And the null hypothesis just says that there's no effect.

And how they can tell us a lot about what's going on in our data.

And, I'll show you the correlation matrix in a moment.

Correlation

TNEF Correlation Key

I am just gonna pause for a moment to let you think about that.

But there is zero correlation between IQ and emotional empathy, feeling with the other person.

And here's the output from that script.

And looked at the correlations among their scores in those different disciplines.

Is r larger than 0? r 0? Or r smaller than 0?

Though there's plenty who would.

But just to be clear.

So what you see here is a modest positive correlation.

Which I haven't really explained yet.

So, you might think well, this is just like what we did in correlation.

So this can be applied to a correlational analysis.

Or here contributing to the numerator in the formula.

How do we compute such statistical correlations?

So the closer you get to positive one, the stronger you get the negative one, the stronger the correlation.

Then we came to the meat of the unit.

Except now we're dealing with two variables, right?

Not just it coincides with the fact that you die and very frequently people in the public get this wrong.

In the middle of this distribution. What that means is if I did the study over, and over, and over again.

Unfortunately, it's not, because I would like to be taller, but I'm not.

The answer is yes clearly for different values of A, I get different values of B.

In MCMC, that's not true.

Throughout this whole thing, making toys,

The important topics to take away from segment one is just again, what is a correlation?

Well, what does this have to do with language?

What about causality?

Now, let's move to the very different looking wall in the Alhambra.

So, the fact the reliability estimate is this high, I was, I was impressed.

So the important topics to take away from this segment.

So why shouldn't we say they have the same symmetries?

The tools for solving the problems are knowledge and understanding.

And we want to come up with a correlation between coverage on one side, and outcome of the run on the other side.

But again, now that we're in multiple regression, this is not the same as the correlation coefficient.

If someone, for example, shows you the salary of a person and the age of a person, you can say they're really correlated, or you could say they're not correlated at all.

But if I have some degree of co relation, then my predictions get better to the point where if'X' and'Y' are co related at one, then I can get perfectly accurate prediction.

The important topics to take away from this segment are validity and reliability.

Then I would correlations, assuming the null, assuming the null hypothesis, assuming the correlation is zero.

There must be some single factor that caused.

They're all members of the same organization.

And what's the correlation between runs scored and on base percentage?

So, like X and Y. We've done a lot of examples