In article <[EMAIL PROTECTED]>, Stan Brown <[EMAIL PROTECTED]> wrote: >les <[EMAIL PROTECTED]> wrote in sci.stat.edu: >>i have 327 data points and i plotted the histogram which >>looks like poisson distribution. I want to test the hypothesis that >>this distribution is significantly different from uniform and want to get >>get some confidence level (say 0.95 ). I was thinking of chi square but >>does'nt chi square assume normal distributed data?
>Not necessarily. Chi-square compared the observed number of data >points in each "bucket" with the expected number in each "bucket". >You would set up your "buckets" and then calculate how many data >points out of 327 would fall into each "bucket" if the distribution >is what you want to test, such as Poison. >What I don't know is how you decide how many "buckets" to use. >Intuition tells me that the number of "buckets" may influence >whether you get a significant difference or not, since in the >limiting case of n=1 you obviously get no difference at all. The chi-squared test of goodness of fit is generally a very poor test, with low resolving power. This is because it completely ignores the ordering of the data points. The test statistic only asymptotically has a chi-squared distribution, and this is only if one tests from a fixed distribution, which means that no parameters are estimated. If the parameters are estimated from the "bucket" proportions ONLY, the asymptotic distribution is still chi-squared with a reduced number of degrees of freedom. Other tests are more powerful, especially parametric tests. -- This address is for information only. I do not claim that these views are those of the Statistics Department or of Purdue University. Herman Rubin, Deptartment of Statistics, Purdue University [EMAIL PROTECTED] Phone: (765)494-6054 FAX: (765)494-0558 . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
