<P> One is the generalized R was originally proposed by Cox & Snell, and independently by Magee: </P> <Dl> <Dd> R 2 = 1 − (L (0) L (θ ^)) 2 / n (\ displaystyle R ^ (2) = 1 - \ left ((L (0) \ over L ((\ hat (\ theta)))) \ right) ^ (2 / n)) </Dd> </Dl> <Dd> R 2 = 1 − (L (0) L (θ ^)) 2 / n (\ displaystyle R ^ (2) = 1 - \ left ((L (0) \ over L ((\ hat (\ theta)))) \ right) ^ (2 / n)) </Dd> <P> where L (0) is the likelihood of the model with only the intercept, L (θ ^) (\ displaystyle (L ((\ hat (\ theta))))) is the likelihood of the estimated model (i.e., the model with a given set of parameter estimates) and n is the sample size . </P>

R square explains how much of the variability in y is explained by x