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

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

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

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

And here's the output from that script.

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

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

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

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

So what you see here is a modest positive correlation.

How do we compute such statistical correlations?

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

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

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

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

Correlation

TNEF Correlation Key

So why shouldn't we say they have the same symmetries?

But just to be clear.

Which I haven't really explained yet.

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

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

level of a group. It does not apply to an individual.

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

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

Because what the data shows, above all else, is one thing, which is that success and likeability are positively correlated for men and negatively correlated for women.

Or we could calculate the Spearman rank correlation, if they're both, ordinal, or rank variables.

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.

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

Though there's plenty who would.

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

So the important topics to take away from this segment.

Throughout this whole thing, making toys,

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

That's a rather negative association.

If, if a, if a measure X doesn't correlate with itself, meaning, it shows low reliability.

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

The tools for solving the problems are knowledge and understanding.

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

There's no communication.

The important topics to take away from segment one is just again, what is a correlation?

Then I would correlations, assuming the null, assuming the null hypothesis, assuming the correlation is zero.

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

Okay, back to the example. Long tangent, sorry.

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