### Abstract

Multivariate linear regression models have been commonly used as software efort prediction models. To improve the prediction accuracy, it is a common practice to transform (especially, log-transform) the data before building a model, although its theoretical basis is not necessarily clear. This paper reveals that the log-transformed linear regression model (log-log regression model) is equal to the exponential model, which is suitable to characterize various relationships among software related metrics. However, when using a log-log regression model, the result of inverse transformation tends to under-estimate the efort. This paper also introduces a method to correct such bias.

Original language | English |
---|---|

Pages (from-to) | 234-239 |

Number of pages | 6 |

Journal | Computer Software |

Volume | 27 |

Issue number | 4 |

Publication status | Published - 2010 |

Externally published | Yes |

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### ASJC Scopus subject areas

- Software

### Cite this

*Computer Software*,

*27*(4), 234-239.

**The effect of log transformation in multivariate liner regression models for software effort prediction.** / Monden, Akito; Kobayashi, Kenichi.

Research output: Contribution to journal › Article

*Computer Software*, vol. 27, no. 4, pp. 234-239.

}

TY - JOUR

T1 - The effect of log transformation in multivariate liner regression models for software effort prediction

AU - Monden, Akito

AU - Kobayashi, Kenichi

PY - 2010

Y1 - 2010

N2 - Multivariate linear regression models have been commonly used as software efort prediction models. To improve the prediction accuracy, it is a common practice to transform (especially, log-transform) the data before building a model, although its theoretical basis is not necessarily clear. This paper reveals that the log-transformed linear regression model (log-log regression model) is equal to the exponential model, which is suitable to characterize various relationships among software related metrics. However, when using a log-log regression model, the result of inverse transformation tends to under-estimate the efort. This paper also introduces a method to correct such bias.

AB - Multivariate linear regression models have been commonly used as software efort prediction models. To improve the prediction accuracy, it is a common practice to transform (especially, log-transform) the data before building a model, although its theoretical basis is not necessarily clear. This paper reveals that the log-transformed linear regression model (log-log regression model) is equal to the exponential model, which is suitable to characterize various relationships among software related metrics. However, when using a log-log regression model, the result of inverse transformation tends to under-estimate the efort. This paper also introduces a method to correct such bias.

UR - http://www.scopus.com/inward/record.url?scp=78650819653&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78650819653&partnerID=8YFLogxK

M3 - Article

VL - 27

SP - 234

EP - 239

JO - Computer Software

JF - Computer Software

SN - 0289-6540

IS - 4

ER -