Programming the statistical procedures from SAS

Adjusted R square

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Contributor
Posts: 59

Adjusted R square

Adj R2=1- (1-r2)(N-1)/N-p-1

 

where N=total sample size

        p=no of predictors

         r-coeff of determination

 

 If i get a negative adj r2 then what can i conclude from that??

Contributor
Posts: 66

Re: Adjusted R square

The predicted R-squared helps you find out how well the model fits the original data. Generally, if this is low (approaching zero, or negative), then your model is not very good.

 

"Adjusted" R-squared lets you compare regression models with different numbers of predictors.

 

So, to answer your question, your model is not very good and you should try another.

 

Norman. 

Norman.
SAS 9.4 (TS1M4) X64_7PRO WIN 6.1.7601 Service Pack 1 Workstation

SAS Super FREQ
Posts: 3,547

Re: Adjusted R square

It is possible. It usually means that you have many explanatory effects compared to the number of observations. I can't remember the rule-of-thumb right now, but some experts recommend a minimum of 10-20 observations per regressor.  If you have only 100 observations and you try to use 75-100 effects, your model will be bad and you might get a negative adjusted R-squared value.

 

Limit yourself to main effects instead of many interactions, or choose fewer explanatory variables.

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