Refined Prediction for Linear Regression Models.

Abstract

Adequate prediction of a response variable using a multiple linear regression model is shown in this article to be related to the presence of multicollinearities among the predictor variables. If strong multicollinearities are present in the data, this information can be used to determine when prediction is likely to be accurate. A region of prediction, R, is proposed as a guide for prediction purposes. This region is related to a prediction interval when the matrix of predictor variables is of full column rank, but it can also be used when the sample is undersized. The Gorman-Toman (1966) ten variable data is used to illustrate the effectiveness of the region R. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1976
Accession Number
ADA031365

Entities

People

  • J. L. Hess
  • R. F. Gunst

Organizations

  • Southern Methodist University

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Computer Programs
  • Data Science
  • Eigenvalues
  • Eigenvectors
  • Equations
  • Estimators
  • Intervals
  • Literature
  • New York
  • Observation
  • Regression Analysis
  • Simultaneous Equations
  • Statistical Algorithms
  • Statistical Inference
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.