**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.

- Engineering Statistics
- Probability and Statistics
- Introduction to Mathematical Statistics
- Nonparametric Statistics
- Statistical Methods
- Statistical Methods of Analysis and Design
- Statistical Inference
- Statistical Modeling
- Applied Statistics
- Applied Statistics and Mathematical Statistics

## 1. Engineering Statistics

1."Engineering Statistics" by Douglas C Montgomery
“Engineering Statistics” Book Review: This modern engineering statistics book provides a comprehensive overview of how statistical tools can be effectively utilized in engineering problem-solving processes. The book covers various topics including descriptive statistics, probability and probability distributions, statistical tests, and confidence intervals for one and two samples, as well as building regression models, designing and analyzing engineering experiments, and statistical process control. To aid in comprehension and self-assessment, the text offers numerous examples and exercises for readers.
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2."Mathematical Statistics with Applications" by Dennis Wackerly
Book Review: The presented book establishes a firm basis in statistical theory and emphasizes its crucial role in addressing real-world problems. With practical applications and a variety of exercises, the book explores the nature of statistics and its significance in scientific research. Its chapters delve into graphical and numerical methods, probability and inference, as well as various probability distributions including binomial and geometric, normal, gamma, beta, and multivariate.
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3."Mathematical Statistics and Data Analysis" by John A Rice
Book Review: The focus of this book is the mathematical statistics course, covering a wide range of topics with a strong emphasis on data analysis. The author effectively integrates computer usage to aid in the understanding and application of statistical concepts. Additionally, the book provides a practical approach by exploring real-world problems and their accompanying data, while still reinforcing theoretical concepts. It also includes statistical information, graphical displays, and various realistic applications to enhance the reader’s understanding of the material.
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4."Statistical Inference" by Roger Berger and George Casella
Book Review: This book teaches how to make statistical theories by using probability theory. The author uses techniques, definitions, and statistical concepts to create statistical inference theory. It is a helpful book for graduate students who are studying statistics and have a strong background in math. The book also emphasizes the practical use of statistical concepts, as well as understanding the basics. It covers topics like different types of discrete and continuous distributions with proofs, and provides example problems for better understanding. The book also covers advanced topics like random number generation, simulation methods, bootstrapping, EM algorithm, p-values, and robustness. It is a good book for first-year graduate students studying statistics.
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5."Springer Handbook of Engineering Statistics" by Pham Hoang
“Springer Handbook of Engineering Statistics” Book Review: Engineers, statisticians, researchers, teachers, and students of all fields can benefit from this book. It covers numerous statistical techniques to provide practical statistical insights. The book covers topics such as fundamental statistical processes for monitoring and improvement, reliability modeling, survival analysis, regression methods, data mining, statistical methods and modeling, and various applications including six sigma. With this book, readers can gain sufficient knowledge to improve their products and services.
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6."Modern Engineering Statistics" by Thomas P Ryan
“Modern Engineering Statistics” Book Review: This book takes a statistical approach to engineering applications and maintains a good balance between methodology and practical engineering statistics. It starts with explaining fundamental concepts before delving into more complex statistical techniques. Each chapter ends with a summary, and the book includes numerous examples, exercises, case studies, and illustrations. The book also provides a clear explanation of the relationship between hypothesis tests and confidence intervals. To illustrate statistical analyses, the book makes use of tools like ‘Minitab’ and ‘JMP’.
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7."Modern Statistical and Mathematical Methods in Reliability" by Wilson Alyson
“Modern Statistical and Mathematical Methods in Reliability” Book Review: This book focuses on ‘Reliability Theory’ and covers various research activities and applications related to it. It includes topics such as reliability modeling, network and system reliability, Bayesian methods, survival analysis, degradation and maintenance modeling, and software reliability. The book is based on papers presented at The Fourth International Conference on Mathematical Methods in Reliability held in Santa Fe, New Mexico.
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8."Computational Methods for Reliability and Risk Analysis" by Enrico Zio | |

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9."Life-time Data: Statistical Models and Methods" by Jayant V Deshpande
“Life-time Data: Statistical Models and Methods” Book Review: This book is primarily intended for students pursuing post-graduate courses in statistics, engineering statistics, and medical statistics. It provides a detailed explanation of the concept and role of aging in choosing suitable models for lifetime data. The book covers topics such as aging, tests for exponentiality, competing risks, and repairable systems. It also includes information on the ‘Public Domain R-software’ and provides clear instructions on how to use it.
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10."Paperback : Si Version ENGINEERING STATISTICS" by Runger
“Paperback: Si Version Engineering Statistics” Book Review: This book provides up-to-date information on engineering statistics and explains how statistical tools can be integrated into the engineering problem-solving process. It covers all major topics related to engineering statistics, including descriptive statistics, probability and probability distributions, statistical tests and confidence intervals for one and two samples, building regression models, designing and analyzing engineering experiments, and statistical process control, with detailed explanations.
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11."Statistics for Everyone: A Spreadsheet Oriented Approach" by Dr. A N Sah | |

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12."All of Statistics: A Concise Course in Statistical Inference" by Larry Wasserman
“All of Statistics: A Concise Course in Statistical Inference” Book Review: The aim of this book is to provide a comprehensive theory on all major aspects of statistics. It covers state-of-the-art topics such as nonparametric curve estimation, bootstrapping, and classification, with fully updated chapters. The book also addresses the process of data analysis. Some of the featured topics and methods utilize basic calculus and linear algebra as tools. This book is a valuable resource for graduating and advanced undergraduate students in computer science, mathematics, and statistics. It is equally important for students and researchers of data mining and machine learning.
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13."Introductory Statistics" by Prem S Mann
“Introductory Statistics” Book Review: This book is an updated and revised edition that covers the latest topics, methods, and applications in statistics. It draws inspiration from a wide range of disciplines and media sources. Each chapter is supported with marginal notes, step-by-step solutions, and numerous examples. The book provides a wealth of information, with real-world problems and solutions clearly illustrated. Case studies and examples are used to demonstrate the applications of the featured concepts and methods. This book is highly recommended for business professionals looking to enhance their statistical knowledge.
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14."Variational methods in statistics" by Rustagi | |

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## 2. Probability and Statistics

1."Introduction to Probability and Statistics" by J S Milton and J C Arnold
“Introduction to Probability and Statistics” Book Review: The book presents an introduction to basic probability theory and statistical inference with a balance of theory and methodology. The author uses diagrams to make the concepts more understandable. The book includes topics that match the latest syllabus of various universities in India. The author provides a detailed description of practical approaches to statistical modeling and data analysis. The book also contains many solved numerical examples drawn from various university examinations at the end of each chapter.
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2."Miller and Freund’s Probability and Statistics for Engineers" by R A Johnson and C B Gupta
“Miller and Freund’s Probability and Statistics for Engineers” Book Review: This book is intended for engineering students and teachers, and provides a balance of both theory and practical applications. It includes exam patterns and the latest research results to help readers prepare more comprehensively. The book covers a range of concepts, including basic concepts of Probability, Statistics, and Random Variables, as well as more advanced concepts. Each chapter includes descriptions, examples, laws, and important points to aid in revision.
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3."Probability and Statistics for Engineers and Scientists" by Walpole R E
“Probability and Statistics for Engineers and Scientists” Book Review: This book is intended for undergraduate students and presents concepts in a clear and concise manner. The material is logically organized, with a focus on applied problems. The book covers a range of topics, including an introduction to basic probability theory and statistical inference, with a unique balance of theory and methodology. The text provides clear and error-free diagrams, and employs standard and straightforward procedures for deriving equations. Numerous solved examples are provided throughout the book. While the book will be particularly useful for students in chemical and mechanical engineering, it will also be helpful for students in other fields.
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4."Statistics for Engineers and Scientists" by Navidi W
“Statistics for Engineers and Scientists” Book Review: This textbook provides a practical approach to statistical modeling and data analysis that portrays the subject as both a science and an art. The book includes numerous solved examples to support the theories and explanations, and additional figures have been added for clarity and comprehension. Pedagogically arranged questions, including multiple-choice questions, are also provided to help students assess their understanding. The book is primarily intended as a textbook for undergraduate and postgraduate students, but it may also be useful for undergraduate engineering students.
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5."An Introduction to Probability and Statistics" by Vijay K Rohatgi and A K Md Ehsanes Saleh
“An Introduction to Probability and Statistics” Book Review: The book offers a comprehensive learning experience by providing solutions and ample exercises for readers to deepen their understanding of the subject matter. It covers the basic concepts of Probability, Statistics, and Random Variables, as well as advanced topics such as the theory of error functions, conditional probability, and binomial distribution. Each chapter includes numerous solved examples, enhancing the reader’s comprehension of the material. The book is beneficial for undergraduate and postgraduate students pursuing BA, B.Com, and Arts and Commerce degrees, as well as those preparing for competitive examinations.
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6."Probability and Statistics (Schaum’s Outline Series)" by Murray Spiegel and John Schiller
“Probability and Statistics (Schaum’s Outline Series)” Book Review: This book serves as a comprehensive guide for statistics and probability courses offered across the country. The course is typically taught to students in their first to third year of college, after completing elementary courses. The book has been periodically updated to include all the relevant changes that occur each year. It covers essential topics like introduction to basic probability theory and statistical inference in subsequent chapters, providing a unique balance of theory and methodology.
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7."Probability and Statistics for Engineers and Scientists" by Walpole
“Probability and Statistics for Engineers and Scientists” Book Review: This book strikes a balance between analytical rigour and ease of comprehension. The latest edition features new chapters on important topics such as histograms, hypotheses, covariance, independent events, mean, and median. The book also contains numerous examples and exercises to help readers practice and test their understanding of the concepts covered. The book provides a comprehensive introduction to basic concepts of Probability, Statistics, and Random Variables. The diagrams are clearly labelled and the book also includes an excellent list of references. This book is useful for undergraduate students as well as those preparing for various competitive examinations.
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8."Probability - Statistics and Random Processes" by Veerarajan
“Probability – Statistics and Random Processes” Book Review: The book presents a comprehensive and concise explanation of various topics including the basic concepts of probability theory and statistical inference with a well-balanced blend of theory and methodology. Specifically designed for undergraduate engineering students, this book covers a broad range of important chapters such as the law of large numbers, independent events, normal distribution, scatter diagram, and more.
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9."Probability and Statistics" by E Rukmangadachari
“Probability and Statistics” Book Review: The aim of this textbook is to present the basic concepts of Probability, Statistics, and Random Variables as both a science and an art. To enhance clarity and understanding, the theories and explanations are backed up with numerous solved examples and additional figures. Pedagogically arranged multiple choice questions and other exercises are also provided to enable students to assess their subject knowledge. While primarily designed as a textbook for undergraduate and postgraduate students, it is also beneficial for undergraduate students of engineering.
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10."Probability and Statistics with Reliability, Queuing and Computer Science Applications" by Trivedi
“Probability and Statistics with Reliability, Queuing and Computer Science Applications” Book Review: This book explains statistics and probability in a clear and easy-to-understand way. It covers the topics taught in most universities and is suitable for both undergraduate and graduate students, as well as teachers. Each chapter has exercises at the end, divided into three types: descriptive, analytical, and objective, to help readers test their understanding of the subject. The book presents the practical methods of statistical modeling and data analysis in a logical sequence. The methods for analyzing and interpreting basic probability theory and statistical inference are explained clearly with practical examples to demonstrate their applications.
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## 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.
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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.
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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.
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4."Introduction to Mathematical Statistics" by Robert V Hogg | |

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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.
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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.
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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.
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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.
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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.
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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.
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11."Mathematical Statistics" by T S Ferguson | |

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## 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.
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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.
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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.
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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.
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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.
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6."Nonparametric Statistics for Behavioural Science" by Sidney Siegel and Castellan | |

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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.
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8."Nonparametric Statistics (The Six Sigma Research Institute Series)" by David C Sutor and Donald B White | |

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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.
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10."Nonparametric Statistics for Behavioural Science (Psychology)" by Sidney Siegel | |

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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.
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## 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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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## 6. Statistical Methods of Analysis and Design

1."Engineering Statistics" by A B Bowker and G J Liberman | |

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2."Statistics and Experimental Design in Engineering and the Physical Sciences" by N L Johnson and F C Xeen Leone | |

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3."Probability and Statistical Inference" by R V Hogg and E A Tanis
“Probability and Statistical Inference” Book Review: This introductory book on probability and statistics emphasizes the ubiquity of variation in almost all processes and the role of probability and statistics in understanding this variation. It is written in a student-friendly style and well-organized, with a balanced treatment of the topic. The book is divided into eleven chapters covering Probability, Discrete Distributions, Continuous Distributions, Bivariate Distributions, Distributions of Functions of Random Variables, Estimation, Tests of Statistical Hypotheses, Nonparametric Methods, Bayesian Methods, Some Theory, Quality Improvement Through Statistical Methods. The book also includes references and exercises.
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4."The Design and Statistical Analysis of Animal Experiments" by Dr Simon T Bate and Dr Robin A Clark
“The Design and Statistical Analysis of Animal Experiments” Book Review: This comprehensive guidebook covers the design and statistical analysis of animal experiments. It is tailored to practitioners and provides insight into the statistical tools employed in real-life experiments. With a range of design types, including block, factorial, nested, cross-over, dose-escalation, and repeated measures, readers can analyze the data generated experimentally. The book is accessible and describes key techniques in non-mathematical terms, making it easy for readers without a statistical background to follow along. Topics covered include statistical concepts, experimental design, randomization, statistical analysis, and analysis using In VivoStat, along with references and an index.
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5."Design of Experiments: Statistical Principles of Research Design and Analysis" by Robert
“Design of Experiments: Statistical Principles of Research Design and Analysis” Book Review: This comprehensive guide to the fundamental principles of experimental design and statistical analysis. The book offers practical guidance on planning, conducting, and analyzing experiments, including examples from a wide range of fields such as agriculture, engineering, and medicine. The author’s clear and concise writing style, along with numerous illustrations and real-world case studies, makes this book an excellent resource for both novice and experienced researchers. Overall, “Design of Experiments” is a valuable reference for anyone involved in scientific research.
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6."Propensity Score Analysis: Statistical Methods and Applications" by Shenyang Guo and Mark W Fraser
“Propensity Score Analysis: Statistical Methods and Applications” Book Review: This book provides an in-depth guide to the statistical technique of propensity score analysis. The book explains the methodology and applications of propensity score analysis in detail, with numerous examples and case studies. It covers topics such as propensity score matching, weighting, stratification, and multiple imputation. The authors provide clear and concise explanations, making the book accessible to both beginners and experts in the field. This comprehensive guide is a must-read for researchers and students who wish to apply propensity score analysis in their research.
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7."An Introduction to Statistical Methods and Data Analysis" by R Lyman Ott and Micheal T Longnecker
“An Introduction to Statistical Methods and Data Analysis” Book Review: This book is a comprehensive guide that covers the fundamental concepts and techniques of statistical analysis. The book is suitable for both undergraduate and graduate students and professionals who want to learn statistical analysis. The book includes practical examples and exercises to reinforce the concepts learned. The authors have used a clear and concise writing style that is easy to understand, making it an ideal resource for individuals with little or no background in statistics.
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8."Statistical Methods for Psychology" by David C Howell
“Statistical Methods for Psychology” Book Review: This is a useful textbook for students of psychology seeking to learn statistical concepts and their applications. The book introduces basic statistical concepts and then moves on to more advanced topics, including multiple regression and analysis of variance. Howell provides clear explanations and numerous examples, making the material accessible to readers without a strong mathematical background. The book also includes exercises and quizzes to help readers practice and test their understanding of the material. Overall, “Statistical Methods for Psychology” is a valuable resource for anyone seeking to understand statistical methods in psychology.
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9."Factor Analysis: Statistical Methods and Practical Issues" by Jae-On Kim and Charles W Mueller
“Factor Analysis: Statistical Methods and Practical Issues” Book Review: This book provides a comprehensive coverage of exploratory and confirmatory factor analysis as well as various methods of constructing factor scales. It explains the conceptual foundation of factor analysis, methods of extracting initial factors, rotation, and solving the number of factor problems. The book also introduces confirmatory factor analysis and covers the construction of factor scales with examples. In-depth details on initial factoring and factor rotation are provided.
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10."The Statistical Analysis of Experimental Data" by John Mandel
“The Statistical Analysis of Experimental Data” Book Review: This book teaches how to measure things precisely and solve statistical problems. It explains the fundamental mathematical concepts behind modern statistical theory and shows how to use statistics to interpret data. The book provides many examples with step-by-step instructions, figures, and tables to summarize each chapter. The book covers various topics, including measurement, statistical models, mathematical frameworks, accuracy and precision of measurements, fitting of curves and surfaces, testing statistical models, and principles of sampling.
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11."Basic Business Statistics: A Casebook (Textbooks in Matheamtical Sciences)" by Dean P Foster and Robert A Stine
“Basic Business Statistics: A Casebook(Textbooks in Mathematical Sciences)” Book Review: This book presents a set of real-life scenarios and how statistical analysis was used to address business-related inquiries. It offers a practical approach to the use of statistics without relying heavily on formulas, and emphasizes the relevance of statistical knowledge. The book is divided into 11 sections, each comprising a case study that begins with a business question and ends with a response to that question
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12."Mathematics And Statistics For Technologists" by H G Cuming and C J Anson | |

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## 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.
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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.
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3."Statistical Inference" by Kumar and Chaudhary | |

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4."Linear Statistical Inference and its Application" by C Radhakrishna Rao | |

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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.
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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.
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## 8. Statistical Modeling

1."Density Estimation for Statistics and Data Analysis" by B W Silverman
“Density Estimation for Statistics and Data Analysis” Book Review: This book is about density estimation techniques used in statistics and data analysis. It covers various topics such as the survey of existing methods, the kernel method for univariate and multivariate data, three important methods, and density estimation in action. The book contains around 50 graphs and figures that provide a clear understanding of the essential techniques. This book is useful for students of statistical mathematics and computational engineers.
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2."Tools for Statistical Inference" by M. A. Tanner
“Tools for Statistical Inference” Book Review: This book covers data augmentation methods and statistical inference tools. It discusses topics such as normal approximation to likelihoods and posteriors, the EM algorithm, data augmentation algorithm, and Markov chain Monte Carlo algorithm. The book provides examples with code for each algorithm. It is helpful for students studying data analysis and statistical mathematics
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3."An Introduction to the Bootstrap" by B Efron and R J Tibshirani
“An Introduction to the Bootstrap” Book Review: This book is an introduction to the bootstrap and computational techniques for analyzing challenging datasets. The primary topics covered include assessing the accuracy of a sample mean, random samples and probabilities, the empirical distribution function, and the plug-in principle. Additional topics covered are standard errors and estimated standard errors, the bootstrap estimate of standard errors, and regression models. Each chapter includes end-of-chapter problems to help students better understand the material. Bibliographic notes are provided at the end of each chapter. This book is useful for engineers and statisticians working in the field of data analysis.
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4."Statistical Digital Signal Processing and Modeling" by Monson H Hayes
“Statistical Digital Signal Processing and Modeling” Book Review: This book covers essential and advanced topics in digital signal processing and modeling. It begins with discrete time random processes and signal modeling, followed by more advanced topics such as the Levinson recursion and lattice filters. Other important topics discussed in this book include Wiener filtering, spectrum estimation, adaptive filtering, and recursive least squares. Each chapter concludes with practice problems and their solutions. The book also includes MATLAB programs and tables of symbols. It is recommended for statisticians and engineers working in the field of electronics and communication.
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5."Modeling and Simulation Using MATLAB - Simulink: For ECE" by Dr Shailendra Jain
“Modeling and Simulation Using MATLAB – Simulink: For ECE” Book Review: This book provides an overview of modelling and simulation using MATLAB software. It covers essential topics like MATLAB basics, programming in MATLAB, and basic electrical and network applications. Additionally, the book includes chapters on Simulink introduction, fuzzy logic, and artificial neural network applications. All the topics are supported with relevant codes to enhance the reader’s understanding. The book is particularly useful for students pursuing electrical and electronics engineering.
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6."Applied Predictive Modeling" by Max Kuhn and Kjell Johnson
“Applied Predictive Modeling” Book Review: This book explores the techniques of predictive modeling. It covers a range of topics, including data pre-processing, overfitting and model tuning, regression models, and measuring performance in regression models. Additionally, the book delves into topics like nonlinear regression models, regression trees, rule-based models, classification models, and measuring predictor importance. Each chapter includes exercises to help students practice what they have learned, and case studies are provided to give students practical knowledge. This book is suitable for advanced mathematics students and engineers working in the field of data processing and analysis.
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7."Statistical and Adaptive Signal Processing: Spectral Estimation, Signal Modeling, Adaptive Filtering" by Dimitris G Manolakis and Vinay K Ingle
“Statistical and Adaptive Signal Processing: Spectral Estimation, Signal Modeling, Adaptive Filtering” Book Review: This book presents statistical and adaptive methods for signal processing. It covers essential topics such as spectral estimation, signal modeling, adaptive filtering, and array processing. The book contains over 3000 equations and 300 illustrations to help students learn and understand the material. Additionally, the book provides MATLAB functions that solve real-world problems. This book is recommended for students studying electrical and electronic engineering, as well as mathematicians interested in signal processing.
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8."Statistical Rethinking: A Bayesian Course with Examples in R and Stan" by Richard McElreath
“Statistical Rethinking: A Bayesian Course with Examples in R and Stan” Book Review: This book explains the Bayesian Inference with practical examples in R and Stan programming languages. The main topics covered include the Golem of Prague, Small Worlds and Large Worlds, Sampling the Imaginary, and Geometric Methods. Additional chapters such as The Many Variables and the Spurious Waffles, The Haunted DG and the Casual Terror are also discussed. Each unit ends with a practice section to test students’ knowledge and for practicing purposes. Complete R code examples are provided throughout the book. This book is intended for PhD students and experienced professionals in the field of specialized statistical modeling.
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9."Statistical Modeling and Computation" by Dirk P Kroese and Joshua C C Chan
“Statistical Modeling and Computation” Book Review: This book covers various topics related to statistical modeling and computation. It begins with an introduction to probability models, random variables, and probability distribution, and goes on to discuss joint distribution, statistical inference, common statistical models, likelihood, and Monte Carlo sampling. Each chapter includes exercises for students to practice. Additionally, the book includes an appendix with Matlab programs. It is a suitable textbook for undergraduate students.
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10."Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis" by Bruce Ratner
“Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis” Book Review: This book provides an in-depth understanding of statistical and machine learning techniques for data mining. The main topics covered in this book are the science of dealing with data, two basic data mining methods for variable assessment, and CHAID-based data mining for paired variable assessment. Other topics covered include symmetrizing ranked data, the importance of straight data, principal component analysis, and market share estimation. With a total of 43 chapters, this book discusses various quantitative techniques used in the field of data mining. Some chapters also include case studies to provide practical knowledge. This book is particularly useful for graduate students studying data mining and predictive modeling.
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11."Spatial Statistics and Computational Methods (Lecture Notes in Statistics)" by Jesper Møller
“Spatial Statistics and Computational Methods (Lecture Notes in Statistics)” Book Review: This book offers a comprehensive coverage of current topics in spatial and computational statistics, making it an ideal resource for both postgraduate students and experienced statistical researchers. It provides insights into how sophisticated spatial statistical and computational methods can be used to tackle an array of problems that are becoming increasingly important in various fields of science and technology.
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## 9. Applied Statistics

1."Probability Concepts in Engineering Planning and Design" by Ang H S and Tang W H
Book Review: This book is a comprehensive resource that introduces mathematical concepts and provides practical examples of engineering problems. It is particularly useful for engineers who need a working knowledge of the basic concepts and tools of probability. The book is designed to help students and engineers learn probability as part of their professional engineering education. It includes two main parts: the first part covers basic concepts and methods of probability and statistics, while the second part focuses on advanced concepts and applications such as risk analysis, reliability analysis, and probability-based design. Overall, this book is an excellent textbook for both students and practicing engineers in various engineering fields.
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2."Probability Statistics and Decision for Civil Engineers" by Benjamin J R and Cornell C A
“Probability Statistics and Decision for Civil Engineers” Book Review: This updated and revised book covers the latest theories and recent developments in civil engineering, addressing both theoretical and practical aspects of strength of materials, soil mechanics, construction planning, and water resource design. It also introduces concepts of statistics and probability, making it a comprehensive resource for both students and professionals in the field of civil engineering.
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3."Probability and Statistics for Engineers" by Little R E | |

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4."Analysis and Adjustment of Survey Measurements" by Mikhail E M and Gracie G | |

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5."Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control" by James Spall
“Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control” Book Review: This comprehensive book delves into both the theoretical and practical aspects of stochastic algorithms, providing a detailed overview of all the key concepts in statistics as well as research papers on stochastic algorithms. It features numerous exercises and web links to support the content, making it an ideal resource for students interested in researching stochastic optimization.
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6."Contributions to a General Asymptotik Statistical Theory" by Pfanzagl Wefelmeyer
“Contributions to a General Asymptotik Statistical Theory” Book Review: This book offers a well-balanced approach covering both the theoretical and practical aspects of asymptotic statistical theory. It discusses various important topics including tangent cones and provides extensive examples and answers to questions related to convex tangent cones. The text includes many case studies to illustrate the concepts presented. The book is specifically beneficial for civil engineering students seeking to gain a deeper understanding of asymptotic statistical theory.
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7."Control Theoretic Splines: Optimal Control, Statistics, and Path Planning" by Clyde Martin
“Control Theoretic Splines: Optimal Control, Statistics, and Path Planning” Book Review: This book provides an in-depth study of the construction of curved sets of raw data, which is relevant to various areas including robotics, computer theory, engineering, and econometrics. The chapters of this book cover important topics such as periodic spline, monotone splines, and optimization tools over vector space. Many live case studies are included to provide practical examples of computer technology development and solutions to various statistical problems. This book is suitable for students, researchers, professionals, and engineers interested in the development of these fields
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8."Applied Statistics and Probability for Engineers" by Douglas C Montgomery
“Applied Statistics and Probability for Engineers” Book Review: This book provides a comprehensive understanding of statistics from both theoretical and practical perspectives. It covers the latest information and practical approaches to solving real-world problems. It is an invaluable resource for students and professionals in statistics, probability, physical and chemical sciences, electrical and mechanical engineering, and material sciences. Whether you are seeking an introduction to the subject or an in-depth review, this book is a valuable reference.
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9."Probability and Statistical Models: Foundations for Problems in Reliability and Financial Mathematics" by Arjun K Gupta
“Probability and Statistical Models: Foundations for Problems in Reliability and Financial Mathematics” Book Review: The primary objective of this book is to provide up-to-date knowledge and fundamental concepts of stochastic modeling. It also focuses on the application of numerical techniques to solve real-world problems in fields such as reliability, insurance, finance, and credit risk. Advanced concepts in the subject are also covered. Written from both theoretical and practical perspectives, the book offers easy-to-understand knowledge and includes project definitions. It is a valuable resource for students and professionals in mathematics, statistics, and economics alike.
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10."Stochastic Differential Games. Theory and Applications" by Chris P Tsokos Kandethody M Ramachandran Tsokos RAMACHANDRAN
“Stochastic Differential Games. Theory and Applications” by Book Review: The focus of this book is on both the practical and theoretical knowledge of various theories of finance, economics, and health investments. The chapters cover important topics and use mathematical and computational methods. The book provides the latest information on practical approaches to problem sets in realistic situations. It is a valuable resource for students and professionals in industrial engineering, finance, economics, investment strategies, health sciences, and the environment.
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11."The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)" by Trevor Hastie and Robert Tibshirani
“The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)” Book Review: This comprehensive book presents important ideas in a variety of fields, including medicine, biology, finance, and marketing, with a focus on concepts rather than mathematics, although the approach is statistical. Numerous examples are provided, with a generous use of colourful graphics. The book covers a range of topics, including neural networks, support vector machines, classification trees, and boosting. It is an invaluable learning resource for statisticians and anyone interested in data mining for scientific or industrial applications.
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12."Statistics: A Guide For Therapists" by John McCall BSc MSc
“Statistics: A Guide For Therapists” Book Review: This book is designed to address the evolving practice and education of therapy. It offers a practical set of skills that are essential for effective therapy. The primary audience for this book is therapists, and it provides the necessary information to assist them in their clinical practice. The book also provides a clear understanding of statistical procedures, with a focus on practical applications. It aims to provide a basic understanding of commonly used methods and terms.
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13."Statistics for Food Scientists: Making Sense of the Numbers" by Frank Rossi and Victor Mirtchev
“Statistics for Food Scientists: Making Sense of the Numbers” by Frank Rossi and Victor Mirtchev Book Review: This book is a valuable resource for food scientists and researchers. The book offers a comprehensive introduction to the principles of statistics and its application in the field of food science. It includes numerous practical examples and exercises to help readers understand and apply statistical concepts. The authors also discuss data analysis and interpretation, experimental design, and sampling techniques. Overall, this book provides a solid foundation for anyone interested in statistics and its relevance to food science research.
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14."Statistics for Engineering and the Sciences" by Terry L Sincich and William M Mendenhall
“Statistics for Engineering and the Sciences” by Terry L Sincich and William M Mendenhall Book Review: This book presents a comprehensive analysis of various tests used to study charge transport in proteins and proposes a unified theoretical model to interpret the observable outcomes related to the amino acid backbone structure of a single protein. It explores the development of new molecular devices based on proteins, such as nanometric organic sensors of a new era. The book also reviews existing data and lays the foundation for future development of a new aspect of nanodevices.
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15."Schaum's Outline of Statistics in Psychology (Schaums' Humanities Social Science)" by Larry J Stephens
“Schaum’s Outline of Statistics in Psychology (Schaums’ Humanities Social Science)” by Larry J Stephens Book Review: This book, Schaum’s Outline of Measurements in Psychology, provides students with an understanding of fundamental concepts and offers additional practice on topics such as frequency distributions, central tendency, inferential statistics, probability and tests, z scores, t-Test, correlations, and nonparametric tests. It also includes coverage of the design of experiments and surveys, their implementation, and the statistical tasks required to analyze data using these methods. A special section on computer use for specific statistical tasks has also been included.
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16."Contributions to Sampling Statistics (Contributions to Statistics)" by Maria Giovanna Ranalli and Fulvia Mecatti
“Contributions to Sampling Statistics (Contributions to Statistics)” Book Review: This book is a compilation of papers presented at the ITACOSM 2013 Conference, covering important aspects of sampling methodology and techniques. It also delves into other significant topics such as calibration, quantile-regression, and multiple frame surveys. The book has been updated to exclude or modify some outdated strategies, making it a reliable and up-to-date study guide for researchers, professionals, and practitioners in the field.
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17."Advances and Challenges in Space-time Modelling of Natural Events (Lecture Notes in Statistics)" by Emilio Porcu and José–María Montero
“Advances and Challenges in Space-time Modelling of Natural Events (Lecture Notes in Statistics)” Book Review: The book provides insight into recent advances, novel methods, and practical applications in spatial statistics and related fields. It serves as a follow-up to the International Spring School “Advances and Challenges in Space-Time Modelling of Natural Events,” which occurred in Toledo, Spain in March 2010. This book targets young researchers, master’s students, PhD candidates, and postdoctoral researchers from academia, research institutions, and industry.
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## 10. Applied Statistics and Mathematical Statistics

1."Apllied Statistics and Mathematical Statistics" by S C Gupta
“Applied Statistics and Mathematical Statistics” Book Review: This book is mainly designed for undergraduate and postgraduate students and offers practical applications of statistics in various fields such as industry, economics, agriculture, demography, education, and psychology. It assumes prior knowledge of calculus. The book features numerous illustrations and problems. Topics covered include statistical quality control, time series, index numbers, demand analysis, analysis of variance, design of experiments, design of sample surveys, psychological and educational statistics, and vital statistics. Each chapter concludes with exercises to aid in comprehension and retention.
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2."Applied Statistics" by Parimal Mukhopadhyay
“Applied Statistics” Book Review: This book is an ideal resource for students and practitioners in statistics. It covers a wide range of topics, including probability distributions, estimation, hypothesis testing, linear regression analysis, experimental designs, non-parametric tests, and time series analysis. The book also includes discussions on the application of statistical methods in real-world scenarios such as quality control, reliability analysis, and survival analysis. The author presents the concepts in an easily understandable language and provides ample examples and exercises for better comprehension. This book is a valuable reference for anyone interested in applied statistics.
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3."Methods of Mathematics Applied to Calculus, Probability, and Statistics (Dover Books on Mathematics)" by Richard W Hamming
“Methods of Mathematics Applied to Calculus, Probability, and Statistics (Dover Books on Mathematics)” Book Review: The contents of this book encompass the practical use of mathematical methods, along with subjects related to essential fields like probability and statistics. Proficiency in calculus is essential to comprehend mathematics in various domains. The book features a comprehensive four-part approach, including algebra and analytic geometry, algebraic and transcendental function calculus, and their applications. Furthermore, the book includes three useful appendices and practice exercises with answers. This guide caters to advanced undergraduates and graduate students, and professionals can also benefit from it as a practical reference.
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4."Applied Statistics for Business and Economics" by Robert M Leekley
“Applied Statistics for Business and Economics” Book Review: This book is an easy-to-understand guide for students in business and social sciences to learn some basic methods of understanding the world. The book uses standard spreadsheet packages to perform calculations and has real-life examples to explain concepts better. The book covers topics such as probability, estimation, hypothesis samples, and analysis of variance. It also teaches how to analyze relationships between variables and time-series analysis. The book is useful for students who want to learn practical statistics. It will teach them how to summarize data using charts and graphs and make inferences from what they learn in the book.
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5."Applied Statistics: A Handbook of Techniques (Springer Series in Statistics)" by Lothar Sachs and Zenon Reynarowych
“Applied Statistics: A Handbook of Techniques (Springer Series in Statistics)” Book Review: This book is helpful for research workers and consulting statisticians. It explains important statistical methods in a simple way, without using too much complicated math. It focuses on the basic principles of statistics and explains when to use certain formulas and tests. The book is for anyone who wants to learn about statistics, including non-mathematicians, technicians, engineers, executives, students, and researchers in other fields. It prefers to use small samples and distribution-free methods. The book has 440 examples, 57 exercises with solutions, and many tools to help with calculations. It also includes 232 tables. This book will teach mathematicians how to apply statistics practically.
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6."Applied Statistics for Business and Management using Microsoft Excel" by Linda Herkenhoff and John Fogli
“Applied Statistics for Business and Management using Microsoft Excel” Book Review: This book teaches how to use Microsoft Excel for applied statistics. It is a guide for students and practitioners who want to learn how to solve practical statistical problems in their industry using Excel. Even students who are good at math but not comfortable with computers can benefit from this book. It explains how to apply Excel to statistics in courses and workplaces. Excel’s computational power and graphing functions make learning statistics easier. Each chapter includes statistical formulas, practice problems with solutions to help solve real business problems.
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7."Computational Electromagnetics (Texts in Applied Mathematics)" by Anders Bondeson and Thomas Rylander
“Computational Electromagnetics (Texts in Applied Mathematics)” Book Review: This book talks about using computers to solve problems in electromagnetics. It includes “matlab code” throughout the book. The book has different techniques and exercises to help you learn. It teaches the most popular ways to solve electromagnetic field problems: finite difference method, finite element method, and method of moments. This book is good for students who have some knowledge of electromagnetic field theory, numerical analysis, and MATLAB programming. It also covers important topics such as Maxwell’s equations, convergence analysis, extrapolation, von Neumann stability analysis, and dispersion analysis.
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8."Mathematical Foundations of Neuroscience (Interdisciplinary Applied Mathematics)" by G Bard Ermentrout and David H Terman
“Mathematical Foundations of Neuroscience (Interdisciplinary Applied Mathematics)” Book Review: This book explains how to use math to understand how the brain works. It uses modern mathematical methods to create models of how neurons behave. It’s helpful for researchers who like math and neuroscience, and for neuroscientists who want to learn how to make models and analyze them. The book talks about different methods like numerical, analytical, dynamical systems, and perturbation. It also covers topics like noise, multiple time scales, and spatial interactions that create complex patterns in the brain. The book has lots of pictures, summaries, and exercises about biology, math, and analysis. You only need to know basic math like calculus and differential equations to understand it.
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9."Methods of Applied Mathematics (Dover Books on Mathematics)" by Francis Begnaud Hildebrand | |

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10."Statistical Learning Theory" by Vladimir N Vapnik
“Statistical Learning Theory” Book Review: This book is split into three parts. The first section talks about the Theory of Learning and Generalization. It covers different ways to approach learning and how to estimate Probability Measure. The second section focuses on Support Vector Estimation of Functions. It explains perceptrons and their variations, as well as SV Machines used for Function Approximations. The final section is about the Statistical Foundation of Learning Theory. It includes Necessary conditions for uniform convergence of frequencies to their probabilities, as well as necessary and sufficient conditions for uniform one-sided convergence of means.
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11."Essentials of Statistics for Scientists and Technologists" by C Mack
“Essentials of Statistics for Scientists and Technologists” Book Review: This book is designed for people with limited mathematical knowledge, who are able to substitute numerical values in simple formulas. Some mathematical proofs are included to illustrate the derivation of the subject. The book aims to explain the important tests and methods that a scientist or technologist may need, and to demonstrate the relationship between probability and the proportion of a population. It covers topics such as normal distribution, sampling, significance of tests, and analysis of variance. The book is useful for scientists who want to understand essential statistical methods.
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