Hi On 25 Mar 2004, Bin Zhou wrote:
> 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? No, the two ways of computing r^2 should agree, no matter how many predictors you have. Just to be clear, if y^ = b0 + b1*x1 + b2*x2 .... , where one or more xs could be polynomial predictors, then R^2 = SSy^ / SSy (just another variation of your formula) will equal r(yy^)^2 Best wishes Jim ============================================================================ James M. Clark (204) 786-9757 Department of Psychology (204) 774-4134 Fax University of Winnipeg 4L05D Winnipeg, Manitoba R3B 2E9 [EMAIL PROTECTED] CANADA http://www.uwinnipeg.ca/~clark ============================================================================ . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
