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02-19-2017 08:57 AM - edited 02-19-2017 08:58 AM

I am facing a case of heteroskedasticity of the errors in linear regression. I know there are different ways to account for it, but my professor told me to use PROC MIXED which can handle non-cosntant variance.

In case of categorical X2 the code should be the following:

```
proc mixed data=mydata;
class X2;
model Y = X2 / ddfm=satterth;
repeated / group=X2;
run;
```

Which iterations converge without problems.

But my independent variable X1 is continuous and I don't know if the code I wrote is right:

```
proc mixed data=mydata;
model Y = X1;
repeated / group=X1;
run;
```

Which gives me back the warning of infinite likelihood, probably because of:

- too many parameters to estimate.

- no variation is some values of X.

- my code is not right.

Hence, in summary I don't have repeated measures, I just want a model which take into account different variances of the residuals.