@article{MAKHILLJMMS20115228168,
    title = {Alternative Goodness-of-Fit Test in Logistic Regression Models},
    journal = {Journal of Modern Mathematics and Statistics},
    volume = {5},
    number = {2},
    pages = {43-46},
    year = {2011},
    issn = {1994-5388},
    doi = {jmmstat.2011.43.46},
    url = {https://makhillpublications.co/view-article.php?issn=1994-5388&doi=jmmstat.2011.43.46},
    author = {C.A.,A.U.,E.I. and},
    keywords = {predicted probabilities,observed proportions,standard error,Pearson chi-square,Deviance,p value,Nigeria},
    abstract = {The 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.}
    }