Heteroscedasticity and How to Address It

Heteroscedasticity and How to Address It

Statistical inference is concluding a population based on a sample. It is a fundamental tool in many fields, including economics, social sciences, and engineering. However, the validity of the statistical inference is often threatened by Heteroscedasticity, which is the unequal variance of errors in a regression model.  Heteroscedasticity can lead to biased estimation of regression coefficients, incorrect standard errors, and misleading hypothesis testing. Therefore, it is crucial to address Heteroscedasticity in statistical inference.  In this article, we will explore the impact of Heteroscedasticity on statistical inference, the common causes of Heteroscedasticity, and how to detect and address it using various methods, including weighted least squares, robust standard errors, and the White test.  By the end of this article, you will better understand how Heteroscedasticity can affect statistical inference and how to deal with it. The Impact of Heteroscedasticity on Statistical Inferenc…