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

Correlation

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.

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

What about causality?

In MCMC, that's not true.

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

He said it was nothing personal.

Though there's plenty who would.

Throughout this whole thing, making toys,

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

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

What about the second assumption?

It's smaller than 0 if the relationship is negative.

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

So the important topics to take away from this segment.

Good relationships, bad relationships, annoying relationships, agnostic relationships, and what I'm going to do is focus on the central piece of an interaction that goes on in a relationship.

There's no communication.

This is bullshit, come on.

And that's true of any sample statistic.

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

Females after their first calf is already weaned a lower load.

Not just it coincides with the fact that you die and very frequently people in the public get this wrong.

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

And they can do it because they don't link causation and correlation.

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

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.

Kids do worse in the more unequal societies.

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.

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

Remember that was just calculating sum of cross products relative to sum of squares and X and Y. So, that's

In summary, you really learned about correlation coefficients.

And here it's really easy to see that there is something wrong, but jumping to an incorrect conclusion about causality when you see a correlation is the most often made logical mistake.

And there are different kinds of relationships between the people.

You could have friendship relationships, sibling relationships, spousal relationships, co worker relationships, neighbor relationships and the like.

Now, if you're playing doubles, then the person on your side of the net is in a non zero sum relationship with you, because every point is either good for both of you positive, win win or bad for both of you, it's lose lose.

So we could say is one correlation significantly larger than another.

Almeida's not working for any of them.

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.

Which I haven't really explained yet.

I suppose I am affiliated...

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

Velocity of train A, and then of course you have the velocity of train B.