My hump contains up to 80 lbs. of fat, but doesn't store H2O.

It senses what I want to do, where I want to go, and then augments my strength and endurance.

And what we see for age is the opposite.

We'll see when we look at the descriptive statistics.

But it is negative. So as people age, their physical endurance scores drop.

He say you Blade Runner.

And I call that active years.

The first thing I'm gonna do in this script is look at some scatter plots.

Allowed us to do is move from unstandardized to standardized regression coefficients.

God, just because you lost your hardon doesn't mean you have to have a fucking meltdown.

So for an average amount of active years. And that's why it got a little steeper and shallower.

I am a runner.

But if your engaged in physical activity for more years.

So sort of an arbitrary scale.

And there're only three variables in there.

He is a fast runner.

So this is now the slope of endurance relating, excuse me, the slope of active years relating to endurance for someone of an average age in this sample.

If you have more than one predictor in the model, that won't hold. But with one it is.

So the model r squared is the same as in the last segment.

So this first scatter plot is looking at endurance, related to age.

What would you say?

Artwork for runners and default theme

I'm Deckard, Blade Runner. 26354. I'm filed and monitored.

So now the regression constant is zero.

That's, important to remember. So, now let's do the simple regression using active years to predict endurance.

So, I, I like this example. And it's pretty intuitive.

Okay but we're gonna skip all that today and just jump into the scatter parts.

Well think about the example.

Tony is a fast runner.

I am the fastest runner.

She's a runner like me.

Is negative, but it's very slight. It doesn't have a very, steep slope.

So if we look at this scatter plot, we see a positive slope, positive regression line, and it's a bit steeper than the previous function.

Martin was always a great runner.

Again, that's .13.

Naoko is a fast runner.

This is Kawu, our fastest runner.

Here comes the second runner.

Strong.

The runners rounded the corner into the homestretch.

Miss Kanda is a very fast runner.

Masataka Gouda held 3 title terms.

Is that. Putting these two, predictors together.

So, 33.16 is just the intercept in this analysis and'negative point 13' is the slope of the line relating age to endurance.

Why is it that women get stronger as distances get longer?