Then we came to the meat of the unit.

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

Which I haven't really explained yet.

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

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

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,

And it's wonderful, wonderful work.

Statistics one. In the last lecture, we talked about correlations.

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

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

Treat their measurement with the thermometer as variable x2.

So, you might think well, this is just like what we did in correlation.

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

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

And what's the correlation between runs scored and on base percentage?

And here's the output from that script.

And now, to sort of revisit the concept or the logic behind null hypothesis significance testing.

And, if X and Y overlap, if they co vary.

Now, let's move to the very different looking wall in the Alhambra.

What about causality?

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

So this can be applied to a correlational analysis.

Our plan now is we compute the Phi correlation for each line, and then we rank the lines from high correlation to low correlation.

But just to be clear.

So one one one, multiply that by r, then you just get r. That's why, the standardized regression c oefficient is so easy.

Correlations can range from positive one, which is a perfect positive correlation.

And this network of relations is ever evolving.

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

Correlation

TNEF Correlation Key

How do we compute such statistical correlations?

What about the second assumption?

The reason is we know that r is between 1 and 1 so it can't be 3 or 2.

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

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

We forgot about the entire chain around us.

And then in the second segment we'll take a more mathematical approach.

If you look at that data between axes x and y,

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

Here, we will take a more conceptual approach to correlation and talk about how to interpret correlations?

And in lecture five, we'll talk a lot about assessment of reliability.

That's a rather negative association.

In the prediction step, I don't know my velocity, so I can't possibly predict for location. I'm going to assume.