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Alternative methods to parametric significance testing in linear regression and ANOVA

Dissertation (MSc)--University of Pretoria, 2015.

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Other Authors: Steffens, Francois E.
Format: Thesis
Language:English
Published: University of Pretoria 2016
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access_status_str Open Access
author2 Steffens, Francois E.
author_browse Steffens, Francois E.
author_facet Steffens, Francois E.
collection Thesis
dc_rights_str_mv © 2016, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.
description Dissertation (MSc)--University of Pretoria, 2015.
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institution University of Pretoria (South Africa)
language English
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provenance_str_mv Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository
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publisher University of Pretoria
publisherStr University of Pretoria
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spelling oai:repository.up.ac.za:2263/53516 Alternative methods to parametric significance testing in linear regression and ANOVA Steffens, Francois E. Nhlanhla2007@gmail.com Fletcher, Lizelle Makhanya, Nhlanhla Well-Beloved UCTD Dissertation (MSc)--University of Pretoria, 2015. The aim of the study was to survey permutation tests, bootstrapping and jackknife methods and their application to significance testing of regression coefficients in linear regression analysis. A Monte Carlo simulation study was performed in order to compare the different methods in terms of empirical probability of type 1 error, power of a test and confidence interval where coverage and average length of confidence interval were used as measures of comparison. The empirical probability of type 1 error and power of a test were used to compare permutation tests, bootstrapping and parametric methods, while the confidence intervals were used to compare jackknife, bootstrap as well as the parametric method. These comparisons were performed in order to investigate the effect of (1) sample size (2) when errors are normally, uniformly and lognormally distributed (3) when the number of explanatory variables is 1, 2 and 5. (4) When the correlation coefficient between the explanatory variables is 0, 0.5 and 0.9. The results obtained from the Monte Carlo simulation study showed that permutation and bootstrap methods produced similar probability of type 1 error results while the parametric methods understated probability of type 1 error when errors are lognormally distributed. In the absence of multicollinearity all the methods were almost equally powerful and in presence of multicollinearity they all suffered equally in terms of power. The jackknife produced poor result in terms of 100(1???)% confidence interval while the bootstrap produced reasonable results especially for larger sample sizes. The improvement was observed under the jackknife method when percentile based intervals were applied. It was concluded that permutation tests as well as bootstrap methods are good alternative methods to use in significance testing in regression and ANOVA. Statistics MSc Unrestricted 2016-07-01T10:33:14Z 2016-07-01T10:33:14Z 2016-04-13 2015 Dissertation Makhanya, NW 2016, Alternative methods to parametric significance testing in linear regression and ANOVA, MSc Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/53516> A2016 http://hdl.handle.net/2263/53516 en © 2016, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria
spellingShingle UCTD
Alternative methods to parametric significance testing in linear regression and ANOVA
title Alternative methods to parametric significance testing in linear regression and ANOVA
title_full Alternative methods to parametric significance testing in linear regression and ANOVA
title_fullStr Alternative methods to parametric significance testing in linear regression and ANOVA
title_full_unstemmed Alternative methods to parametric significance testing in linear regression and ANOVA
title_short Alternative methods to parametric significance testing in linear regression and ANOVA
title_sort alternative methods to parametric significance testing in linear regression and anova
topic UCTD
url http://hdl.handle.net/2263/53516