Please recommend a better test strategy??
- To: mathgroup at smc.vnet.net
- Subject: [mg24974] Please recommend a better test strategy??
- From: Richard Palmer <mapsinc at bellatlantic.net>
- Date: Mon, 28 Aug 2000 08:27:31 -0400 (EDT)
- Sender: owner-wri-mathgroup at wolfram.com
I'm examining some binary response data using a non parametric approach. I want to test the parametric approach. For each observation in the data, I estimated a probability of response: call it Pr[response][observation(i)]. I have the observed response for the data, call it ObservedResponse[observation(i)]. I created a new table with 10 cells and three columns. The first column counts the # of observations where the estimated Pr[response][observation(i)] is in the interval [0,0.1}, the second column of the new table is the sum of the probabilities in this category, and the third column is the count of the number of observations where a response occurred. If I divide the second and the third column by the first column, I have an average probability for the range, and an an average frequency of response for the range. The other 9 cells of the table are constructed similarly. There are more than 20 observations in each cell in the new table. I want to test the hypothesis that average frequency=average probability. A regression model average frequency=c+k * average probability +error does not reject c being 0 and k being 1. Is there a better test strategy? Would some Chi Square variant work here? Richard Palmer