Otherwise known as the probability distribution function.
So you have one value,
But how likely could it be?
Go to the right — not so much.
Go to the left — ah, now see.
So Randy wants to cover the greater area,
But is that where he shall land?
Then you’ll understand.
Anyway, till next week! Oh, and Randy on a distribution plot would look a little like a mountain climber on a mountain, don’t you think? 😉
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 …
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 …
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.
Okay … lets talk about … the mode! And what is … the mode? Well … the mode … is the most frequent number in a list of numbers. So lets say Randy is given a list of dimensions where Anton is. And some dimensions appear more than one and more than some others on the list. The dimensions that appears the most on the list would be … the mode!! Now, wasn’t that an easy lesson for today? Yeah, thought so too 😉 So anywhere what dimension appears the most with Anton in it anyway? Well, you know how to figure that out!
But anywhoo, till next week!
You have two underlying variables,
Following a distribution so normal,
But then you see that they are,
Binary after all.
Sort of like Randy finding Anton,
In a world good or bad,
Maybe underlying bad Anton,
Is the weather of income that is said.
So another poem is created,
Maybe next time we do one with the fantasy cast or no?
We will yet see,
But the perfect holiday gift for a scifi lover is, you know.
So the coefficient of variance or CV came up quite a bit at work recently and so I decided to talk about that. Why? Because I can! Because it’s my blog! And because it’s another short and sweet topic that we can cover before you go on with your holiday shopping ** cough, cough ** Order of The Dimensions ** cough, cough ** perfect gift for any holiday lover ** cough, cough **. And what it is is a measure of the variation relative to the mean. So ideally, we for the CV to be lower but a low standard deviation and a low mean could give a similar CV as a high standard deviation and a high mean so really we want a low standard deviation and a high mean. Got all that? Good, I hope. But just to be sure I’m doing another Randy-Anton example. So say Randy’s looking at three dimensions, one where he has a high probability of catching Anton among a lot of places but those places are so far apart, one where he has a low probability of catching Anton among a lot of places but those places are close together, and one where he has a high probability of catching Anton among a lot of places and those places are close together. So where would he look first? There you go — the third one as that would be his “low CV” as you will. But does he find such a dimension? Well, maybe you and the scifi lover on your holiday list then … yeah 😉