Superconducting?

So I can just rewrite that as A so now A is not a function anymore, it's just a number A times the second derivative of y, with respect to x, plus B times the first derivative, plus C times y.

Modulus

This is supposed to be a constant

2nd derivative r squared.

And that's true of any sample statistic.

So there's a fair amount of interconnection between a node's neighbors.

So just getting it correct to a couple of digits is probably sufficient.

Correlation

Extinction

Cooling factor

Normal Binomial

Displaystyle Binomial

Textstyle Binomial

So we'd like to have a way of getting a pretty good answer in time a lot less than this.

Now it's about eighteen.

AMS Binomial Expression

Scale by factor

n Choose m

Next I want to focus for a little while on the notion of a clustering coefficient.

I think you see what I did.

Let's just solve x squared minus x minus thirty is equal to zero.

It's a power symbol.

They're on the same side of the equation.

We'll come back to that again in multiple regression, and in lecture nine in R. And then, the importance of this distinction between unstandardi zed and standardized regression coefficients.

You literally just look at this coefficient right here, and you say, OK, well what's half of that coefficient?

So the important topics to take away from this segment.

When we complete the square, we just take half of this coefficient.

The source of my power.

Power Ballad

looks like a squared.

So, I just said and look, the a is just the coefficient on the x term, right? a is the coefficient on the x squared term. b is the coefficient on the x term, and c is the constant.

I am going to say that the clustering coefficient for graph is just the average of the clustering coefficients of the nodes in the graph.

And let's see whether this is equal to 0.

So a plus b will be equal to the middle coefficient.

We have x of 1, 2, and 3 and y of 4, 7, and 13.

Let's do that.

How is this the same thing as this?

And then, we can have multiple predictors and multiple regression coefficients.

We just have to multiply both sides times the reciprocal of the coefficient on the left hand side.

So in this example what's A?

Routing power.

And what makes it linear?

The unstandardized regression coefficient, B.

Anything where there's five.