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A Tutorial to Developing Statistical Models for Predicting Disqualification Probability

A Tutorial to Developing Statistical Models for Predicting Disqualification Probability

Ilmari Juutilainen, Satu Tamminen, Juha Röning
Copyright: © 2012 |Pages: 32
ISBN13: 9781466601284|ISBN10: 1466601280|EISBN13: 9781466601291
DOI: 10.4018/978-1-4666-0128-4.ch015
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MLA

Juutilainen, Ilmari, et al. "A Tutorial to Developing Statistical Models for Predicting Disqualification Probability." Computational Methods for Optimizing Manufacturing Technology: Models and Techniques, edited by J. Paulo Davim, IGI Global, 2012, pp. 368-399. https://doi.org/10.4018/978-1-4666-0128-4.ch015

APA

Juutilainen, I., Tamminen, S., & Röning, J. (2012). A Tutorial to Developing Statistical Models for Predicting Disqualification Probability. In J. Davim (Ed.), Computational Methods for Optimizing Manufacturing Technology: Models and Techniques (pp. 368-399). IGI Global. https://doi.org/10.4018/978-1-4666-0128-4.ch015

Chicago

Juutilainen, Ilmari, Satu Tamminen, and Juha Röning. "A Tutorial to Developing Statistical Models for Predicting Disqualification Probability." In Computational Methods for Optimizing Manufacturing Technology: Models and Techniques, edited by J. Paulo Davim, 368-399. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-0128-4.ch015

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Abstract

Different industries utilize statistical prediction models that predict the product properties in process planning, control, and optimization. An important aim is to decrease the number of disqualifications. The model can prevent disqualifications efficiently if the disqualification probability is predicted accurately. This study gives step-by-step instructions for developing, validating, comparing, and visualizing models that predict the disqualification probability with high accuracy. The work summarizes industrially applicable statistical modeling methods that are most suitable for the development of accurate predictors for the disqualification probability. Currently, the information on such statistical methods, e.g. quantile regression, modeling of distribution shape, and joint modeling of mean and deviation, is scattered in the existing literature. The main contribution of this work is that it pulls together this methodology into a unified framework which allows the comparative analysis of probability predictors that are based on the different approaches. The proposed modeling procedure (ProPred) is demonstrated using three manufacturing industry applications. In the case applications, the predictors generated using the ProPred procedure are 10-30% more efficient in avoiding disqualifications by means of process planning and control operations than the baseline predictors.

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