Statistical Modeling: A Fresh Approach

Statistical Modeling: A Fresh Approach (second edition) 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 appear widely in published research but are 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.

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Praise for A Fresh Approach

If we statistician/teachers can follow this example of teaching modeling in a first stat course, in such an accessible, thoughtful, statistically sophisticated way, the consequences for the future of our profession could be profound.“— Prof. George Cobb, Mount Holyoke College

Brings a refreshing approach to data and statistics, laying a foundation for statistical modeling. The goal of modeling is immediately apparent: reliable answers and useful predictions. I am thankful that this textbook is available to my students.”— Prof. Roy Henk, Kyoto University

An outstandingly good introduction to statistics.“— Dr. Geoff Smith, MBE, University of Bath

We’ve been hearing a lot recently about the need to incorporate much more realistic and complicated statistical models into our introductory statistics courses. This book does that and more, with appropriate computing support in R and lots of interesting and sophisticated models.“— Prof. Nicholas Horton, Amherst College

I have taught introductory statistics and mathematical statistics many times. The standard syllabi are heavy on calculation and technique, just like those for calculus and for differential equations. Students can “succeed” in any of those courses without becoming able to apply their learning to a real-world situation. What tends to be missing in such courses is the learning of modeling, the step from a real-world problem to a mathematical formulation. It is a hard step, because it requires—besides a mathematical toolbox—some understanding of the context of the particular problem, plus the interest and curiosity to try to solve it. This book teaches the modeling step in statistics—in marvelous fashion!—without assuming any previous knowledge of statistics. The last section of each chapter is devoted to “computational technique,” but there are no traditional statistical formulas. Computations are accomplished in a carefully chosen subset of R, through the front end RStudio (both are free for all platforms, hence have the advantage of being accessible to students throughout their lives)“. — Review from Mathematics Magazine 85(2012):302