Imputing new worlds with multiversal appeal

# Monthly Archives: January 2016

## New Topic Next Week!

But now, a kitten on the beach!  I mean, what’s not to love … a kitten … a beach … all you need is your very own copy of … yeah, you get the picture.

## Incidental comparisons

So a brief of review in cumulative incidence rates indicates the rate of something happening over time.  But what if we want to compare several such rates, say, if between dimensions to see if the number of times Anton’s henchmen were caught over time were different or not.  Well, we have something called the Gray’s K-sample test, which depends on the ratio between the sum of the means of the incidence rates over the sum of their variances.  And then we can determine if the henchmen get caught in those different dimensions at different rates or not.  Which may be useful to Randy if he wants to determine where Anton strikes next … and you know how to find that out!  But until  next time … I’ll just part with telling you about my next passion … screenwriting!

In other words, it’s going really well.

## WEE and inverse probabilities

So how are the weights calculated for the WEEEEEEEEEEEEEE, sorry, I mean, WEE equations? It’s a model where observations are given weights inversely proportional to the probability that they were observed.  And we do that as we do not want to get our results biased.  Kinda like if Anton were to actually leave Randy clues about which dimension he could be in.  But could Randy really trust those clues? Or could it be a trap?  Hmmm … well, in order to find out …. oh yeah … but anyway.  Long story short, he might not want to trust those close so he actually ways those dimensions less than those without clues.  But anyway, that was just a little more about WEEEEEEEEEEEEEE, sorry, I mean, WEE equations.  Until next time …

## A Weighty Issue

So why don’t we talk about another way to handle missing data which involves weighted estimating equations, or WEE.  Isn’t it fun just to say at least?  WEE!!!!  And what it is is it models entries with different weights, depending on how much missing data is in that entry.  Kinda like how Randy would assign different weights to dimensions where he could find Anton depending on how much info he had or didn’t have on that dimension.  And how can he do that? Well … we can get into that next week.  Until then …

WEEEEEEEEEE!!!!!!!!!!!! …………………..

## Getting skewed with skewness

Hello! Did everyone have a good New Year?  Great!  So here we go — today, I’ll talk about skewness which basically tells you which values are more likely to happen based on whether they are higher or lower than the median value.  So if Randy were more likely to find Anton in a warmer climate, he’d want to look at a distribution of dimensions skewed towards higher temperatures if in fact the distribution he was looking at a distribution of temperatures of the dimensions. Kinda like this …

So there you have it.  Skewness in a nutshell.  Well, until next time!!  And if you need some reading material then … yeah, I’m stopping.