Selecting Important Independent Variables in Linear Regression Models. Revision.

Abstract

A large body of literature exists on the techniques for selecting the important variables in linear regression analysis. Many of these techniques are ad hoc in nature and have not been studied from a theoretical viewpoint. This paper discusses some of the more commonly used techniques and propose a selection procedure based on the statistical selection and ranking approach. This procedure is easy to compute and apply. The procedure depends on the goodness of fit of the model and the total error associated with it. Keywords: Selection procedures; Noncentrality parameters; Noncentral F; Total square error; Reduced model; Inferior models; Selection criteria. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1986
Accession Number
ADA176078

Entities

People

  • Deng Yuang Huang
  • Shanti Gupta

Organizations

  • Purdue University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Data Science
  • Distribution Functions
  • Equations
  • Experimental Design
  • Information Science
  • Lepidoptera
  • Linear Regression Analysis
  • Military Research
  • Normal Distribution
  • Probability
  • Regression Analysis
  • Residuals
  • Statistical Analysis
  • Statistical Samples
  • Statistics
  • United States
  • United States Government

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.