So related to our last post, we are going to talk about what’s the best outcome to take if we have multiple imputations, each giving us slightly different data. Or, going back to our trilogy example, which dimension is Tina most likely to be in. So say she could be in Indianapolis or Miami or Atlanta where the average temperatures in January are 31, 75, and 47 degrees Fahrenheit, respectively. And again, why did she not just stay in the Miami realm in that case? Well, again, you’ll just have to read the books and see, won’t you? Now, let’s say that we get 100 temperature readings but misplace 40 of them … but replace them via multiple imputation based and take the average of the average temperatures from each of the data sets. So with the missing values, the average temperature calculated from the original 60 observed values is 49.91911. But if we impute the data 5 times, we get averages of 49.67150, 50.53665, 50.37694, 50.14105, and 50.35803. And the average of those averages are 50.2168. Anyway, hope that’s enough averages for ya! And on the average of the average of the … you get the picture … it looks like Tina was most likely in the Atlanta realm that January. Maybe not as nice as the 75 she could have had in Miami, but hey, at least she, um, well uh, could always have access to some delicious peach cobbler.
I mean, just look at that deliciousness! But even being in the Indianapolis realm wouldn’t be that bad, as discussed before. But anyway, till next time, hope you dig in!