So a good measure of central tendency just describes the center point of a distribution.

I've got in my example minus the mean, minus the center, minus the mean of distribution.

Middle value in the normal distribution

We will be able to look at income distributions in completely new ways.

You think I act like this because I'm egocentric?

So first let's revisit this normal distribution.

Dispersion of the normal distribution

We distributed these rapid transport corridors throughout the city center, and extended them into the outskirts.

Mean of the distribution

Your purse?

Let's start with 2 Gaussians and say they have identical variances. Let's multiple them.

A positive times a positive contributes more to the sum.

Very good I got it right it would be embarrassing if I didn't lets do the next question.

Suppose our mean is 10 and 13, and the variances are imbalanced 8 and 2 which corresponds to the following picture.

And to arrive here

In the generative version of expectation maximization you first draw a cluster center and then we draw from the Gaussian attached to this cluster center.

This tells you the height of the normal distribution function.

Don't have it, left my wallent in the car.

Finally the shape of this distribution of sample means will be approximately normal even if the distribution in the population isn't normal.

Thus, I suspect that each of these examples was distributed according to a normal distribution or

little higher than if you do it, uh, orally. So we looked at this histogram before.

Let me ask the hard question now.

Linear middle of the distribution

Standard deviation of the distribution

Degrees of freedom for the t distribution

You learned something really important.

And, there's a standard deviation of the distribution of sample means, standard air.

lets do one more of these.

What we're going to do, is assume that the feature,

This is the formula for the Gaussian again.

looking at what people carry.

And I have to explain to you, the top of this graph shows you that frequency distribution that I talked about.

It's one standard deviation in the distribution of sample means. So this distribution of sample means, again.

Mean value of the normal distribution

Putting it in population parameters, but putting it in sample statistics.

Then we have a Gaussian distribution that is centered around zero, because that's Mu.

And so therefore, their kinetic energy can be characterized by a velocity dispersion.

0 density, 1 distribution

On the periphery, there are residencies and hospitals.

So I can get any value less than 20 given this distribution.

so the center pieces never move.

MV is the linear middle of the distribution.

STD is the standard deviation of the distribution.

Standard deviation of the normal distribution

That's largely influenced by the sample size.