Here are my best guesses. Okay?

These are old faculty salaries.

And we can test that later.

And analysts predict it will open

So again, here's the simple regression equation.

We can just conclude that. This more complex model.

Those are the coefficients that go into the regression equation.

That's a bit unusual.

It's structured as an X coordinate, a Y coordinate, and also a heading direction, and these 3 values together comprise a single guess, but a single guess is not a filter.

It turns out, A, it's useful, and for a geek like me, it's fun.

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

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

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 let's see how that works.

That's not so interesting. It's just the predicted score on Y, when both X's are zero.

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

Motion comes in, 3.66. Uncertainty goes up.

I got that. From this piece of the output.

Forecast

Kidron's population's just over 30,000.

This should be obvious by now, the predicted value on Y, the predicted value on Y when all the predictors are zero and so on.

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.

First, I want you to know if I make this modification what is the effect on x2?

If you just do this, then prediction becomes this 4 by 1 dimensional vector, on the right, that just gives you all the predicted prices.

The arrow calculation, the matrix S with a transpose, the Kalman gain K with the inverse, back to my next prediction and my measurement update, and this is the prediction step.

If they understand which way, say, a blown tire affects their dynamics. There has been a lot of work on this.

Global projections are pretty staggering.

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

So the intercept is 33.16.

It's 2.10x10²⁰, and it's a typical situation in statistics.

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

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

We move 2 to the right, measure 9, 1 to the right, measure 10, and move 1 again.

But basically, the predicted score equals 46,911, that's the regression constant.

Time, publications, and gender.

The regression coefficient again it's always just for a one unit increase in x it's the predicted change in Y.

XTide Tide Predictor

Monthly estimates

I can then do, model comparisons.

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

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

We built engines so we built 100 different engines over the last two years.

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

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