Research Catalog

Title

An introduction to regression graphics / R. Dennis Cook, Sanford Weisberg.

Author

Cook, R. Dennis.

Publication

New York : Wiley, [1994], ©1994.

Items in the Library & Off-site

Holdings

Details

Additional Authors

Weisberg, Sanford, 1947-

Description

xx, 253 pages : illustrations; 25 cm +

Summary

• Understanding how a response variable depends on one or more predictor variables is a universal scientific problem. Regression analysis consists of ideas and methods for addressing this problem. Historically, regression methods have been largely numerical, with graphics playing an important but subsidiary role. By allowing informative and novel visualizations of regression data, modern computer hardware and software promise to reverse the historical roles of numerical and graphical regression methods.
• How shall this be done in practice? What can be learned from graphs and which graphs should be drawn? How can graphs be used to learn about fundamental features of regression problems?
• . An Introduction to Regression Graphics answers these questions and more, providing the ideas, methodology, and software needed to use graphs in regression. From simple manipulations, such as changing the aspect ratio and marking points, to more sophisticated ideas like extracting smooths or looking at uncorrelated directions in 3D plots, R. Dennis Cook and Sanford Weisberg provide step-by-step software instructions and concise explanations of how graphs can be used in almost any regression problem.

Series Statement

Wiley series in probability and mathematical statistics

Uniform Title

Wiley series in probability and mathematical statistics.

Alternative Title

Regression graphics

Subject

Regression analysis > Data processing

Note

• "A Wiley-Interscience publication."
• System requirements for computer disks: Macintosh and System software (1st disk) or IBM PC and DOS (2nd disk).

Bibliography (note)

• Includes bibliographical references (p. 241-244) and index.

Contents

1. Getting Started -- 2. Simple Regression Plots -- 3. Two-Dimensional Plots -- 4. Scatterplot Matrices -- 5. Three-Dimensional Plots -- 6. Visualizing Linear Regression with Two Predictors -- 7. Visualizing Regression without Linearity -- 8. Finding Dimension -- 9. Predictor Transformations -- 10. Response Transformations -- 11. Checking Models -- 12. Assessing Predictors -- 13. Influence and Outliers -- 14. Confidence Regions -- App. A: The R-code.

ISBN

0471008397 (acid-free paper)

LCCN

94019291

OCLC

• 963285952
• ocn963285952

Owning Institutions

Columbia University Libraries