Identifying outliers and influential observations in general linear regression models

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dc.contributor.advisorTroskie, Casper Gen_ZA
dc.contributor.authorKatshunga, Dominiqueen_ZA
dc.date.accessioned2014-08-30T05:58:32Z
dc.date.available2014-08-30T05:58:32Z
dc.date.issued2004en_ZA
dc.descriptionIncludes bibliographical references (leaves 140-149).en_ZA
dc.description.abstractIdentifying outliers and/or influential observations is a fundamental step in any statistical analysis, since their presence is likely to lead to erroneous results. Numerous measures have been proposed for detecting outliers and assessing the influence of observations on least squares regression results. Since outliers can arise in different ways, the above mentioned measures are based on motivational arguments and they are designed to measure the influence of observations on different aspects of various regression results. In what follows, we investigate how one can combine different test statistics based on residuals and diagnostic plots to identify outliers and influential observations (both in the single and multiple case) in general linear regression models.en_ZA
dc.identifier.apacitationKatshunga, D. (2004). <i>Identifying outliers and influential observations in general linear regression models</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/6772en_ZA
dc.identifier.chicagocitationKatshunga, Dominique. <i>"Identifying outliers and influential observations in general linear regression models."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2004. http://hdl.handle.net/11427/6772en_ZA
dc.identifier.citationKatshunga, D. 2004. Identifying outliers and influential observations in general linear regression models. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Katshunga, Dominique AB - Identifying outliers and/or influential observations is a fundamental step in any statistical analysis, since their presence is likely to lead to erroneous results. Numerous measures have been proposed for detecting outliers and assessing the influence of observations on least squares regression results. Since outliers can arise in different ways, the above mentioned measures are based on motivational arguments and they are designed to measure the influence of observations on different aspects of various regression results. In what follows, we investigate how one can combine different test statistics based on residuals and diagnostic plots to identify outliers and influential observations (both in the single and multiple case) in general linear regression models. DA - 2004 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2004 T1 - Identifying outliers and influential observations in general linear regression models TI - Identifying outliers and influential observations in general linear regression models UR - http://hdl.handle.net/11427/6772 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/6772
dc.identifier.vancouvercitationKatshunga D. Identifying outliers and influential observations in general linear regression models. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2004 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/6772en_ZA
dc.language.isoeng
dc.publisher.departmentDepartment of Statistical Sciencesen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherMathematical Statisticsen_ZA
dc.titleIdentifying outliers and influential observations in general linear regression modelsen_ZA
dc.typeThesis
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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