Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

Guided Regression Modeling for Prediction and Exploration of Structure with Many Explanatory Variables

J. C. Baskerville and J. H. Toogood
Technometrics
Vol. 24, No. 1 (Feb., 1982), pp. 9-17
DOI: 10.2307/1267573
Stable URL: http://www.jstor.org/stable/1267573
Page Count: 9
  • Download ($14.00)
  • Cite this Item
Guided Regression Modeling for Prediction and Exploration of Structure with Many Explanatory Variables
Preview not available

Abstract

A modeling procedure for multiple linear regression is proposed. This procedure begins with preliminary interior and global analyses. The global analysis is based on a form of canonical analysis of the sample correlation matrix of all variables, and, depending on the regression objective, the procedure uses information from that analysis as a guide in the selection of methods to achieve the objective. The two objectives discussed are prediction and exploration of structure. The dependence of the choice of methods on the regression objective is illustrated on a "benchmark" data set, and the results obtained by our approach are compared with published results obtained by other methods. The procedure suggested is particularly useful for data sets with large numbers of explanatory variables that render more conventional methods more expensive, less flexible, or less informative concerning relationships among variables.

Page Thumbnails

  • Thumbnail: Page 
9
    9
  • Thumbnail: Page 
10
    10
  • Thumbnail: Page 
11
    11
  • Thumbnail: Page 
12
    12
  • Thumbnail: Page 
13
    13
  • Thumbnail: Page 
14
    14
  • Thumbnail: Page 
15
    15
  • Thumbnail: Page 
16
    16
  • Thumbnail: Page 
17
    17