Applied Regression Including Computing and Graphics

by Cook, R. Dennis; Weisberg, Sanford

• ISBN13: 9780471317111
• ISBN10: 047131711X
• Edition: 1st
• Format: Hardcover
• Copyright: 1999-08-06
• Publisher: Wiley-Interscience

Summary

A step-by-step guide to computing and graphics in regression analysis In this unique book, leading statisticians Dennis Cook and Sanford Weisberg expertly blend regression fundamentals and cutting-edge graphical techniques. They combine and up- date most of the material from their widely used earlier work, An Introduction to Regression Graphics, and Weisberg's Applied Linear Regression; incorporate the latest in statistical graphics, computing, and regression models; and wind up with a modern, fully integrated approach to one of the most important tools of data analysis. In 23 concise, easy-to-digest chapters, the authors present:? A wealth of simple 2D and 3D graphical techniques, helping visualize results through graphs * An improved version of the user-friendly Arc software, which lets readers promptly implement new ideas * Complete coverage of regression models, including logistic regression and generalized linear models * More than 300 figures, easily reproducible on the computer * Numerous examples and problems based on real data * A companion Web site featuring free software and advice, available at www.wiley.com/mathem atics Accessible, self-contained, and fully referenced, Applied Regression Including Computing and Graphics assumes only a first course in basic statistical methods and provides a bona fide user manual for the Arc software. It is an invaluable resource for anyone interested in learning how to analyze regression problems with confidence and depth.

Author Biography

R. DENNIS COOK, PhD, is Professor of Statistics at the University of Minnesota.<br> <br> D WEISBERG, PhD, is Professor of Statistics and Director of the Statistical Consulting Service at the University of Minnesota.

Table of Contents

Looking Forward and Back.

Introduction to Regression.

Introduction to Smoothing.

Bivariate Distributions.

Two-Dimensional Plots.

TOOLS.

Simple Linear Regression.

Introduction to Multiple Linear Regression.

Three-Dimensional Plots.

Weights and Lack-of-Fit.

Understanding Coefficients.

Relating Mean Functions.

Factors and Interactions.

Response Transformations.

Diagnostics I: Curvature and Nonconstant Variance.

Diagnostics II: Influence and Outliers.

Predictor Transformations.

Model Assessment.

REGRESSION GRAPHICS.

Visualizing Regression.

Visualizing Regression with Many Predictors.

Graphical Regression.

LOGISTIC REGRESSION AND GENERALIZED LINEAR MODELS.

Binomial Regression.

Graphical and Diagnostic Methods for Logistic Regression.

Generalized Linear Models.

Appendix.

References.

Indexes.