### 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) |
---|---|

Pages (from-to) | 515-522 |

Number of pages | 8 |

Journal | Journal of Optimization Theory and Applications |

Volume | 127 |

Issue number | 3 |

DOIs | |

State | Published - Dec 1 2005 |

### Fingerprint

### Keywords

- Multicollinearity
- Optimal control
- Regression coefficients
- Statistical tests

### ASJC Scopus subject areas

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

### Cite this

*Journal of Optimization Theory and Applications*,

*127*(3), 515-522. https://doi.org/10.1007/s10957-005-7499-4

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

Research output: Contribution to journal › Article

*Journal of Optimization Theory and Applications*, vol. 127, no. 3, pp. 515-522. https://doi.org/10.1007/s10957-005-7499-4

}

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

AN - SCOPUS:29144532109

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 -