But so, can anyone guess what's going on here?

Nothing ever happens out of thin air.

What about causality?

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

And Ha ha, there is a picture and here is I. And what does it matter?

The problem is that these observational studies.

And then the idea of confounds. And the most important concept to take away from this segment is the idea of causality.

I'm not a good person to get involved with.

Random representative sampling, independent and dependent variables, double blind, single blind experiments, confounds and causality.

That we could create a classification of the mental illnesses.

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

And in fact, the sickness did cause you to die, and also effected your decision of whether you go to a hospital or not.

The why meant really,

And its solution isn't a number, its solution is a function, or a set of functions, or a class of functions.

The left hand always is the same and puts sinusoidal tickle.

Correlation

Indeed, some writers do not think the relation of brain to consciousness is a causal relation in the first place.

We're still going to have an intercept, that's still the predicted score on Y when the X's are zero.

Because unlike polio, we couldn't get right to the heart of the matter.

If this sounds complicated, we haven't seen much.

This is supposed to be a constant

The data came back from the United States, from England, from Sweden, from Canada, and it was all the same no correlation, no connection, none at all.

One great thing about Bayes nets is that we're not restricted to going only in one direction.

Okay.

I think we can presume that their relationship had soured somewhat.

So I can just rewrite that as A so now A is not a function anymore, it's just a number A times the second derivative of y, with respect to x, plus B times the first derivative, plus C times y.

And that's one of the examples I'll show you when we do this in R.

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.

2nd derivative r squared.

And then, we can have multiple predictors and multiple regression coefficients.

And the older you are the more years you have to be involved in active excercise.

Cause of death multiple gunshots. Any one of which could have been fatal.

And that's true of any sample statistic.

You would think later that the ball is the cause for the window to break.

And of course, others using more sophisticated methods in the literature have controlled for poverty and education and so on.

And what I want for today is for you to remember that the next time someone wants to prove that there is a causal relation between something and something else it can be anything, it can be vaccines and autism, it can be female bankers and the financial crisis, and if they (Laughter) point out to you that there is a very strong relation, remember that it's not enough to have a correlation.

So the important topics to take away from this segment.

It had to do with what caused all this.

I, I'm gonna start drawing these path models in the next segment with, with arrows that typically depict causality.

A simple event would be an event that is associated with only 1 outcome.

What I discovered is that teens who regularly watch Gangsta rap videos are 3 times more likely to hit a teacher 2 times more likely to have multiple sex partners and 2.5 times more likely to get arrested.

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

So if we were to multiply three times negative two, it shouldn't matter.

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

Now, we're about to publish the first study