as Pitman estimator, Empirical Bayes, and scale models. It is designed for postgraduate statistics students. 3."Statistical Inference" by Kumar and Chaudhary 4."Linear Statistical Inference and its Application" by C Radhakrishna Rao 5."Statistical Inference: Testing of Hypotheses" by Srivastava “Statistical Inference: Testing of Hypotheses” Book Review: The main focus of this book is on the mathematical foundations of hypothesis testing developed by J. Neyman and Egon Pearson. It is written in a way that is easy for students to understand. The book explains important concepts such as optimality, asymptotic relative efficiency, consistency, and asymptotic null distribution in a clear and concise manner. It includes dedicated chapters on essential topics like testing of hypothesis and ratio tests. This book is also a useful reference for researchers working in the areas of biostatistics and econometrics. 6."Probability and Statistical Inference" by HOGG “Probability and Statistical Inference” Book Review: The book begins by introducing fundamental probability concepts such as counting techniques and Bayes’ Theorem. The examples provided help readers gain a deeper understanding of the concepts. The book then goes into detail on topics such as discrete and continuous probability distributions, multivariate distributions, the Normal Distribution, and more. Practice exercises are included to test readers’ understanding of the concepts. The book concludes with a brief introduction to set theory, limits, infinite series, integration, and multivariate calculus.

## 10. Applied Statistics and Mathematical Statistics 