Peter- Maybe I have not articulately my problem clearly, I have had local help with the statistical part just trying to figure out how to correctly program this test. For clarity's sake, I have months worth of data, I want to potentially combine those months into four, shall we say seasons, that have close to the same behaviour. Therefore to do this, I am trying a monthly moving window to categorize these seasons. After talking to a couple water resources statisician's we decided the way to test if the months are different is through the use of hypothesis testing and a dummy variable. So I have one regression, Model A, that includes a zero in the dummy spot with the two months of data combined, then I have another regression, Model B, that includes the interaction term for the changes between the months (the intercept changes, using a 0 or 1 dummy variable). Now we discussed running a Wald testing, Chi squared, to test to see if the interaction term is of importance probability wise, can I do this utilizing anova? Does this make more sense? Then I will run another set of restricted and unrestricted models to account for potential differences in the mean (i.e the slope). Does this explain my problem better?
Meredith -- View this message in context: http://r.789695.n4.nabble.com/Hypothesis-Testing-using-Wald-Criterion-for-two-regression-models-with-dummy-variables-tp4601582p4603260.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
