The term „Binomial Logit Model,” often referred to as logistic regression, is called so because it is used when the dependent variable is binary (binomial), meaning it has only two possible outcomes (0/1, Yes/No, True/False). „Logit” is a function that links the probability of the binary response to a linear combination of explanatory variables.

Here is a breakdown of the term:

**Binomial**: This refers to the binary nature of the dependent variable. In a binomial distribution, there are two possible outcomes for each trial, and the binomial model predicts the probability of one of those outcomes.**Logit**: The logit function is the logarithm of the odds ratio (the ratio of the probability of success to the probability of failure). In logistic regression, the logit function is used to transform probabilities into an unbounded continuum. This is useful because probabilities must lie between 0 and 1, but the logit of the probability can take any value from negative to positive infinity, making it easier to model using linear regression techniques.

The model takes the form:

The logistic regression model is a type of generalized linear model (GLM) that uses a logistic function to model a binary outcome variable. The coefficients of the model are typically estimated using the method of maximum likelihood.