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,

What about the second assumption?

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

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

So the important topics to take away from this segment.

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.

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

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

And that's true of any sample statistic.

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

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

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.

This is bullshit, come on.

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

Well, what does this have to do with language?

What happened in the Sisyphic condition?

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

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

Is there any correlation, positive or negative, between intelligence and tendency to be religious?

So let's start with segment one.

In summary, you really learned about correlation coefficients.

And there are different kinds of relationships between the people.

How do we compute such statistical correlations?

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

There's no communication.

It's going to depend whether they're inner city or rural.

The last question comes from Katembe.

In Uganda we see the prevalence went down.

So, if I go to another sample. And measure the same two variables.

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

That too will depend on the validity of our measures and again in, in lecture five we'll talk about validity in more detail.

Which I haven't really explained yet.

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

We forgot about the entire chain around us.