why is homoskedasticity important 
homoskedasticity is important because it identifies dissimilarities in a population any variance in a population or sample that is not even will produce results that are skewed or biased making the analysis incorrect or worthless the bottom linein a linear regression model homoskedasticity occurs when the variance of the error term is constant this indicates that the model is well defined meaning that the dependent variable is adequately defined by the predictor variable if there is too much variance in the error term the model isn t well defined this is known as heteroskedasticity too much variance indicates that there are other factors influencing the dependent variable these factors need to be considered through further investigation or modeling 