Statistical Measures for Least Squares Using the αQβR Algorithm

R. E. Kalaba, J. Johnson and H. H. Natsuyama
Additional contact information
R. E. Kalaba: University of Southern California
J. Johnson: University of Miami
H. H. Natsuyama: California State University

Journal of Optimization Theory and Applications, 2005, vol. 127, issue 3, No 6, 515-522

Abstract: Abstract This paper shows how the output derived from the α Qβ R algorithm can be used to calculate various statistical quantities needed to evaluate linear models. In particular, we show how to calculate standard statistical quantities like the coefficient of determination R2, the F-statistics, and the t-statistics. These quantities serve as a measure of how well the model fits the data.

Keywords: Optimal control; multicollinearity; regression coefficients; statistical tests (search for similar items in EconPapers)
Date: 2005
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10957-005-7499-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joptap:v:127:y:2005:i:3:d:10.1007_s10957-005-7499-4

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10957/PS2

DOI: 10.1007/s10957-005-7499-4

Access Statistics for this article

Journal of Optimization Theory and Applications is currently edited by Franco Giannessi and David G. Hull

More articles in Journal of Optimization Theory and Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().