When the residuals follow a normal distribution, the most likely function that fits the data is found using least squares. In that case: y=f(x_i)+r_i, r∼N(0, sigma^2) What happens when r∼N(0,sigma(x)^2)?

Makayla Eaton

Makayla Eaton

Open question

2022-08-17

When the residuals follow a normal distribution, the most likely function that fits the data is found using least squares. In that case:
y = f ( x i ) + r i , r N ( 0 , σ 2 )
What happens when r N ( 0 , σ ( x ) 2 ) ?

Answer & Explanation

quillassed7

quillassed7

Beginner2022-08-18Added 13 answers

Then you have heteroscedasticity. The estimated values for the standard errors of the parameters are biased. And additional you can´t use the t-Distribution and the F-Distribution for testing the parameters.

Do you have a similar question?

Recalculate according to your conditions!

New Questions in High school statistics

Ask your question.
Get an expert answer.

Let our experts help you. Answer in as fast as 15 minutes.

Didn't find what you were looking for?