Ben, The other posters give some good advice, but you could also set simultaneous confidence intervals for all 7 categories.
If you are trying to establish "equivalence" then you could define some range that would be appropriate (say, within 3%). The CI's are easy enough to compute and I think a Bonferroni approach would work okay here. There are various methods of computing the intervals, but I would suggest a Wilson-type interval. (For a discussion, see the 2000 issue of the Journal of Statistical Software that discusses setting CI's for the multinomial) Warren May [EMAIL PROTECTED] (Benjamin Kenward) wrote in message news:<9vnj9m$s2c$[EMAIL PROTECTED]>... > Hi folks, > > Let's say you have a repeatable experiment and each time the result can be > classed into a number of discrete categories (in this real case, seven). > If a treatment has no effect, it is known what the expected by chance > distribution of results between these categories would be. I know that a > good test to see if a distribution of results from a particular treatment > is different to the expected by chance distribution is to use a > chi-squared test. What I want to know is, is it valid to compare just one > category? In other words, for both the obtained and expected > distributions, summarise them to two categories, one of which is the > category you are interested in, and the other containing all the other > categories. If the chi-square result of the comparison of these categories > is significant, can you say that your treatment produces significantly > more results in particularly that category, or can you only think of the > whole distribution? > > Thanks, > > Ben ================================================================= Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =================================================================
