Statistical measures for least squares using the αQβR algorithm

R. E. Kalaba, Joseph Johnson, H. H. Natsuyama

Research output: Contribution to journalArticle

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 R 2, the F-statistics, and the t-statistics. These quantities serve as a measure of how well the model fits the data.

Original languageEnglish (US)
Pages (from-to)515-522
Number of pages8
JournalJournal of Optimization Theory and Applications
Volume127
Issue number3
DOIs
StatePublished - Dec 1 2005

Fingerprint

QR Algorithm
Least Squares
Statistics
F-statistics
Coefficient of Determination
Calculate
Linear Model
Evaluate
Output
Least squares
Model

Keywords

  • Multicollinearity
  • Optimal control
  • Regression coefficients
  • Statistical tests

ASJC Scopus subject areas

  • Control and Optimization
  • Management Science and Operations Research
  • Applied Mathematics

Cite this

Statistical measures for least squares using the αQβR algorithm. / Kalaba, R. E.; Johnson, Joseph; Natsuyama, H. H.

In: Journal of Optimization Theory and Applications, Vol. 127, No. 3, 01.12.2005, p. 515-522.

Research output: Contribution to journalArticle

@article{b6a63a52c28d4db79da11e04428bf638,
title = "Statistical measures for least squares using the αQβR algorithm",
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 R 2, the F-statistics, and the t-statistics. These quantities serve as a measure of how well the model fits the data.",
keywords = "Multicollinearity, Optimal control, Regression coefficients, Statistical tests",
author = "Kalaba, {R. E.} and Joseph Johnson and Natsuyama, {H. H.}",
year = "2005",
month = "12",
day = "1",
doi = "10.1007/s10957-005-7499-4",
language = "English (US)",
volume = "127",
pages = "515--522",
journal = "Journal of Optimization Theory and Applications",
issn = "0022-3239",
publisher = "Springer New York",
number = "3",

}

TY - JOUR

T1 - Statistical measures for least squares using the αQβR algorithm

AU - Kalaba, R. E.

AU - Johnson, Joseph

AU - Natsuyama, H. H.

PY - 2005/12/1

Y1 - 2005/12/1

N2 - 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 R 2, the F-statistics, and the t-statistics. These quantities serve as a measure of how well the model fits the data.

AB - 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 R 2, the F-statistics, and the t-statistics. These quantities serve as a measure of how well the model fits the data.

KW - Multicollinearity

KW - Optimal control

KW - Regression coefficients

KW - Statistical tests

UR - http://www.scopus.com/inward/record.url?scp=29144532109&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=29144532109&partnerID=8YFLogxK

U2 - 10.1007/s10957-005-7499-4

DO - 10.1007/s10957-005-7499-4

M3 - Article

VL - 127

SP - 515

EP - 522

JO - Journal of Optimization Theory and Applications

JF - Journal of Optimization Theory and Applications

SN - 0022-3239

IS - 3

ER -