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

But just to be clear.

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

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

How do we compute such statistical correlations?

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

Well, what does this have to do with language?

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

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

Then we came to the meat of the unit.

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

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

And it's wonderful, wonderful work.

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

Correlation

TNEF Correlation Key

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

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.

Treat their measurement with the thermometer as variable x2.

And here's the output from that script.

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

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

That too will depend on the validity of our measures and again in, in lecture five we'll talk about validity in more detail.

The general expectation is that clustering has to get stronger in time or conversely, it'll be weaker at higher redshifts.

And then they did a re test say months later or even a year later.

Though there's plenty who would.

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

A way to quantify this is to follow the 2 point correlation function.

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

So what you see here is a modest positive correlation.

What about causality?

That is, to send more winged queens and males to that year's mating flight.

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

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.

There must be some single factor that caused.

In Uganda we see the prevalence went down.

Which I haven't really explained yet.

So let's start with segment one.

So, like X and Y. We've done a lot of examples

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

But if you don't talk, you can't engage the other side.

So this can be applied to a correlational analysis.

Or here contributing to the numerator in the formula.

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