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

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

What about the second assumption?

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

Correlation

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

What about causality?

Throughout this whole thing, making toys,

The polar line of a point with respect to a conic.

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

So the important topics to take away from this segment.

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

The polar point of a line with respect to a conic.

A perfectly positive linear relationship would look something like this, but this and this are also positive relationships, whereas a negative linear relationship would look like this, but this and this are also negative relationships.

In MCMC, that's not true.

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?

Construct a polar line wrt. this conic

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

And that's true of any sample statistic.

Though there's plenty who would.

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

There's no communication.

Turn to the left without regard to the signal.

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

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

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.

So let's start with segment one.

And there are different kinds of relationships between the people.

Which I haven't really explained yet.

So Tony starts doing his homework.

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

In summary, you really learned about correlation coefficients.

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

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.

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

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.

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

The last question comes from Katembe.

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

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

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