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dc.contributor.authorGray, Andrewen_NZ
dc.contributor.authorMacDonell, Stephenen_NZ
dc.date.available2011-04-07T03:05:29Z
dc.date.copyright1996-03en_NZ
dc.identifier.citationGray, A., & MacDonell, S. (1996). A comparison of alternatives to regression analysis as model building techniques to develop predictive equations for software metrics (Information Science Discussion Papers Series No. 96/05). University of Otago. Retrieved from http://hdl.handle.net/10523/902en
dc.identifier.urihttp://hdl.handle.net/10523/902
dc.description.abstractThe almost exclusive use of regression analysis to derive predictive equations for software development metrics found in papers published before 1990 has recently been complemented by increasing numbers of studies using non-traditional methods, such as neural networks, fuzzy logic models, case-based reasoning systems, rule-based systems, and regression trees. There has also been an increasing level of sophistication in the regression-based techniques used, including robust regression methods, factor analysis, resampling methods, and more effective and efficient validation procedures. This paper examines the implications of using these alternative methods and provides some recommendations as to when they may be appropriate. A comparison between standard linear regression, robust regression, and the alternative techniques is also made in terms of their modelling capabilities with specific reference to software metrics.en_NZ
dc.format.mimetypeapplication/pdf
dc.publisherUniversity of Otagoen_NZ
dc.relation.ispartofseriesInformation Science Discussion Papers Seriesen_NZ
dc.subject.lcshQA76 Computer softwareen_NZ
dc.titleA comparison of alternatives to regression analysis as model building techniques to develop predictive equations for software metricsen_NZ
dc.typeDiscussion Paperen_NZ
dc.description.versionUnpublisheden_NZ
otago.bitstream.pages29en_NZ
otago.date.accession2011-02-01 19:53:31en_NZ
otago.schoolInformation Scienceen_NZ
otago.openaccessOpen
otago.place.publicationDunedin, New Zealanden_NZ
dc.identifier.eprints1083en_NZ
otago.school.eprintsSoftware Metrics Research Laboratoryen_NZ
otago.school.eprintsInformation Scienceen_NZ
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