Comparison of Orthogonal Regression and Least Squares in Measurement Error Modeling for Prediction of Material Property

Guo Liang Ding; Biao Chu; Yi Jin; Chang An Zhu

doi:10.4028/www.scientific.net/AMR.661.166

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 661Comparison of Orthogonal Regression and Least...

Comparison of Orthogonal Regression and Least Squares in Measurement Error Modeling for Prediction of Material Property

Abstract:

A critical challenge in prediction of material property is the accuracy of estimation for regression coefficient between the structure or process of material and its macroscopic property. One source of the estimation errors is measurement errors which commonly exist in practice. To provide guidance on the use of simple linear regression methods in measurement error modeling for prediction of material property, we investigated and compared least squares (LS) and orthogonal regression (OR) theoretically. And their applications in prediction of tensile strength for quenched and tempered steel 45 were presented as an example. OR has better performance than LS in the prediction of material property in presence of measurement errors under certain conditions.

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Periodical:

Advanced Materials Research (Volume 661)

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166-170

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https://doi.org/10.4028/www.scientific.net/AMR.661.166

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Online since:

February 2013

Authors:

Guo Liang Ding, Biao Chu, Yi Jin, Chang An Zhu

Keywords:

Least Square (LS), Material Property Predicting, Measurement Error Model, Orthogonal Regression, Regression Analysis

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