On 27-Apr-2004, Michael Hochster <[EMAIL PROTECTED]> wrote: > : This isn't a "simple linear regression" problem. It is a nonlinear > : regression problem. There are a number of nonlinear regression programs > : that can solve your problem for a and b. Here is such a program that I > ran > : through my NLREG program (http://www.nlreg.com) > > Yes, it is a simple linear regression problem: ordinary regression > of y on e^-x. As the author of regression software, you should know > better.
I agree, by transforming the input variables this function is easily converted to a linear regression. But it can be handled more easily and properly as a nonlienar regression where no transformations are required. Remember that fitting a function to a transformed independent variable does not always yield the same fitting parameter results as fitting the function to the non-transformed input -- minimizing the sum of squared deviations for X is not the same as log(X) or sin(X). The difference can be significant. -- Phil Sherrod (phil.sherrod 'at' sandh.com) http://www.dtreg.com (decision tree modeling) http://www.nlreg.com (nonlinear regression) . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
