If you have tested some mice for a particular temperature, and then other mice for another temperature, then you may want to do that if you have some repeated measurements. If this is the case, then I do not think nesting individuals within temperature is the right thing to do. It seems to me that you have tested each mice in every temperature condition. Have a look at your distribution, if your sample size is low and the distribution doesn't seem to tend towards a normal distribution, you will have to find another statistical test that does not require the data to be normally distributed. You can find more about this debate here: Normality assumption and sample size Two-Way (Factorial) ANOVA Test the effects of two categorical factors and their interaction on population means. The data are those that appear in Table 17-3 of Howell’s Fundamental statistics for the behavioral sciences (7th ed.) and in Table 13.2 of Howell’s Statistical methods for psychology (7th ed.). SAS/JMP provides a powerful tool named diamond plot to visualize CI. The assumption of normality is sometimes hard to verify because some tests are too powerful for datasets with a large sample size. ANOVA2-JMP.docx Two-Way Independent Samples ANOVA with JMP Obtain the file ANOVA2.jmp from my JMP data page. There are at least two ways to view a CI.
#Two way anova in jmp plus#
OReilly members experience live online training, plus books. For the violation of normality, I am assuming that you may have a large sample size since you are working with mice. Get JMP Means Business: Statistical Models for Management now with the OReilly learning platform.
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