So variants you remember is mean squares. It's the sum of squares divided by N.

The mean is now a vector with 1 element for each of the variance.

12.4 is much closer to 13 than 10, and that's because the Gaussian centered on 13 has a much narrower variance than the one on 10.

It changed this value the most.

Now what is going to be the variance?

We can actually model that behavior better as a fluid, if you look at it.

From farther away, they may be no change in pixel location.

The mean is easily calculated it's 2 and 4.

And again, start to embrace this notation, this is gonna be very ah, common, and we'll use it a lot as we move forward through the course.

This was quite too good to lose, Watson.

There are 4 possibilities 0 and 0, 1 and 0, 0 and 1, and 1 and 1.

And now I'm going to be really demanding.

Three over four.

A fun word to say.

So 7 will be the correct mean.

They could be market based, or non market based they could be decentralized, or centralized.

The real challenge is to compute the variance of a coin flip where the outcome is p.

But let me tell you a little bit about the other shift that I'm talking about, which is power diffusion.

Let me give you another word that's really important called standard deviation.

(Laughter)

Power is multi polar.

When he feels fine, he goes for a walk.

If you take 1 4 plus 1 4, then you get 1 2, so 1 over 1 3 equals 2, which means the new variance term is half the size of the previous variance terms.

For each of those check exactly one box.

And as a hint, at some point we talked about something like the following what if you multiple a variable by a constant A, what happens to its variance?

Let me say a word now about the distribution of power and how it relates to power diffusion and then pull these two types together.

But it is the expected value.

We just explode, and we keep exploding at an ever increasing rate.

Let's take a walk for a change.

When I feel fine, I go for a walk.

And the width of this Gaussian is Gaussian thus controlled by sigma by that variance parameter sigma.

We're almost at the correlation matrix.

As we know, this gives us an estimated mean.

Sampling error clearly depends on the size of the sample so, and, but also the degree of variance in the population.

If you go look at it, actually it creates almost ten and half million lines of C code that spread across 800 files.

It's really all I can do today.

So if the user writes in code like this and we're making our JavaScript interpreter for our web browser, what should we turn it into?

If we went from this step to that step where we're multiplying it, that might have been, because you're really just doing the distributive property, but I don't want to confuse you.

Now this looks a little bit wild, and this is probably the worst formula you've encountered in this class in terms of complexity but once you dived in, you'll realize this is really related to a lot of stuff you've seen before such as variances and similar.

This is called often covariance if you've normalized, because it is the variance calculation of a two co occuring variables.

If you want you can look at sort of the changes in the means from time one to time two.

In our normal, ignoring normalization constant, we know that everyone's salary is drawn from a distribution that looks like this.

You might think of that as the privatization of war.

After his defeat, they were pried out and dispersed around the globe.

We'll let R do the work.