Linear and logistic regression

- In linear regression, the outcome (dependent variable) is continuous. It can have any one of an infinite number of possible values.
- In logistic regression, the outcome (dependent variable) has only a limited number of possible values.
- Logistic regression is used when the response variable is categorical in nature. For instance, yes/no, true/false, red/green/blue, 1st/2nd/3rd/4th, etc.
- Linear regression is used when your response variable is continuous. For instance, weight, height, number of hours, etc.
- Linear regression gives an equation which is of the form Y = mX + C, means equation with degree 1. However, logistic regression gives an equation which is of the form Y = eX + e-X
- In linear regression, the coefficient interpretation of independent variables are quite

straightforward (i.e. holding all other variables constant, with a unit increase in this variable, the - The dependent variable is expected to increase/decrease by xxx).
- In logistic regression, depends on the family (binomial, Poisson, etc.) and link (log, logit, inverse-log, etc.) you use, the interpretation is different.

- In case of Linear Regression the outcome is continuous while in case of Logistic Regression outcome is discrete (not continuous)
- To perform Linear regression we require a linear relationship between the dependent and independent variables. But to perform Logistic regression we do not require a linear relationship between the dependent and independent variables.
- Linear Regression is all about fitting a straight line in the data while Logistic Regression is about fitting a curve to the data.
- Linear Regression is a regression algorithm for Machine Learning while Logistic Regression is a classification Algorithm for machine learning.
- Linear regression assumes gaussian (or normal) distribution of dependent variable. Logistic regression assumes binomial distribution of dependent variable.