Linear regression has the following assumptions, failing which the linear regression model does not hold true:
- The dependent variable should be a linear combination of independent variables
- No autocorrelation in error terms
- Errors should have zero mean and be normally distributed
- No or little multi-collinearity
- Error terms should be homoscedastic
These are explained in detail as follows:
- The dependent variable should be a linear combination of independent variables: Y should be a linear combination of X variables. Please note, in the following equation, X2 has raised to the power of 2, the equation is still holding the assumption of a linear combination of variables:
How to diagnose: Look into residual ...
