homoscedasticity and heteroscedasticity
One of the assumptions of linear regression is that the variance of the error terms across all the observations is constant.
However, if this assumption is violated, we talk about heteroscedasticity .
But why exactly do we want constant variance of error terms across all the observations? I get it that if the variances of error terms change , thats a problem , but isnt it be more representative to give more weights to larger variance of error terms rather than giving all the variance of error terms the same weight?
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