Quote (timmayX @ Jul 3 2017 02:16pm)
well I was thinking of a way to prove it in terms of value.
my issue is with non-linear lines such as
so far I have tried to run anova on it (there were a lot more variables, but I included "weight loss" to show my partner that colinearity occurs when you have a variable that is a function of given variables, and it looks really amateurish to include this)
So to make the graph become homoskedastic, from experience, I know you apply a dummy variable (in this case ln) to increase the R-squared value and reduce the variation in the regression.
However, I forgot how to prove the graph is heteroskedastic (in math/stats terms) to apply this. For OLS "Best Linear Unbiased Estimator" we use one of gauss-markov assumptions before you do a regression, and prove it is homoskedastic by covariance over variance equation (or it could be the other way around.)