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?

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,

Though there's plenty who would.

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

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

And that's what effect size means. It's just what's the magnitude of the effect?

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.

So the important topics to take away from this segment.

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

Again, we're not 100 sure that's the right answer, but it looks like a strong correlation.

What about the second assumption?

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

And that's true of any sample statistic.

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

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

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

This is bullshit, come on.

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.

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

But I'm now coming upon ten years at Princeton, and it's sort of a different picture at Princeton.

If you think about this for a moment.

So the higher the value in 1 diagonal and the lower the value in the other diagonal, the stronger the correlation.

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.

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.

There's no communication.

The last question comes from Katembe.

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

Which I haven't really explained yet.

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

To remind you, this describes the following linear relationship between x and y.

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

You could have friendship relationships, sibling relationships, spousal relationships, co worker relationships, neighbor relationships and the like.

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

Relationship

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