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dc.contributor.authorHossain, Ahmed
dc.contributor.authorKhan, H.T. Abdullah
dc.date.accessioned2010-10-14T10:20:23Z
dc.date.available2010-10-14T10:20:23Z
dc.date.issued2004
dc.identifier.urihttp://hdl.handle.net/10361/520
dc.description.abstractThe use of explanatory variables or covariates in a regression model is an important way to represent heterogeneity in a population. Again bootstrapping is rapidly becoming a popular tool to apply in a broad range of standard applications including multiple regression. The nonparametric bootstrap allows us to estimate the sampling distribution of a statistic empirically without making assumptions about the form of the population, and without deriving the sampling distribution explicitly. The main objective of this study to discuss the nonparametric bootstrapping procedure for multiple logistic regression model associated with Davidson and Hinkley's (1997) “boot” library in R.en_US
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.relation.ispartofseriesBRAC University Journal, BRAC University;
dc.subjectNonparametricen_US
dc.subjectBootstrappingen_US
dc.subjectSamplingen_US
dc.subjectLogistic regressionen_US
dc.subjectCovariatesen_US
dc.titleNonparametric bootstrapping for multiple logistic regression model using Ren_US


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