Adjusted R square

[pawandh]

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??

[Norman21]

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. 

[Rick_SAS]

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.