It seems like there is strong evidence that the "offshore" rate is different to the other two rates, but inconclusive evidence as to whether "inshore" and "mid-channel" rates differ. This is showing strong evidence against equal rates, but not in strong evidence favour of a defintie alternative. The model is a bernouli model for the indicator of "death", $Y_$ by about a factor of $4$ - fairly weak evidence in favour of equal rates. I present a probability analysis which does extract this information - it agrees with the chi-square test, but it gives you more information than the chi-square test, and a better way to present the results. Topics of particularinterest to the biological or health science fieldinclude odds ratios, relative risk, and survivalanalysis. Understanding Chi Square Test Results 422. so 30 means 30 organisms died, and 31 means 31 organisms didn't.īased on this the chi-square should be fine, but it will only tell which hypothesis are not supported by the data - it won't tell you if two reasonable hypothesis are better or not. Using the powerful capabilities of JMP, the bookaddresses problems requiring analysis by chi-squaretests, t tests, ANOVA analysis, various regressionmodels, DOE, and survival analysis. Creating a JMP Contingency Table from an Existing Summary Table 411. Steps to run a chi-square test in JMP: Click Analyze -> Fit Y by X Fig 1.1 Analyze>Fit Y by X Select Results as Y, Columns Select Supplier as X, Factor Select Count as Freq Fig 1.2 Distribution for Y and X Click OK Fig 1. I am going to assume that "100% survival" means that your sites only contained a single organism. However to set up those models more details would be needed concerning the 100%>mortality>0% sites. Logistic regression and binomial regression may be even better as they not only give you p values, but also useful estimates and confidence intervals of the effect sizes. The Pearson chi-square statistic ( 2) involves the squared difference between the observed and the expected frequencies. Each chi-square test can be used to determine whether or not the variables are associated (dependent). (A resampling approach may help tackling with ties.) Minitab performs a Pearson chi-square test and a likelihood-ratio chi-square test. There are established post hoc tests for the Kruskal-Wallis test: 1, 2, 3. (What about using the original percentage values instead of categories?) A version of Kruskal-Wallis test may be appropriate here that takes ties appropriately into consideration (maybe a permutation test). In this case you wouldn't any more compare percentages, but compare ordinal mortality measures across three site type categories. Inshore/Midchannel/Offshore look fine, however unless "less than 100% mortality" means "100% survival" in this biological setting you may need to construct tables that account for all the cases observed or explain why you restrict your analysis to the extreme ends of the sample.Īs 100% survival means 0% mortality, you could have a table with columns 100%=mortality / 100%>mortality>0% / mortality=0%. A contingency table should contain all the mutually exclusive categories on both axes.
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