ANALYSIS OF QUANTILE REGRESSION AS ALTERNATIVE TO ORDINARY LEAST SQUARES REGRESSION

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dc.contributor.authorABDULLAHI, IBRAHIM
dc.date.accessioned2015-11-26T11:31:51Z
dc.date.available2015-11-26T11:31:51Z
dc.date.issued2015-06
dc.descriptionA THESIS SUBMITTED TO THE SCHOOL OF POSTGRADUATE STUDIES, AHMADU BELLO UNIVERSITY, ZARIA NIGERIA IN PARTIAL FULFILLMENT FOR THE AWARD OF MASTER OF SCIENCE (M.Sc) DEGREE IN STATISTICS, DEPARTMENT OF MATHEMATICS AHMADU BELLO UNIVERSITY, ZARIA NIGERIAen_US
dc.description.abstractIn this thesis, we present an alternative to ordinary least squares (OLS) regression based on analytical solution in the Statgraphics software is considered, and this alternative is no other than quantile regression (QR) model. We also present goodness of fit statistic called Quantile regression coefficient of determination as well as heteroskedasticity test statistics for the parameters. The procedure is well presented, illustrated and validated by a numerical example based on publicly available dataset on fuel consumption in miles per gallon in highway driving. Theresults obtained from the analysis in this thesissuggest that sometimes OLS estimates can even be misleading what the true relationship between response variable and covariate as the effects can be very different for different subsections of the sample. Quantile Regression therefore gives a better and more complete view of the relationship among random variables.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/7194
dc.language.isoenen_US
dc.subjectANALYSIS,en_US
dc.subjectQUANTILE,en_US
dc.subjectREGRESSION,en_US
dc.subjectALTERNATIVE,en_US
dc.subjectORDINARY,en_US
dc.subjectLEAST,en_US
dc.subjectSQUARES,en_US
dc.subjectREGRESSION,en_US
dc.titleANALYSIS OF QUANTILE REGRESSION AS ALTERNATIVE TO ORDINARY LEAST SQUARES REGRESSIONen_US
dc.typeThesisen_US
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