{"id":57374,"date":"2024-04-16T01:00:55","date_gmt":"2024-04-16T01:00:55","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=57374"},"modified":"2024-04-16T01:00:55","modified_gmt":"2024-04-16T01:00:55","slug":"which-of-the-one-is-true-about-heteroskedasticity","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/which-of-the-one-is-true-about-heteroskedasticity\/","title":{"rendered":"Which of the one is true about Heteroskedasticity?"},"content":{"rendered":"

[amp_mcq option1=”linear regression with varying error terms” option2=”linear regression with constant error terms” option3=”linear regression with zero error terms” option4=”none of these” correct=”option1″]<\/p>\n

The correct answer is A. linear regression with varying error terms.<\/p>\n

Heteroscedasticity is a violation of the assumption of homoskedasticity, which states that the variance of the error terms is constant. In other words, heteroscedasticity occurs when the variance of the error terms is not constant, but instead varies across the values of the independent variables.<\/p>\n

This can be a problem in linear regression because it can lead to biased estimates of the regression coefficients. To test for heteroscedasticity, you can use the Breusch-Pagan test or the White test. If either of these tests indicates that there is heteroscedasticity, you can try to correct for it by using a weighted least squares regression.<\/p>\n

Here is a brief explanation of each option:<\/p>\n