Mathematical Statistics with Applications

Wackerly/Chen/Loy’s “Mathematical Statistics with Applications” 8th Edition, with WebAssign, builds a solid foundation in statistical theory while conveying its relevance in solving practical problems in the real world. You'll discover the nature of statistics and understand its essential role in scientific research. The focused approach emphasizes the connectivity of key concepts with statistical inference being the primary theme. The new edition offers enhanced insights and cutting-edge knowledge on theory and applications of statistics today, including new methodologies in data science, statistical learning and biostatistics.

Professor John T. Chen has been teaching mathematical statistics since 1998 at various universities including, The University of Sydney (1996–1997, Australia), McMaster University (1997–1998 Canada), University of Pittsburg (1998–2000), Bowling Green State University (2000–present), University of Michigan (2010 fall) and University of California, Berkeley (2017 fall). He has published two books, one on multivariate Bonferroni inequalities and another on prediction and statistical learning. Dr Chen’s research comprises of theoretical topics on probability inequalities, distribution theory and simultaneous inference. This aspect is featured by papers published in Biometrika, the Annals of the Institute of Mathematical Statistics, Journal of Applied Probability, among others. Besides theoretical statistics, his research also embraces applications of statistical methodologies to medical investigations and biostatistical consulting. This is reflected by papers published in Biometrics, the Annals of Neurology, The Annals of Thoracic Surgery, Journal of Vascular Surgery, among others. Dr. Chen enjoys cooperating rigorous research thinking and cutting-edge applications of statistical practices into classrooms to inspire students. With his experience and teaching efforts, Dr. Chen has earned teaching-related awards including, Teaching Excellence Awards by the Kappa Mu Epsilon Mathematics Honorary Society (2002 and 2006, BGSU chapter), Appreciations of Faculty Excellence (2019, 2020, 2021, BGSU), Certificate in Effective College Instruction recognized by the Association of College and University Educators and the American Council on Education (2023) and BGSU president’s Innovation award in AI teaching and learning (2024). Part of the materials in this book stem from his teaching notes and lesson plans accumulated over years of his enriched teaching experience.

Adam Loy is an associate professor of statistics at Carleton College. He teaches all levels of the statistics curriculum, including probability and mathematical statistics. Dr. Loy’s research focuses on incorporating realistic models, computation and visualization into the classroom, exploring the potential of visual inference, developing better visualizations to explore complex models and developing useful and usable R packages. He has publications in a variety of statistics journals including, the Journal of Statistics and Data Science Education, the Journal of Computational and Graphical Statistics and The R Journal, among others. Dr. Loy is currently an associate editor for both the Journal of Statistics and Data Science Education and the R Journal.