Logit Regression Procedure

Logit Regression: What Is Logit Regression, and How Does The Procedure Work

For accuracy and power, regression analysis depends upon a linear and additive relationship between independent and dependent variables (Mallios, 1989, pp. 108-110). Problems arise with respect to regression analysis, however, when a dependent variable is dichotomous (Dwyer, 1983, p. 447). A dichotomous variable is one wherein the classifications are mutually exclusive, such as the female and males classifications of gender as a variable.

The first problem that arises in relation to regression analysis with respect to dichotomous dependent variables is that the relationship between the independent and dependent variables is non-linear (Kim and Kohout, 1975, pp. 368-369). With a dichotomous variable such as gender, there is no way to transform the values to attain a linear character. In such instances, a special regression equation employing a dummy variable may be used or non-parametric statistical procedures may be used (Pfaffenberger and Patterson, 1991, pp. 779-780; S

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omparable results with much less effort expended. In logit analysis, the conditional probabilities are transformed into variates for points on the logistic curve where such probabilities occur. The logit transformation procedure converts conditional probabilities to natural logarithmic values. Through the application of logit transformation, the linearity of the relationship between the independent and the dependent variables is restored. Logit transformation, thus, is a procedure through which a linear relationship can be derived between a linear independent variable and dichotomous dependent variables for which an assumption may be made that a linear relationship should exist between the dichotomous and the linear variable. The logit regression procedure based on the logit transformation of the conditional probabilities of dichotomous outcomes at progressive values of an independent variable provides a predictive outcome with less expenditure of effort that is nevertheless comparable to that attainable though probit transformation. Appropriate Applications for Logit Regression The application of the logit regression procedure is most appropriate for multivariate analysis (Dwyer, 1983, p. 453). Appropri
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