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General Information
    • ISSN: 2301-3559
    • Frequency: Quarterly
    • DOI: 10.18178/LNSE
    • Editor-in-Chief: Prof. Jemal Antidze
    • Executive Editor: Ms. Nina Lee
    • Abstracting/ Indexing: EI (INSPEC, IET), DOAJ, Electronic Journals Library, Engineering & Technology Digital Library, Ulrich's Periodicals Directory, International Computer Science Digital Library (ICSDL), ProQuest and Google Scholar.
    • E-mail: lnse@ejournal.net
Prof. Jemal Antidze
I. Vekua Scientific Institute of Applied Mathematics
Tbilisi State University, Georgia
I'm happy to take on the position of editor in chief of LNSE. We encourage authors to submit papers concerning any branch of Software Engineering.

LNSE 2014 Vol.2(1): 42-47 ISSN: 2301-3559
DOI: 10.7763/LNSE.2014.V2.92

Statistical Software and Regression Diagnostic Reporting with Fuzzy-AHP Intelligent Zax (FAIZ)

Shahid Anjum
Abstract—Most statistical Software do efficient regression reporting. This reporting is based on multiple criteria and uses multiple fragmented diagnostic tables. In this study Fuzzy AHP Intelligent Zax (FAIZ) has been proposed which can be incorporated in statistical software as a scoring technique for ranking the simple linear regressions (SLR) based on fuzzy Analytical Hierarchy Process (AHP) logic to push ‘Further Evolution’ in Software Engineering Evolution process for Statistical Software. This approach will help to prevent regressioneering (ignorant sub-optimal uses, or even misuses, of regression analysis) and assist researchers in choosing best regression based on linearized score with FAIZ approach.

Index Terms—Statistical software engineering, artificial intelligence, linear regression analysis, MCDM.

S. Anjum is with the College of Management, Lawrence Technological University (LTU), Southfield, MI 48075 USA (e-mail: anjumsw@ hotmail.com).


Cite: Shahid Anjum, "Statistical Software and Regression Diagnostic Reporting with Fuzzy-AHP Intelligent Zax (FAIZ)," Lecture Notes on Software Engineering vol. 2, no. 1, pp. 42-47, 2014.

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