What about causality?

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.

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

Though there's plenty who would.

In MCMC, that's not true.

Throughout this whole thing, making toys,

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

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.

And here it's really easy to see that there is something wrong, but jumping to an incorrect conclusion about causality when you see a correlation is the most often made logical mistake.

So the important topics to take away from this segment.

There's no communication.

Makes no difference.

This is bullshit, come on.

What about the second assumption?

And that's true of any sample statistic.

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

Prove she wasn't involved.

Yes. But no. For the moment, it's all business.

International Affairs course is going to be my fourth period class for sure!

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

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

And there are different kinds of relationships between the people.

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.

So again remember the idea of mediation.

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

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.

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...

But let's look at on base percentage.

I suppose I am affiliated...

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

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

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

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.

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

He was actually, uh, caught and held prisoner for a while.

I don't wanna get into this now.

Whatever. Whatever.

Well, I don't care