For example, factoring quadratics with

Maximum Transmission Unit

Modulus

And that's true of any sample statistic.

Holy Jesus. Torque.

Correlation

Extinction

Cooling factor

Number of fixed decimal digits

Number of fixed decimal digits.

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.

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.

The inverse Laplace transform of this thing is going to be equal to we can just write the 2 there as a scaling factor, 2 there times this thing times the unit step function.

This is supposed to be a constant

At this point, forget about that there's even a rotation matrix.

Cycle seconds

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.

2nd derivative r squared.

Now it's about eighteen.

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.

And you get six zero zero.

They just link them together.

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.

How strong is the relationship?

But again, now that we're in multiple regression, this is not the same as the correlation coefficient.

They're on the same side of the equation.

So let's multiply both sides by negative 3, right?

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

I think you see what I did.

And if we also throw out the requirement that A and B have to be adjacent, then we get the number of misplaced tiles heuristic.

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.

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

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

But this language allows us to say that they are representations of the same symmetrical abstract object, which we call 4 4 2.

Low error factor

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.