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04-23-2013 11:42 PM

In excel, I have used a second-order polynomial forecast which is fit to intercept the Y axis at 10,000. Excel reports an R^2 of 0.25 for this relationship.

I'm trying to replicate this in SAS in order to observe significance levels/standard errors with the following model:

proc reg data=data2;

model revenue=xr xr2 /white spec;

restrict intercept=10000;

run;

This model outputs parameter intercepts identical to Excel's. Unfortunately, the R^2 is insane at 0.9963.

SAS results report: "Restrictions on intercept. R-Square is redefined". How should I be interpreting the R^2 then? Also, is there any way to output a graphical output which exhibits the confidence bands off the base of the data?

Thanks

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Posted in reply to Wolfram

04-24-2013 03:34 AM

Hello,

I would like to hear what is the mean of revenue. My thoughts are that if it is far from 10000 than it can make sense.

Basically the R^2 is a measure which tells you how much more of variance of your variable REVENUE can be explained by your model compared to the model with intercept only.

I would say the R^2 in excel will be estimated using the model revenue=intercept + resid for comparison (which is estimated by average of revenue),

on the other hand the SAS will use model revenue=10000+resid for comparison and since this one could be pretty weak than the valu 99% additional fit can make sense.

Jakub

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Posted in reply to Wolfram

04-24-2013 08:45 AM

If it were me, I would subtract 10,000 from my Y variable and use the NOINT option on the model statement to consider lines that pass through the origin. (But then I'd ask myself, "why am I considering only no-intercept models.)