So today’s topic is fairly simple and I hope provides an effective argument on why multiple imputation is preferred to say, single imputation, an example of which is hot deck imputation.  And that is that we may want to impute the data several times so that we don’t underestimate the variation in the data.  And why is this important?  Well, just take Tina in our trilogy.  We may want to know where she is at one time and if we just impute the data once, we may determine she’s in Philadelphia.  But she may not be in Philadelphia; she may be in Atlanta … or St. Louis … or Miami … or you get the picture.  Although she might want to be in Miami.  And why not, you say?  Because she’s crazy!  No, no, that’s not it.  Truth is you might want to read my third installment, Final Orders, to find out why.  So how can we determine which dimension Tina is most likely to be in?  Well, one way is to take the average estimate of the probability that she is in each dimension.  But we can cover that next time too.  Now, I’m not saying being single is a bad thing.  In fact, I’m being followed by this cool twitter account, live_singer.  In fact, Tina also might want to stay single in the dimensions that she does.  Why, you ask?  Because she’d craz … again you might want to read my third installment, Final Orders, to find out why.  But it might also be fun to imagine yourself in different places, like Indianapolis or Paris or some other place that end with -is too.  And although Tina might not want to go back to Miami, we might want to.  So here you go, Bienvenidos a Miami.