Daniel Kaplan

The 2nd edition (green cover) is now available and the first edition (brown cover) is now obsolete. The new edition makes use of the MOSAIC package in R (see www.mosaic-web.org/StatisticalModeling) introduces inference earlier, and incorporates suggestions and corrections offered by readers of the first edition. We continue to make the first edition available for students seeking to match the book used in a class that still uses the first edition. Statistical Modeling: A Fresh Approach introduces and illuminates the statistical reasoning used in modern research throughout the natural and social sciences, medicine, government, and commerce. It emphasizes the use of models to untangle and quantify variation in observed data. By a deft and concise use of computing coupled with an innovative geometrical presentation of the relationship among variables, A Fresh Approach reveals the logic of statistical inference and empowers the reader to use and understand techniques such as analysis of covariance that are widely used in published research but hardly ever found in introductory texts.Recognizing the essential role the computer plays in modern statistics, A Fresh Approach provides a complete and self-contained introduction to statistical computing using the powerful (and free) statistics package R.Exercises, software and datasets for the book are available at http://www.mosaic-web.org/StatisticalModeling.

A Fresh Approach

Statistical Modeling

Benjamin Baumer

Nicholas J. Horton

Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world problems with data. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling statistical questions. Contemporary data science requires a tight integration of knowledge from statistics, computer science, mathematics, and a domain of application. This book will help readers with some background in statistics and modest prior experience with coding develop and practice the appropriate skills to tackle complex data science projects. The book features a number of exercises and has a flexible organization conducive to teaching a variety of semester courses.

Modern Data Science with R

Barbara Berkman

Revised edition of Handbook of social work in health and aging, 2006.

The Oxford Handbook of Social Work in Health and Aging

Leon Glass

Mathematics is playing an ever more important role in the physical and biological sciences, provoking a blurring of boundaries between scientific disciplines and a resurgence of interest in the modern as well as the classical techniques of applied mathematics. This renewal of interest, both in research and teaching, has led to the establishment of the series: Texts in Applied Mathematics ( TAM). The development of new courses is a natural consequence of a high level of excitement on the research frontier as newer techniques, such as numerical and symbolic computer systems, dynamical systems, and chaos, mix with and reinforce the traditional methods of applied mathematics. Thus, the purpose of this textbook series is to meet the current and future needs of these advances and encourage the teaching of new courses. TAM will publish textbooks suitable for use in advanced undergraduate and beginning graduate courses, and will complement the Applied Mathematical Sciences (AMS) series, which will focus on advanced textbooks and research level monographs. About the Authors Daniel Kaplan specializes in the analysis of data using techniques motivated by nonlinear dynamics. His primary interest is in the interpretation of irregular physiological rhythms, but the methods he has developed have been used in geo physics, economics, marine ecology, and other fields. He joined McGill in 1991, after receiving his Ph.D from Harvard University and working at MIT. His un dergraduate studies were completed at Swarthmore College. He has worked with several instrumentation companies to develop novel types of medical monitors.

Daniel Kaplan

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