FDlC insured.

He's insured.

He insured himself.

My house is fully insured.

He insured his house against fire.

I am insured for the car.

The house is insured against fire.

What time's the earthquake? You're insured.

It's dangerous. Besides, I ain't insured.

MD5 Sums Equal

MD5 Sums Differ

Calculate simple sums

He insured himself for a rainy day.

He insured his new house against fire.

The freight was insured against all risks.

Your money is insured by the government.

Sums of the squares for the mo del and sums of squares residual.

What's in the denominator? Sums of squares X, and sums of squares Y.

These fragile items must be insured against all risks.

That's the residual sums of squares.

It sums up the situation pretty well.

Why don't you go over your sums?

I'm adding up the little x sums.

That about sums it up for me.

And I know that, because of those individuals who are not insured, it drives up the cost for other individuals, who are insured, to pay their rates.

Well, they're BB, but you're being insured by someone who is AA.

looking for 20 year term life insurance and they insured all of them.

You also compute sums over all rows as well as sums over all columns as well as the total sum of events.

So, the sum A comma 2 and this sums up each row of A and each row of A also sums up to 369.

The correlation coefficient is the sum of cross products over the square root of sums of squares for x times sums of squares for y.

He doesn't have enough brains to do sums in his head.

He doesn't have enough brains to do sums in his head.

He doesn't have enough brains to do sums in his head.

So you do the sums, and you get something like that.

So it sums up a lot of what we're talking about.

And this graph sort of sums up what the problem is.

But what doesn't usually come to light is how shell companies are used to steal huge sums of money, transformational sums of money, from poor countries.

So again, minimize sums of squares residuals, just like in simple regression.

And what that'll do, it will give me row vector of sums.

What you see is the residual sums of squares goes down, alright?

And you're learning about, I don't know, the sums of infinite series.

Because it sums. All the scores together, right? Every score gets to contribute.

You sum up the infinite sums of those little dA's over the region.

We can also refer to that as sums of squares for the model.

That's sums of squares. Divide that by n and that gave us variance.