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Use of Orthogonal Factors for Selection of Variables in a Regression Equation—An Illustration
Author(s) -
Daling Janet R.,
Tamura H.
Publication year - 1970
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.2307/2346330
Subject(s) - statistics , selection (genetic algorithm) , regression analysis , regression , mathematics , structural equation modeling , econometrics , cross sectional regression , computer science , polynomial regression , artificial intelligence
Summary Selection of explanatory variables in the regression equation has been a prime problem in constructing a prediction equation. This paper describes and gives an illustration of a selection technique which makes use of the orthogonality among factors extracted from the correlation matrix. Using the factors not as new variables, but merely as the reference frame, we can identify a near orthogonal subset of explanatory variables. It is indicated that this approach provides the model builder with the flexibility that is not available in the conventional, purely mechanical, selection methods.