# Validation of a linear regression model using R

I have created a multiple linear regression model with R using lm and glm. I am using lm on a training set and predict on a testing set to validate the model. In one test my results are within 80% of what they should be for 80% of the cases. It correlates with 40% for one response variable and with 63% for another response variable (but the response variable with 63% correlation isn't near the actual values of the prediction). I have 53 predicates. What is the probability of that occurring randomly? I've tried to build an multi-class svm off of the features using the predicates but so far the svm has been unable to properly predict the results.

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You could validate by simulation when the predictors truly have no effect or by permuting the responses to give an idea what prediction accuracy to expect purely by chance. – Macro May 20 '12 at 14:43
@Macro thanks macro i think your pointing towardss cross validation. I ran cv.lm with 10 fold cross validation on the datset with 63% corrolation and i found the Sum of squares = 2.91e+08 is Mean square = 1063519 and the data set with 40% corolation has Sum of squares = 23.1 Mean square = 0.08. A low mean square means that it has predictive ability so my main questions is how to i find the the probability of it concurring randomly? – caseyr547 May 20 '12 at 16:36
If you simulate data where the predictors truly have no effect, this will give you a reference. If you permute the response variables, you will also have a situation where the predictors have no effect, so that would be another way of getting a reference. – Macro May 20 '12 at 16:38