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

Treat their measurement with the thermometer as variable x2.

And it's wonderful, wonderful work.

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

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

But just to be clear.

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

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

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,

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

I have mixed feelings about this.

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

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.

In MCMC, that's not true.

Throughout this whole thing, making toys,

What effect size tells you is what was the magnitude of the effect?

Which I haven't really explained yet.

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

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

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

What about the second assumption?

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

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

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

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

Here, I'm mostly just assuming still observational studies.

What about causality?

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

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

So this can be applied to a correlational analysis.

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.

And what you see is a nice positive correlation between runs and batting average.4.

Then we came to the meat of the unit.

In Uganda we see the prevalence went down.

Okay, so to the mediation analysis, as I said, it is typically conductive when we know we have a correlation between x and y.

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

To remind you, this describes the following linear relationship between x and y.

Correlation

TNEF Correlation Key

If you don't get the policy reform and the aid, you don't get the economic recovery, which is the true exit strategy for the peacekeepers.

It is no longer the case.

Now let's look at the relationship between correlation and regression as we make changes to our data.