But the average is about 50 years old in this sample.

Now looking at this same data, I'd like to ask you a different question.

My question for you is when you graph this is the data linear.

They both display a negatively sloping somewhat linear relationship.

Suppose we draw a line at a 45 degree angle,

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

Here.

Rags.

A wallet.

Purse?

My wallet.

Distribution Lists

Distribution List

Scatter Chart

Stock Chart

Gantt Chart

If we can, it isn't typhoid.

My wallet.

Cover him.

Here you go. Sleep well.

Your wallet...

We'll need some more blankets.

A blanket.

Is that your wallet?

That's actually what I do in the other video, just to approximate the area under the curve and give you a good sense that the normal distribution is what the binomial distribution becomes essentially, if you have many trials.

We have x plus 6, and I'm going to distribute this negative 1.

In statistics, pi is used in the equation to calculate the area under a normal distribution curve, which comes in handy for figuring out distributions of standardized test scores, financial models, or margins of error in scientific results.

A 2 dimensional gaussian is defined over that space, and it's possible to draw the contour lines of the gaussian.

My blanket.

Blanket...

Piella's purse.

So what the cumulative distribution function is essentially let me call it the cumulative distribution function it's a function of x.

No, not a thing.

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

Here's my wallet.

Give me back my wallet.

Mean of the distribution

I'll make it come true.

What is it?

That's it.

Oh, that's the storeroom for canvas and Dacron and And line.

Oh Ha Ni!

Just this one suitcase. His wallet?

The woman's handbag.

Throw me the keys.