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How do you test for heteroskedasticity in multiple linear regression?
To check for heteroscedasticity, you need to assess the residuals by fitted value plots specifically. Typically, the telltale pattern for heteroscedasticity is that as the fitted values increases, the variance of the residuals also increases.
What is heteroscedasticity as used to assess a linear regression model?
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.
How to detect heteroscedasticity and rectify it?
The top-left is the chart of residuals vs fitted values, while in the bottom-left one, it is standardised residuals on Y axis. If there is absolutely no heteroscedastity, you should see a completely random, equal distribution of points throughout the range of X axis and a flat red line.
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.
When does heteroscedasticity occur in a data set?
As you can see in the above diagram, in case of homoscedasticity, the data points are equally scattered while in case of heteroscedasticity the data points are not equally scattered. Often occurs in those data sets which have a large range between the largest and the smallest observed values i.e. when there are outliers.