Logistic Regression Analysis

Regression analysis is one of the most useful and the most frequently used statistical methods [6].regression analysis is a form of predictive modeling technique which investigates the relationship between a dependent and one or more predictor variables. Among the different regression models, logistic regression plays a particular role. However , the basic concept of the linear regression model is quantifying the effect of several explanatory variables on one dependent continuous variable. For situations where the dependent variable is qualitative, however, other methods have been developed. One of the method is logistic regression model, which specifically covers the case of binary or dichotomous response[8]. The statistical analysis of dichotomous outcome variable is frequently interpreted with the use of logistic regression methods. The multiple logistic regression

Cramer (2003) discussed an overview of the development of the logistic regression model. He identifies three sources that had a profound impact on the model: applied mathematics, experimental statistics, and economic theory. Agresti (2002) also provided details of the development on logistic regression in different areas. He states, “Sir David R. Cox introduced many statisticians to logistic regression through his 1958 article and 1970 book, The Analysis of Binary Data.” However, logistic regression is widely used as a popular model for the analysis of binary data with the areas of applications including physical, biomedical, and behavioral sciences. For example, Cornfield (1962) presented the preliminary results from the Framingham Study. The purpose of the study was to find the roles of risk factors of cholesterol levels (low versus high values) and blood pressure (low versus high values) in the development of coronary heart disease (yes or no) in the population of the