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Journal of Applied Sciences
  Year: 2010 | Volume: 10 | Issue: 6 | Page No.: 479-486
DOI: 10.3923/jas.2010.479.486
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Importance of Assessing the Model Adequacy of Binary Logistic Regression
S.K. Sarkar and Habshah Midi

Logistic regression is a sophisticated statistical tool for data analysis in both control experimentation and observational studies. The goal of logistic regression is to correctly predict the category of outcome for individual cases using the most parsimonious model. To accomplish this goal, a model is created that includes all predictor variables that are useful in predicting the response variable. The logistic regression model is being used with increasing rate in various fields in data analysis. In spite of such increase, there has been no commensurate increase in the use of commonly available methods for assessing the model adequacy. Failure to address model adequacy may lead to misleading or incorrect inferences. Therefore, the goal of this study is to present an overview of a few easily employed methods for assessing the fit of logistic regression models. The summary measures of goodness-of-fit as Likelihood Ratio Test, Hosmer-Lemeshow goodness-of-fit test, Osius-Rojek large sample approximation test, Stukel test and area under Receiver Operating Characteristic curve indicate that the logistic regression model fits the data quite well. However, recommendations are made for the use of methods for assessing the model adequacy in different aspects before proceed to present the results from a fitted logistic regression model.
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  •    Effect of Transformation on the Parameter Estimates of a Simple Linear Regression Model: A Case Study of Division of Variables by Constants
  •    Mixed Logit Model on Multivariate Binary Response using Maximum Likelihood Estimator and Generalized Estimating Equations
  •    Detection of Outliers and Influential Observations in Binary Logistic Regression: An Empirical Study
How to cite this article:

S.K. Sarkar and Habshah Midi, 2010. Importance of Assessing the Model Adequacy of Binary Logistic Regression. Journal of Applied Sciences, 10: 479-486.

DOI: 10.3923/jas.2010.479.486








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