# Heteroscedasticity in the regression in computer generated data

I have a computer generated data and when I plot the raw data, I see the following plot.

As a quick and dirty method, I applied linear model to this data and when I plot the predicted vs residual, I got expanding flannel plot which shows that the variance is not random. Therefore, I used BoxCoxTran method in caret package to transform the input variable. I got this message : Lambda could not be estimated; no transformation is applied. The studentized Breusch-Pagan test still shows the p-value to be < 2^-16, which confirms the heteroscedasticity problem in the data.

When I plotted predicted value vs residual, I got flannel shaped scatter plot.

I tried transforming the idependent variable (taking sqrt, log, second degree polynomial), but nothing helped.

What other methods should I try to create a better classifier?

Edit: After having clue from the responses below, I created a new variable and then tried to create a model. I finally got relatively better model. The residue plot can be seen below.

new_col = Y/(X+0.1)
df$new_col = new_col model_lm <- lm(Y ~ new_col^2, data = df, na.action=na.exclude)  My new question is: For unknown data, how can I get the response? • Weighted least squares? Apr 23, 2017 at 12:20 • These data can be handled beautifully with linear models--and probably do not need transformation or weighting. Plot$y/x$and proceed from there: that should make it obvious what is going on and it will reveal important details of the smaller variations in the data that are presently obscured. – whuber Apr 23, 2017 at 14:13 • This appears to be repeated-measures data. I suggest you look into mixed effects models. In fact, you data looks exactly like what I usually sketch when I explain mixed effects models to our PhD students. Apr 25, 2017 at 7:41 ## 1 Answer Depending on your goals there are different methods that do not assume homoscedasticity. Two such are quantile regression and regression trees. • The heteroscedasticity in this plot has such an obvious structure that it would be a mistake to work around it. This is clearly a collection of lines through the origin. Another way to put it is that there is an extremely strong correlation structure to these data. Quantile regression will not cope with that. A regression tree would make short work of it indeed, provided one were to introduce an appropriate variable--namely, one that depends monotonically on$y/x\$.
– whuber
Apr 23, 2017 at 14:15
• @whuber, please see my edit. As suggested by you I am able to find relatively good model. For test data (When response is not known), how this model will work? Thanks for your response. Apr 25, 2017 at 3:56