That's a bit unusual.

These are old faculty salaries.

And in this example, I wanna use working memory capacity to predict, SAT.

We can just conclude that. This more complex model.

I am just going to show you two. So we have, our first one is uh, we can predict why from the mediator variable.

So, in the last lecture we talked about simple regression.

Before, when we just did simple regression, that was our equation.

I'll use'X' and'Y' as I've been for the predictor and outcome respectively.

And we're looking at physical endurance in this, in this group of people.

And what is interesting about these situations, is the unpredictability of them.

Forecast

I can then do, model comparisons.

And then, we'll do multiple regression. And we're not gonna get crazy, we're just gonna put two predictors in.

It gets even easier if we want to look at the standardized regression coefficient.

That would be the x is here, it would the input features.

It goes from two forty down to two hundred.

So, observe variables, and some people will call these manifest variables are represented by rectangles.

It is important that we make plans in relation to anticipated changes.

Just two simple regressions and model three is the multiple regression with both predictors in there.

There's also a measurement update step where we use the measurement z.

So, again the rows correspond to individual subjects, or cases.

Try to feel his next movement.

Take upstairs.

We're expecting resistance

We just came up with a better predictor. But we could do other things.

Those are the coefficients that go into the regression equation.

And again, the take home message from this.

So first what we're gonna do is just. Run a simple regression.

I'm going to go back to this example we used in mediation.

So first, let's talk about just what regression is.

We need to delve into this mass of anatomy that we've been compiling to understand where the changes are occurring and what this means.

That has to be for us to just do mediation analysis in the first place.

Do research where two models are competing against one another.

Have you anydea how he intends to do that?

XTide Tide Predictor

Monthly estimates

Again, I'm using Y as the outcome variable.

So keep it simple. There's just four variables.

What is your model getting wrong?

They'll say, I think this company is not going to change.

This is the range that would be expected over the Iast 60 years, but the scientists who specialize in global warming have computer models that long ago predicted this range of temperature increase.

However many people we have on our study, or however many cases, that's how many predicted scores we have.

What we're doing now is we're going to add in as many predictors as we like.

But now imagine, what if we had not only the size of the house as a feature or as a variable of which to try to predict the price, but that we also knew the number of bedrooms, the number of house and the age of the home and years.

You can read the weather, you can predict the price of heating oil.