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Thread: Linear regression - help with meeting/avoiding the assumptions

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    Linear regression - help with meeting/avoiding the assumptions




    Hello all!

    I am tasked with fitting a linear model with one dependent variable y and one independent variable x.

    I am using R for the analysis.

    So i fit my model and have three problems. First of all the QQ plot is strongly suggesting non normality in the residuals. Second the variance is heteroskedastic. It is fanning out from left to right. Finally I have a problem with one outlier which has a Cook's distance of 86.

    Now I have no reason to delete the outlier and thought fitting a robust linear model would be a good alternative. This does indeed solve the problem caused by my outlier however the other two problems still remain.

    Note I am happy to fit either M estimator robust regression or quantile regression based on the median.

    My questions are as follows:

    1.) I read everywhere that robust regression is robust in the sense that it is robust to outliers. I have seen hints of suggestion that it is also robust to departures from the first two problems i mentioned, is this the case and if so can you point me to a text that verifies this?

    2.) If it is not the case, is it OK to apply sandwich estimators to my standard errors to combat the problem caused by non constant variance?

    Also note I have tried Boxcox transformations on both of the variables and have had no joy.

    Thank you very much for your time in advance!

    Laura
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    Last edited by Frost_Maggot; 11-25-2015 at 05:19 PM.

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    Re: Linear regression - help with meeting/avoiding the assumptions

    Can you post a scatterplot of the data?
    I don't have emotions and sometimes that makes me very sad.

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    Re: Linear regression - help with meeting/avoiding the assumptions


    Sure, I had to upload them as attachments as couldn't see another way

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