Please use this identifier to cite or link to this item: http://repository.futminna.edu.ng:8080/jspui/handle/123456789/6812
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dc.contributor.authorBello, Adeshina Oyedele-
dc.contributor.authorBamiduro, T.A.-
dc.contributor.authorChuwkwu, U. A.-
dc.contributor.authorOsowole, O. I-
dc.date.accessioned2021-07-06T15:14:08Z-
dc.date.available2021-07-06T15:14:08Z-
dc.date.issued2015-02-
dc.identifier.citation5en_US
dc.identifier.issnISSN:2348-6848-
dc.identifier.urihttp://repository.futminna.edu.ng:8080/jspui/handle/123456789/6812-
dc.description.abstractThis paper reports on the application of the bootstrap nonlinear regression method to a design of an experiment dataset with fewer experimental runs. Design with desired properties was augmented and verified using graphical techniques. The augmented design with the desired properties benefited the accuracy of the approximated function used. The computation power of R-language and SAS for computing nonlinear function and bootstrap was also compareden_US
dc.description.sponsorshipSelf-Sponsoren_US
dc.language.isoenen_US
dc.publisherInternational Journal of Research (IJR)en_US
dc.relation.ispartofseries2(2);1425-1441-
dc.subjectBootstrap non-linear regression, Gauss Newton Bootstrap Re-Sampling Method; R programming language and SASen_US
dc.subjectSample size; Data visualization (EDA)en_US
dc.titleBootstrap Non-linear Regression Application In A Design Of An Experiment Data For Fewer Sample Sizeen_US
dc.typeArticleen_US
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