Volume 8 Number 11 (Nov. 2013)
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JSW 2013 Vol.8(11): 2706-2710 ISSN: 1796-217X
doi: 10.4304/jsw.8.11.2706-2710

A Modeling Toolbox and Its Applications to Statistical Process Modeling

Young-Don Ko, Helen Shang
School of Engineering, Laurentian University, Sudbury, Ontario, Canada P3E 2C6

Abstract—Analyzing data that are measured or collected in processes requires statistical software. Most methods for analysis use a regression approach or its expanded forms such as polynomial and response surface models. However, the available commercial programs are inconvenient and costly for personal use, and require demanding pre-training in using them. A simple and user-friendly interface program adaptable to personal needs is therefore demanded. In this paper, a Matlab Toolbox, named the Regression Modeler (RM), is developed. The structure of the program and procedure of implementation are described. The program is verified using various datasets in process engineering. The toolbox provides users with solutions to regression models, polynomial models and response surface models with fine 2D and 3D plots.

Index Terms—Matlab toolbox, regression model, multiple regression model, polynomial model, response surface model.

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Cite: Young-Don Ko, Helen Shang, "A Modeling Toolbox and Its Applications to Statistical Process Modeling," Journal of Software vol. 8, no. 11, pp. 2706-2710, 2013.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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