We can just conclude that. This more complex model.

Forecast

I can then do, model comparisons.

These are old faculty salaries.

XTide Tide Predictor

Monthly estimates

Hand to mouth is 2.6 trillion.

But here, the Price to Earnings for 2009 then becomes let's see.

Are those the earnings that you're predicting for next year?

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.

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

And then I'll observe again the 2.

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Here are my best guesses. Okay?

Try to feel his next movement.

Take upstairs.

We're expecting resistance

And again, the take home message from this.

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

Your income is about twice as large as mine is.

That's a bit unusual.

Do research where two models are competing against one another.

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

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

So that's a toggler.

You can follow it through the fossil record.

And they estimated that in 2008,

And who knows?

Have you anydea how he intends to do that?

You won't see the next one coming either.

He borrowed a large sum of money on the strength of expected income.

Your income is about twice as large as mine.

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

His income is larger than that of his wife.

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.

Bush count 'em again ... You can violate your expectations about experience.

This should be very familiar to you.

And there's another example.