Treat their measurement with the thermometer as variable x2.

As you see, there's a high correlation between temperature, CO2 and sea level.

And here's what you need to know.

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

Is r larger than 0? r 0? Or r smaller than 0?

But just to be clear.

Correlation

TNEF Correlation Key

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

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.

And looked at the correlations among their scores in those different disciplines.

I could then run a correlation analysis on subset A and subset B.

So what you see here is a modest positive correlation.

He is well advanced in life.

Slight high correlation, or puh, puh, sorry, positive correlation for high SES.

And the null hypothesis just says that there's no effect.

How much did they believe in the paranormal, supernatural, angels and so forth.

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

Though there's plenty who would.

So this can be applied to a correlational analysis.

Or here contributing to the numerator in the formula.

I work in old age homes, largely.

Which I haven't really explained yet.

And here's the output from that script.

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

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

In MCMC, that's not true.

Throughout this whole thing, making toys,

Just run the correlation between X1 and X2, and that provides you with a reliability estimate.

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.

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.

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

This is, again, keep it simple, stupid.

Except now we're dealing with two variables, right?

If you think about this for a moment.

What about causality?

He's better off working in Tokyo than hiding here.

So, the fact the reliability estimate is this high, I was, I was impressed.

So the important topics to take away from this segment.

The last question comes from Katembe.

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

It doesn't mean that everyone is right on the regression line.