C.A.  Udomboso, A.U.  Chukwu, E.I.  Enang, M.E.  Nja, 
    Alternative Goodness-of-Fit Test in Logistic Regression Models,
    Journal of Modern Mathematics and Statistics,
    Volume 5,Issue 2,
    2011,
    Pages 43-46,
    ISSN 1994-5388,
    jmmstat.2011.43.46,
    (https://makhillpublications.co/view-article.php?doi=jmmstat.2011.43.46)
    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.
    Keywords: predicted probabilities;observed proportions;standard error;Pearson chi-square;Deviance;p value;Nigeria