In summary, linear regression seeks the correlation between two numerical variables and logistic regression can have numerical or categorical variables. Logistic regression is used when the answer to your problem can be categorized. Ex: which employees are more likely to leave the company? In this case this question is a binary answer...classifying, either the employee leaves or stays and the model will separate these two blocks and you can evaluate through the confusion matrix. Hope this helps. Helberth