Thanks for your response. Could you give me some suggestions on how to test whether a model fitted the dataset significantly?
Even if model A fits the data significantly better than model B, we still don't know at what extent model A fits the data. Maybe both A and B are far away from the truth. [EMAIL PROTECTED] (dave martin) wrote in message news:<[EMAIL PROTECTED]>... > [EMAIL PROTECTED] (akhan) wrote in message news:<[EMAIL PROTECTED]>... > > Is there any statistical metod which can be applied to test whether a > > non-linear model fit a dataset well or significantly? > > Fisher's F-test can be used to quantitatively compare two models of > data. > > The F-test can answer the question "Are these two models significantly > different at the X% level?". > > One model can be constant; i.e. assume that data scatter is entirely > random about the dependent variable mean & not dependent on the > independent variable(s) at all. > > Or one can see if adding another parameter to a fitting function makes > a significant difference. > > Or one can find out which of two arbitrary models best fits the data; > this is very useful for comparing two theories. > > As an example, alternate theories for chemical diffusivity D are: > > (1) D=(K)*exp(-Q/T) > (2) D=(K/T)*exp(-Q/T) > where K and Q are experimentally determined constants and T is > temperature. > > Given a set of (D,T) data the F-test can be used to see if there is a > significant difference between these two models. > > Any number of models can be compared using the F-test by testing two > at a time. . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
