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Understanding Variance, Co-Variance, and Correlation

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Re: Understanding Variance, Co-Variance, and Correlation

#4

Post by mturmon »

thetwiceler wrote: post #3

To define variance as E[x^2] - E[x]^2 and not ever allude to the far more meaningful version, E[ (x - E[x])^2 ] is just criminal. This is not at all a good explanation of variance, covariance, and correlation.

I have to agree with you here.

One problem with introducing it the way the article does is that it's hard to see why the variance is never negative, and is zero exactly when the R.V. is constant.

This is a very important property, to say the least.

It would be better to say you measure the "energy" with

  E x^2
but that this is not immune to level shifts, so you need to subtract some constant off first. And it so happens that the optimal constant to subtract off is our friend E x.

Edited to add: The notion of introducing the ideas of a sample space and a random variable (in the technical sense), as is done in the article, and at the same time being shy about calculus, is rather contradictory. That is, the intersection of

  { people who want measure-theoretic probability concepts }
and

  { people who don't know calculus }
may be empty.

Re: Understanding Variance, Co-Variance, and Correlation

#6

Post by hessenwolf »

mturmon wrote: post #4
thetwiceler wrote: post #3

To define variance as E[x^2] - E[x]^2 and not ever allude to the far more meaningful version, E[ (x - E[x])^2 ] is just criminal. This is not at all a good explanation of variance, covariance, and correlation.

I have to agree with you here. One problem with introducing it the way the article does is that it's hard to see why the variance is never negative, and is zero exactly when the R.V. is constant. This is a very important property, to say the least. It would be better to say you measure the "energy" with E x^2 but that this is not immune to level shifts, so you need to subtract some constant off first. And it so happe…

It looks like an enthusiastic newbie with some clipart and an equation. Fair play for trying.

I would suggest adding the following.

1. What the poster above said.

2. The reason for the E[(x_{bar} - x_i)^2] choice. Why not E[|x_{bar} - x_i|]? Was it a mathematical convencience? Was it, perhaps, because Gauss had the integral of e_{t^2} from -Inf to plus Inf lying around in a letter from Laplace?

3. It is an equation with a square. Use a square somewhere.

4. The square root of the variance happens to be the horizontal distance between the mean and the point of inflection in the normal distribution. How cool is that?

Re: Understanding Variance, Co-Variance, and Correlation

#7

Post by edtechdev »

This is a horrible explanation of variance. And it's missing WHY we need variance, or, what is the usefulness of variance vs. other measures like mean and range.

Say you want to buy a car and want to choose a brand and model based on user ratings of quality and value online.

Cars A, B, and C all have the same average rating - let's say 8 out of 10. How to choose? You need more information, but all you have are the ratings.

You could look at the range of ratings. This is the difference between the maximum rating and minimum rating. But what if only one or two people gave a car a bad (low) rating of 1 or 2, whereas another car had a lot of low ratings of 3 and 4, but no one rated it a 1 or 2. If you just look at the range, it might not be a good characterization of the ratings on the whole, because just one person (data point) can skew the information.

You want to look at the spread of the ratings - how consistent or variable the ratings are. A car with a lot of 7, 8, 9 ratings is better than a car with ratings all over the place, that happen to average the same (8). When you buy a car with an average rating of 8 out of 10, you expect a car that is an 8. You want to minimize the chance of getting a lemon.

This spread can be calculated by looking at the difference between each individual rating with the average rating. If you add up all these differences though, the negative differences with the mean would cancel out the positive differences with the mean. With variance, this difference is thus squared to make them all positive (or zero). And so on...

Re: Understanding Variance, Co-Variance, and Correlation

#8

Post by anonymousDan »

edtechdev wrote: post #7

This is a horrible explanation of variance. And it's missing WHY we need variance, or, what is the usefulness of variance vs. other measures like mean and range. Say you want to buy a car and want to choose a brand and model based on user ratings of quality and value online. Cars A, B, and C all have the same average rating - let's say 8 out of 10. How to choose? You need more information, but all you have are the ra…

But why take the square and not just the absolute value of the differences? Is the idea to emphasize outliers and hence give higher variance to skewed datasets?
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