We'll let R do the work.

We're almost at the correlation matrix.

That's just dividing by N.

It's pretty cool.

Because those correlations were based on samples that had a broader range of cognitive ability.

And now I have a variance co variance matrix.

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

We're just standardizing, the variances to be one.

And, if X and Y overlap, if they co vary.

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

You can see it over here 10, 8, 6, 4, and 2.

And the co variances to fall between negative one and positive one essentially to be correlations, alright.

We take the product of the variance of x and the variance of y modally with the normalizers and we get something quadratic even in variance space.

But this one is just like the variance calculation but it mixes x's and y's whereas these are x² and y².

The Pearson product moment correlation coefficient, little r.

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

For each of those check exactly one box.

The prior has a mean of mu and a variance of sigma squared.

Dispersion

Variance

Is there any way to pull out this uncertainty from our omega matrix?

That gives me my new x.

Velocities are 0 and 20 for the decrements of 2.

This is very standard linear algebra.

It does help reinforce the concept, of correlation and covariance.

I finally want to teach you about the concept of a standard score.

A monitor for an Icecream compilation network

Distributed Process Launcher

Dispersion samples

Field variance?

So James Surowieki has this idea of the wisdom of crowds, that says that a whole bunch of people are smarter than any individual.

Dispersion of the normal distribution

In, in doing examples in'R', So a co relation matrix is a special matrix and the unstandardized version of that is the variance, co variance matrix is of special in statistics.

We gotta split them up!

The covairance matrix looks as follows.

So those would be the high peaks of the density field.

Remember, variance is just sum of squares divided by n, if we're doing descriptive, and that's all we're doing here.

The way you get that is divide the 6618 by 83. How about the variance?

The solvents in them do that very efficiently.

Yes, there is. It turns out the omega matrix is the inverse covariance matrix.

We're going to use these throughout this entire course are standard deviation and variance.

I'll get rid of my body's excess water.

The kalman filter in contrast, under certain assumptions, is quadratic.

I feel like taking a walk.

We see that indeed it was, it was 0.6.