Hello, all, In linear regression, Y1 is dependent variable, Y2 is predicted value. R is Pearson's r for Y1 and Y2, so I can get R square. I can also get R square by the following formula. R square = 1 - SSE/CSS WHERE SSE = the sum of squares for error CSS = CORRECTED TOTAL SUM OF SQUARES FOR THE DEPENDENT VARIABLE.
I found the two values are different. So I think I can only use the second formula for nolinear regrssion, in linear regression, I can only calculate pearson's r. Is it right? thank you very much. . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
