Linear Regression has the below set of Assumptions,
- The Y-Values (or the errors, "e") are independent!
- The Y-Values can be expressed as a linear function of the X variable.
- Variation of observations around the regression line (the residual SE) is constant (homoscedasticity).
- For given value of X, Y values (or the error) are Normally distributed.
Is there any empirical test instead of visual test in R that can be used to validate the Assumptions in 1, 2 and 4?
I can only find for Assumption 3, Empirical Test: ncvTest() from CAR package Value to be observed: p-value Pass criteria: > 0.05
This is to make an automated script that can assist to choose the best model for a set non linear data using linear regression.
Do assist to point to any books or website if this has been discussed previously as my search has been futile. I find many approaches are visual based then empirical.