in general regression model the dependent variable is continuous and independent variable is discrete type.

in genral regression model the variables are linearly related.

in logistic regression model the response varaible must be categorical type.

the relation ship between the response and explonatory variables is non-linear.

Binary logistic regression is a statistical method used to model the relationship between a categorical dependent variable with two levels and one or more independent variables. It estimates the probability that an observation belongs to one of the two categories based on the values of the independent variables. The output is in the form of odds ratios, which describe the influence of the independent variables on the probability of the outcome.

Using real-world data from a data set, a statistical analysis method known as logistic regression predicts a binary outcome, such as yes or no. A logistic regression model forecasts a dependent data variable by examining the correlation between one or more existing independent variables.

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To evaluate a logistic regression model, you can start by analyzing coefficient values to determine the significance and direction of each predictor variable. Next, you can examine the goodness-of-fit measures like deviance or chi-square tests to assess how well the model fits the data. Finally, you can apply validation techniques like cross-validation or holdout sample testing to evaluate the model's performance on new data.

In cases wherethe dependent variable can take any numerical value for a given set of independent variables multiple regression is used.But in cases when the dependent variable is qualitative(dichotomous,polytomous)then logistic regression is used.In Multiple regression the dependent variable is assumed to follow normal distribution but in case of logistic regression the dependent variablefollows bernoulli distribution(if dichotomous) which means it will be only0 or 1.