A Backward Elimination General Significance Regression Model.

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Abstract:

The report develops a statistical procedure for selecting a proper set of independent variables in a linear regression model. The procedure is a backward elimination procedure in which initially a large model is hypothesized and systematically non-significant variables are eliminated one by one. Two different situations were investigated concerning sample estimates of the error pure error, and using lack-of-fit as an error estimate, with the effects on the testing procedure for each case. Author

Author(s):
Colvin, Charles E. ; Machtolff, Maureen E.
Author Organization(s):
ARMY MISSILE COMMAND REDSTONE ARSENAL AL SYSTEMS ANALYSIS DIV
Descriptive Note:
Technical rept.,
Pagination:
0055

Security Markings

DOCUMENT & CONTEXTUAL SUMMARY

Distribution:
Approved For Public Release

RECORD

Collection: TR
Identifying Numbers
Report Number(s):
DA-TR-70-1
Project Number(s):
DA-11-B-15011
 Subject Terms
Communities of Interest:
No COI(s) Identified
Descriptor(s):
(*REGRESSION ANALYSIS, STATISTICAL TESTS), SET THEORY, RANDOM VARIABLES, MATRICES(MATHEMATICS), MATHEMATICAL MODELS, WEAPON SYSTEMS, GUIDED MISSILES, ROCKET ENGINES
Field(s)/Group(s):
Statistics and Probability
Keyword(s):
COMPUTERIZED SIMULATION, FORTRAN, FORTRAN 4 PROGRAMMING LANGUAGE
Report Date:
1970 Oct 01