This book review section covers several statistical and computational methods, focusing on their applications and practical aspects. The first review discusses "Genomic Clinical Trials and Predictive Medicine" by Richard M. Simon, which explores the use of genomic data in designing and analyzing clinical trials, particularly in oncology. The book provides practical examples and covers topics such as phase II and III trials, Bayesian adaptive designs, and the development of predictive markers. The second review introduces "Statistical Learning with Sparsity: The Lasso and Generalizations" by Trevor Hastie, Robert Tibshirani, and Martin Wainwright. This book focuses on sparse statistical modeling, emphasizing models with only a few non-zero parameters. It is suitable for graduate students and practitioners with a basic understanding of probability, statistics, and numerical analysis. The third review is of "Bayesian Nonparametric Data Analysis" by Peter Müller, Fernando Andres Quintana, Alejandro Jara, and Tim Hanson. This book provides a comprehensive review of non-parametric Bayesian methods, including density estimation, regression, and survival analysis. It is well-structured and includes numerous examples and applications. The fourth review discusses "Current Trends in Bayesian Methodology with Applications" by Satyanshu K. Upadhyay, Umesh Singh, Dipak K. Dey, and Appaia Loganathan. This book covers a wide range of Bayesian methodologies and their applications in various fields, making it a valuable resource for researchers and practitioners. The fifth review is of "Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction" by Guido W. Imbens and Donald B. Rubin. This book provides a comprehensive overview of causal inference, covering theoretical foundations, experimental design, and real-world applications. It is well-written and includes detailed proofs and examples. The sixth review introduces "Bayesian and Frequentist Regression Methods" by Jon Wakefield. This book offers a detailed comparison of Bayesian and frequentist approaches to regression models, suitable for advanced students and researchers. It covers a wide range of topics, including linear, generalized linear, and non-parametric regression models. The seventh review is of "John Napier: Life, Logarithms and Legacy" by Julian Havil. This book delves into the history of logarithms and John Napier's contributions to mathematics, providing a thorough analysis of his publications and their historical context. The eighth review discusses "Analysis of Categorical Data with R" by Christopher R. Bilder and Thomas M. Loughin. This book provides an extensive introduction to analyzing categorical data using R, covering recent model-building techniques and practical examples. The ninth review is of "Meta-Analysis: A Structural Equation Modeling Approach" by Mike W.-L. Cheung. This book focuses on conducting meta-analyses within the structural equation modeling framework, offering practical examples and illustrations using the R-package metaSEM.This book review section covers several statistical and computational methods, focusing on their applications and practical aspects. The first review discusses "Genomic Clinical Trials and Predictive Medicine" by Richard M. Simon, which explores the use of genomic data in designing and analyzing clinical trials, particularly in oncology. The book provides practical examples and covers topics such as phase II and III trials, Bayesian adaptive designs, and the development of predictive markers. The second review introduces "Statistical Learning with Sparsity: The Lasso and Generalizations" by Trevor Hastie, Robert Tibshirani, and Martin Wainwright. This book focuses on sparse statistical modeling, emphasizing models with only a few non-zero parameters. It is suitable for graduate students and practitioners with a basic understanding of probability, statistics, and numerical analysis. The third review is of "Bayesian Nonparametric Data Analysis" by Peter Müller, Fernando Andres Quintana, Alejandro Jara, and Tim Hanson. This book provides a comprehensive review of non-parametric Bayesian methods, including density estimation, regression, and survival analysis. It is well-structured and includes numerous examples and applications. The fourth review discusses "Current Trends in Bayesian Methodology with Applications" by Satyanshu K. Upadhyay, Umesh Singh, Dipak K. Dey, and Appaia Loganathan. This book covers a wide range of Bayesian methodologies and their applications in various fields, making it a valuable resource for researchers and practitioners. The fifth review is of "Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction" by Guido W. Imbens and Donald B. Rubin. This book provides a comprehensive overview of causal inference, covering theoretical foundations, experimental design, and real-world applications. It is well-written and includes detailed proofs and examples. The sixth review introduces "Bayesian and Frequentist Regression Methods" by Jon Wakefield. This book offers a detailed comparison of Bayesian and frequentist approaches to regression models, suitable for advanced students and researchers. It covers a wide range of topics, including linear, generalized linear, and non-parametric regression models. The seventh review is of "John Napier: Life, Logarithms and Legacy" by Julian Havil. This book delves into the history of logarithms and John Napier's contributions to mathematics, providing a thorough analysis of his publications and their historical context. The eighth review discusses "Analysis of Categorical Data with R" by Christopher R. Bilder and Thomas M. Loughin. This book provides an extensive introduction to analyzing categorical data using R, covering recent model-building techniques and practical examples. The ninth review is of "Meta-Analysis: A Structural Equation Modeling Approach" by Mike W.-L. Cheung. This book focuses on conducting meta-analyses within the structural equation modeling framework, offering practical examples and illustrations using the R-package metaSEM.