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dc.contributor.authorNja, M. E.-
dc.contributor.authorEnang, E. I.-
dc.contributor.authorChukwu, A. U.-
dc.contributor.authorUdomboso, C. G.-
dc.identifier.otherJournal of Modern Mathematics and Statistics 5(2), pp. 43-46-
dc.description.abstractThe Deviance and the Pearson chi-square are two traditional goodness-of-fit tests in generalized linear models for which the logistic model is a special case. The effort involved in the computation of either the Deviance or Pearson chi-square statistic is enormous and this provides a reason for prospecting an alternative goodness-of-fit test in logistic regression models with discrete predictor variables. The Deviance is based on the log likelihood function while the Pearson chi-square derives from the discrepancies between observed and predicted counts. Replacing observed and predicted counts with observed proportions and predicted probabilities, respectively in a cross-classification data arrangement, the standard error of estimate is proposed as an alternative goodness-of-fit test in logistic regression models. The illustrative example returns favourable comparisons with Deviance and the Pearson chi-square statistics.en_US
dc.publisherMedwell Journalsen_US
dc.subjectPearson chi-squareen_US
dc.subjectStandard erroren_US
dc.subjectObserved proportionsen_US
dc.subjectPredicted probabilitiesen_US
dc.subjectp valueen_US
dc.titleAlternative goodness-of-fit test in logistic regression modelsen_US
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