If all , then . Now, on another hand, if , then , where g is the number of adjustable parameters and N – g is the number of degrees of freedom in the mathematical model for the data. We’d like to see for a “good” fit, while indicates that the quality of the fit is ambiguous (sometimes called over fitted), and indicates a “poor” fit.
, then 
. Now, 
, then 
, where g is the number 
for a “good” fit, while 
indicates that the quality of the fit is ambiguous (sometimes called over fitted), and 
indicates a “poor” fit.