02-14-2016 05:04 AM
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.
02-14-2016 03:26 PM
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.