Four key assumptions to be able to draw valid conclusions from a simple linear regression model:
- Linearity: The relationship between the dependent variable, Y, and the independent variable, X, is linear.
- Homoskedasticity: The variance of the regression residuals is the same for all observations.
- Independence: The observations, pairs of Ys and Xs, are independent of one another. This implies the regression residuals are uncorrelated across observations.
- Normality: The regression residuals are normally distributed.
