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The goal of this book is to take statistical theory in correlation and regression and make it accessible to readers using words, equations, and a variety of applied examples (which help explain how the techniques work and under what circumstances some creativity in application is necessary). The book's goal is practical, and the tone is commonsensical. These objectives reflect the underlying philosophy that the application of correlation and regression techniques are not necessarily straightforward, and require thinking about each technique in creative ways.
The first half of the book covers topics in correlation (calculating and interpreting r, hypothesis testing of correlations, applications in measurement, the effects of range restriction, etc.). The second half of the book is devoted to regression topics (parameter estimates in bivariate and multivariate regression, distribution theory, applications to utility analysis, polynomials, interactions, validity shrinkage, etc.). Within each chapter, examples are provided which practitioners of science will undoubtedly encounter in some form or another. While some chapters cover the traditional topics in regression and correlation, other chapters in this book are written entirely as examples of applications issues. This makes the book unique. Chapter IV is devoted to the ubiquitous problem of "measurement" in the social sciences and how correlations help us to better assess scientific phenomena. Chapter V is devoted to the problem of "range restriction." It is a common fact of life, yet no other book devotes the necessary space to this critical problem. Chapter VII is specifically focused on applications of bivariate regression (e.g., regression to the mean; utility analysis). Finally, Chapters IX and X give more extended coverage of interactive models and validity shrinkage (two very important issues) than you will find in other regression books.
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Philip Bobko is Professor of Management at Rutgers University.
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