Programming the statistical procedures from SAS

Nonlinear regression

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Super Contributor
Posts: 412

Nonlinear regression

 Hi,

 

I am trying to make a linear regression but my data is heteroskedastic, so will have to do a nonlinear regression.

 

Could you please tell me what sas procedure is good for nonlinear regressions, and which will also find the right model because I have several variables and don't even know if any should be logarithmic or exponential for example.

 

I tried searching the internet but only got more confused...

 

Thank you 

Respected Advisor
Posts: 4,606

Re: Nonlinear regression

We need a lot more information about your data to suggest an appropriate statistical modeling approach. Nonlinear regression is not the most common way to deal with heteroskedasticity. Generally, if your data looks like this:

 

Heteroskedastic.png

i.e. heteroskedastic because the standard deviation of the errors is not constant but proportional to the mean, then a logarithmic transformation of the dependent variable (y) is often a suitable approach.  

PG
Super Contributor
Posts: 412

Re: Nonlinear regression

Hi,

 

Thnak sfor the reply, I didn't answer for a while becasue I didn't knwo that I ha d areply, the new sas communities site doesn't send an email when there is a reply...

 

I tried transforming the y variable into log y (among others) but was still getting heteroskedasticity...

 

Please see what the y vs x graph and residual vs x graphs look like in the attached word doc (couldn't just copy and paste it here)

 

 

Thank you!

 

 

Respected Advisor
Posts: 2,655

Re: Nonlinear regression

[ Edited ]

I really don't see much evidence for heteroskedasticity, especially in the plot of car3 vs ln_mkt.  The residual plot does show some more variablity at low values of the independent variable, but this may be more a function of high influence points.

 

Consider using either PROC ROBUSTREG or PROC QUANTREG to fit the data.

 

(On the other hand, I don't see much use of the independent variable in these plots as a predictor.  That regression line is nearly as flat as the Texas Panhandle).

 

Steve Denham

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