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

Correlation

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.

What about causality?

In MCMC, that's not true.

Throughout this whole thing, making toys,

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.

It's smaller than 0 if the relationship is negative.

So the important topics to take away from this segment.

What about the second assumption?

And that's true of any sample statistic.

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

Though there's plenty who would.

And now the data sits, a square in which data is uniformly arranged, correlated, yes or no?

It's larger than 0. If there's a positive relationship between x and y.

And there are different kinds of relationships between the people.

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

But the question I wanna pose to you. And I, I don't have a slide after this one.

There's no communication.

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

So, final slide, the important topics to take away is...

It's 1. Again, 2 18 gives us 36. Square root is 6.

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

Good relationships, bad relationships, annoying relationships, agnostic relationships, and what I'm going to do is focus on the central piece of an interaction that goes on in a relationship.

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

In summary, you really learned about correlation coefficients.

This is bullshit, come on.

Relationship

Involved?

So Tony starts doing his homework.

Well, what does this have to do with language?

What happened in the Sisyphic condition?

Remember that was just calculating sum of cross products relative to sum of squares and X and Y. So, that's

To sum up this segment, what I want you to take away is just the concept of correlation, what their used for, and how we use scatter plots to evaluate correlations and see where correlations, might be going wrong or where they, where the strength is coming from.

And they can do it because they don't link causation and correlation.

So let's start with segment one.

Most of the examples you'll see in research are Pierson's product mormon correlation.

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

Which I haven't really explained yet.

The universality, to repeat, is us that we are the city.

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.

The last question comes from Katembe.

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