Question

What are the assumptions required for linear regression? What if some of these assumptions are violated?

Answer 1

The assumptions are as follows:

1. The sample data used to fit the model is representative of the population
2. The relationship between X and the mean of Y is linear
3. The variance of the residual is the same for any value of X (homoscedasticity)
4. Observations are independent of each other
5. For any value of X, Y is normally distributed.
6. Extreme violations of these assumptions will make the results redundant. Small violations of these assumptions will result in a greater bias or variance of the estimate.