Question

Difference between Linear and Logistic Regression?

Answer 1

Two main difference are as follows -

1.  Linear regression requires the dependent variable to be continuous i.e. numeric values (no categories or groups). While Binary logistic regression requires the dependent variable to be binary - two categories only (0/1). Multinomial or ordinary logistic regression can have dependent variable with more than two categories.
2. Linear regression is based on least square estimation which says regression coefficients should be chosen in such a way that it minimizes the sum of the squared distances of each observed response to its fitted value. While logistic regression is based on Maximum Likelihood Estimation which says coefficients should be chosen in such a way that it maximizes the Probability of Y given X (likelihood)