But you married him.

What's so awesome about a queensize futon sale?

So we get no revenue that period

Your half of the 35,000.

But my background is black so I'll use green. So we'll say being in the green.

But total movie revenues across theaters, home video and pay per view are up.

And I won't go into the details of accounting.

Obviously the whole discussion is what happens to this price over a bunch of different scenarios?

So remember, it's normal meaning it's perfectly symmetrical so 50 percent of the distribution falls below the mean, 50 falls above the mean.

But it's sort of, it doesn't have high, as high of a peak.

Notice what happens when I change it.

And it has been a powerful marketplace differentiator, increasing sales and profits.

Sales fell off in the third quarter.

High tax and poor sales bankrupted the company.

So first let's revisit this normal distribution.

Sami stole a wallet and sold it to me.

How much are you producing relative to what you can produce? 100

Let me ask the hard question now.

Dispersion of the normal distribution

We have 14, was the peak in that histogram.

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

Mean of the distribution

Your purse?

Other content markets like book publishing and radio are also up.

Times 2.

We got a good price for that.

How much is this?

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

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

This tells you the height of the normal distribution function.

0 density, 1 distribution

In this scenario, this is the income statement, at least as far as we get to the operating income line.

To maximize revenue. And why? Revenue to the artists?

Here's a 2 dimensional space.

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

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

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

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

Linear middle of the distribution

Standard deviation of the distribution

Degrees of freedom for the t distribution

You learned something really important.

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

We have a weighted mean over here.

There's 500 in there and the wallet's worth more than that.