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

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

Forecast

You and I and the 2130 are all blameless, doctor.

Monthly estimates

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

These are old faculty salaries.

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

That's really the difficult step in Kalman filtering.

You are comparing it to a word you just heard.

And the reason that we need to use science is because then we can reproduce what we're doing it's not just wisdom or guesswork.

Narrator So, in this class you will learn about particle filters.

And then I'll observe again the 2.

Here are my best guesses. Okay?

Try to feel his next movement.

We can just conclude that. This more complex model.

XTide Tide Predictor

(Laughter)

You obviously do what the precautionary principle suggests, you try to anticipate it, but after anticipating it, you constantly asses it, not just once, but eternally.

And today, I'd like to introduce you to multiple regression.

Do research where two models are competing against one another.

We actually think the future's going to be event driven.

So this happens in physics all the time.

What's our time frame?

I don't know what you do.

Anticipation.

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

That's a bit unusual.

And again, the take home message from this.

No. It's difficult.

Here is the demand forecast for 1998.

But most of the world in the middle are pushing forwards very fast.

So this scenario makes predictions.

There's different things you could try to do with machine learning technique.

And analysts predict it will open

I can then do, model comparisons.

An amount with which the precision of a measurement can be estimated.

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Take upstairs.

We're expecting resistance

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

There's a new covariance matrix, and for the third observation followed by the prediction, the prediction is correctly effectively 4, 3.999.

And we can test that later.

I'm right again. Nobody could predict that Dr. Grant would suddenly jump out of a moving vehicle.

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