What are the key differences between linear and logistic regression?

What are the key differences between linear and logistic regression?

Linear regression is used for predicting continuous outcomes, while logistic regression is used for binary outcomes.

Can you explain why we use logistic regression for binary outcomes?

Logistic regression models the probability of a binary outcome using the logistic function, which outputs values between 0 and 1.

What kind of data do I need for each type of regression?

For linear regression, you need continuous data for the dependent variable. For logistic regression, the dependent variable should be categorical with two classes.

Are there any assumptions that I should be aware of for these models?

Yes, linear regression assumes linearity, normality, and homoscedasticity, while logistic regression assumes independence of observations and a linear relationship between the logit of the outcome and the predictors.