# 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 language English (US) 515-522 8 Journal of Optimization Theory and Applications 127 3 https://doi.org/10.1007/s10957-005-7499-4 Published - 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

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