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

The use of logistic regression as an analytical tool to forecast the outcome is a common practice. Based on the assumption there is a relation between a set of independent variables (predictors), and a single 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.

Logistic regression allows researchers to identify which variables are statistically significant in predicting an outcome, while also controlling other factors. Researchers can then identify variables that are statistically significant for predicting an end result, allowing them to better understand the interaction between factors. This type of analysis is used objectively to predict future events and anticipate results in order for plans to be adjusted accordingly to achieve desired outcomes.