TY  - JOUR
T1  - Alternative Goodness-of-Fit Test in Logistic Regression Models
AU - Udomboso, C.A. AU - Chukwu, A.U. AU - Enang, E.I. AU - Nja, M.E. 
JO  - Journal of Modern Mathematics and Statistics
VL  - 5
IS  - 2
SP  - 43
EP  - 46
PY  - 2011
DA  - 2001/08/19
SN  - 1994-5388
DO  - jmmstat.2011.43.46
UR  - https://makhillpublications.co/view-article.php?doi=jmmstat.2011.43.46
KW  - predicted probabilities
KW  -observed proportions
KW  -standard error
KW  -Pearson chi-square
KW  -Deviance
KW  -p value
KW  -Nigeria
AB  - 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.
ER  -