Assumptions of linear regression?

There are four assumptions associated with a linear regression model:

  1. Linearity : The relationship between X and the mean of Y is linear.
  2. Homoscedasticity : The variance of residual is the same for any value of X.
  3. Independence : Observations are independent of each other.
  4. Normality : For any fixed value of X, Y is normally distributed.

Linear Regression is a machine learning algorithm based on a supervised ml task to compute the regression coefficients. Regression models a target prediction based on independent variables.

  1. Linearity
  2. No Endogeneity
  3. Normality
  4. Homoscedasticity
  5. No autocorrelation
  6. No multicollinearity