# Significance of $r^2$ value

I know about $r^2$ tells you about the amount of variation that can be explained by the predictor variables. I have run a model in which the rsquare has value 0.3010 but has false positive rate of around 15.60%. So this model which is logit in nature predicts 84% cases right. I want to know two things:
1) Is this false positive rate significant, i.e. is this prediction good enough?
2) Does it matter if I $r^2$ is as low as 0.3 if my model is good in prediction as we know that $r^2$ is a value used for explaning and not predicting?

• Does it predict new data with 85% accuracy, or part of the original data set that you held back for validation? Nov 5, 2010 at 11:08
• @richie..that rings an alarm..I didnt test it for any data. I ran a decision tree and it gave me say x false positives and y true positives than x/x+y is .156 which is misclassification..am i right then in saying that it has 84.6% accuracy ? Nov 5, 2010 at 11:24
• Which $R^2$ measure are your referring to? If I understand you question correctly, this is logistic regression, and you may find useful information on this related question, stats.stackexchange.com/questions/3559/….
– chl
Nov 5, 2010 at 12:14
• For future: when you don't know which tag to use, add for-retag.
– user88
Nov 5, 2010 at 12:16
• Isn't it $R^2$ rather than $r^2$ ? Nov 5, 2010 at 13:41