A limitation of ordinary linear models is the requirement that the dependent variable is numerical rather than categorical. But many interesting variables are categorical (people may pass or fail, and so on). A range of techniques has been developed for analyzing data with categorical dependent variables, including discriminant analysis, probit analysis, log-linear regression and logistic regression. The various techniques listed above are applicable in different situations: for example, log-linear regression requires all regressors to be categorical, whilst discriminant analysis strictly requires them all to be continuous. Logistic regression is a type of predictive model that can be used when the target variable is a categorical variable with two categories, and when we have a mixture of numerical and categorical regressors. Logistic regression models can be used for classification. So, it can be used only with a categorical target variable that has exactly two categories, and a continuous target variable that has values in the range (0) to (1) representing probability values.

In this paper, we consider a study whose goal is to model the response to a student’s grades as a function of the student’s gender (as a logistic regression model). The target (dependent) variable, response, has a value (1) if the student’s grade is ≥ pass (pass, good, very good, and excellent) and (0) if the student’s grade is < pass. The value of response predicted by the model represents the probability of achieving an effective outcome.

For this paper, a systematic sample of students selected from students of Faculty of Commerce, Tanta University , 2007. A binary response logistic regression model is considered for identifying the relationship between the students’ grades and the students’ gender as a social phenomenon. The statistical software package SPSS will be used for the binary response logistic regression modeling. Based on the goodness-of-fit tests, Likelihood-ratio Test, and Classification table, the Logistic Regression model performs as good as or better than the other regression models. Our model leads to (For Arabic section) the prediction that the probability of student grade are ≥ pass is 68.6% for female and 54.9% for male, the predicted odds of student grade is ≥ pass for male is 0.534 times for female, adding the system of study as a predictor variable significantly improved the model  and the overall success rate in classification has improved from 74.1% to 78.85%. But for English section, the probability of student grade is ≥ pass is 79.8% for female and 67.8% for male, and the predicted odds of student grade is ≥ pass for male is 0.534 times for female.