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

For this quiz we will assume a degenerative case of 3 data points and just 1 cluster center.

The result is generally poor.

So first let's revisit this normal distribution.

Dispersion of the normal distribution

Mean of the distribution

Your purse?

Measurement Update So question 1 is measurement update.

So suppose we know we are looking at faces.

laugh Um, I enjoy wine tasting.

We go down to two, which is a one above zero and the two on the left, all the way back to one.

And so this is my introduce concept of biasing to allow for a possibility that

This tells you the height of the normal distribution function.

And again remember, there are a lot of assumptions we should be testing here.

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.

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

So, remember in the last lecture. You

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

Middle value in 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

That's the point.

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

MV is the linear middle of the distribution.

STD is the standard deviation of the distribution.

Standard deviation of the normal distribution

Ah, but I'm plotting them all on one slide so that we can pair, can compare what these different distributions look like.

That's largely influenced by the sample size.

So, it's um, it tries to smoothen to that, that rectangle, right? Um, but it still is a rectangle.

In this video, I'd like to talk about the Gaussian distribution, which is also called the normal distribution.

If you remember from lecture two when we started to talk about probability, we can use the normal distribution.

larger triangle right up there

Intuitively speaking, this is the case because we actually gain information.

She'll need to go through and check the frequency distribution of different intervals.