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What are the possible causes of Heteroscedasticity?
Heteroscedasticity is mainly due to the presence of outlier in the data. Outlier in Heteroscedasticity means that the observations that are either small or large with respect to the other observations are present in the sample. Heteroscedasticity is also caused due to omission of variables from the model.
Why is Heteroscedasticity a problem in linear regression?
Heteroscedasticity is a problem because ordinary least squares (OLS) regression assumes that all residuals are drawn from a population that has a constant variance (homoscedasticity). To satisfy the regression assumptions and be able to trust the results, the residuals should have a constant variance.
What we can say about the heteroskedasticity in linear regression?
What is Heteroskedasticity? Heteroskedasticity refers to situations where the variance of the residuals is unequal over a range of measured values. When running a regression analysis, heteroskedasticity results in an unequal scatter of the residuals (also known as the error term).
Is there no heteroscedasticity in linear regression?
As mentioned above that one of the assumption (assumption number 2) of linear regression is that there is no heteroscedasticity. Breaking this assumption means that OLS (Ordinary Least Square) estimators are not the Best Linear Unbiased Estimator (BLUE) and their variance is not the lowest of all other unbiased estimators.
Why does heteroscedasticity occur in large datasets?
Heteroscedasticity, also spelled heteroskedasticity, occurs more often in datasets that have a large range between the largest and smallest observed values. While there are numerous reasons why heteroscedasticity can exist, a common explanation is that the error variance changes proportionally with a factor.
Which is the second assumption of heteroscedasticity?
The second assumption is known as Homoscedasticity and therefore, the violation of this assumption is known as Heteroscedasticity. Therefore, in simple terms, we can define heteroscedasticity as the condition in which the variance of error term or the residual term in a regression model varies.
What are the assumptions in simple linear regression?
In Simple Linear Regression or Multiple Linear Regression we make some basic assumptions on the error term . Simple Linear Regression: Multiple Linear Regression: 1. Error has zero mean 2. Error has constant variance 3. Errors are uncorrelated 4. Errors are normally distributed