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Journal of Software Engineering
  Year: 2016 | Volume: 10 | Issue: 4 | Page No.: 424-430
DOI: 10.3923/jse.2016.424.430
Application of Partial Least-squares Regression to Material Consumption Prediction
Si Li, Shenyang Liu, Xinzhong Li, Zhen Li and Yuan Wang

Abstract:
Background: Nearly all the segments about material include acquisition, storage, supplying and management have close connections with the maintenance material consumption information. The material consumption rule has a great significance on all the segments about material include acquisition, storage, supplying, management and improve the scientificalness of material support. Materials and Methods: Through making an analysis of the character of material consumption and some factors that affects material consumption, this study applies partial least-squares regression to solve the problem of material consumption prediction when the sample is small. Results: The example indicates that partial least-squares regression is much more accurate than multiple linear regressions. Conclusion: The models provide a theoretical basis for calculating reserves of material scientifically and have the vital important guiding significance.
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How to cite this article:

Si Li, Shenyang Liu, Xinzhong Li, Zhen Li and Yuan Wang, 2016. Application of Partial Least-squares Regression to Material Consumption Prediction. Journal of Software Engineering, 10: 424-430.

DOI: 10.3923/jse.2016.424.430

URL: https://scialert.net/abstract/?doi=jse.2016.424.430

 
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