Explain the logistic regression model, the independent variables and the dependent variable, assumptions of the model, as well as the objectives.

Logistic regression is an analytical technique used to predict the outcome of a given event. It is based on the assumption that there exists a relationship between one or more independent variables (predictors) and a dependent variable (outcome). The model also assumes that data are binomially distributed with two possible outcomes – such as success/failure, yes/no, etc. – and uses maximum likelihood estimates to determine the probability of each outcome occurring.

The purpose of logistic regression is to explore any potential relationships between independent variables and the dependent variable while controlling for other factors that might influence results. This allows researchers to identify which variables are statistically significant in predicting an outcome so they can better understand how various factors interact with each other. Objectively, this type of analysis can be used to make predictions about future events or anticipate outcomes in order to adjust plans accordingly in order to ensure desired results are achieved.