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Hi everyone, I am building a linear regression model and I found that, when I plot the residuals (y axis) versus the predicted values (x axis), I get something that appears linear with the variance increasing as the x value increases (wedge shape). I have found the following recommendation for fixing this kind of problem of non-constant variance (following is my interpretation of it): run unweighted regression and save predicted values and residuals, calculate the variance of residuals at each predicted value, calculate reciprocal of the variance (this is the Weight), merge this Weight back to the original dataset via predicted value, run weighted regression (using the Weight variable calculated), resulting residual plot should have more constant variance across predictor point. Does anyone have experience with this approach? Did I understand this right? Any other ideas to fix my problem (I have already tried several transformations of predictors)? Thank you in advance!!!! Regina