I have a question regarding goodness of fit and associated probability testing. Here is my situation:
I have a data series (Array X) which I am performing a spectral analysis on. From that analysis I have found a certain number of periodic components (which in my case will be sine waves). I would like to test how well the original data series (Array X) fits a known sine wave (Array Y) of given frequency, amplitude, and phase. I would also like to test the statistical strength (associated probability) that the measure of the goodness of fit could not have been achieved due to randomness (i.e.: the probability that the measure of the goodness of fit of Array X to Array Y could have been achieved if the data in Array X was purely random). Due to my lack of formal education and knowledge in this area, I am hoping that someone here can illustrate the correct methodology for accomplishing this. Some tests which I have found, which may, or may not be appropriate are: Pearson's correlation coefficient Chi Squared Goodness-of-Fit Test Kolmogorov-Smirnov Goodness-of-Fit Test If you are able, would you please comment on the above tests (which I should use and how should I use them and which should I not) and if there are other more appropriate tests, would you please inform me. Thank you very much in advance for your assistance. With warmest regards. Andrew Peskin [EMAIL PROTECTED] . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
