Statistical Modeling: A Fresh Approach

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.

Daniel Kaplan is DeWitt Wallace Professor of Mathematics and Computer Science at Macalester College where he teaches statistics, applied mathematics, and scientific computing. He earned a B.A. in physics from Swarthmore College and a Ph.D. in biomedical physics from Harvard University. He is a winner of Macalester's Excellence in Teaching award. Other books by the author: Understanding Nonlinear Dynamics Introduction to Scientific Computation and Programming