Full metal jacket.

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You also have a rotation matrix that will rotate the model or anything else around the y axis.

This just means my

And so on this slide, you know this matrix is the matrix A, and this matrix is the matrix A inverse.

So for each predictor, we're gonna have one beta or one regression coefficient.

I got a 2 by 2 matrix.

Matrix

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learned from Wikipedia, the correct term is the adjugate of matrix a.

I think you see the pattern.

So the inverse of matrix a is equal to 1 over the determinant of a times the adjugate, or adjoint, of matrix a.

So the matrix A is a m by n dimensional matrix, so m rows and n columns and we are going to multiply that by a n by 1 matrix, in other words an n dimensional vector.

So A is a three by two matrix and features

Could I have multiplied the matrices the other way?

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All right.

Take us in. Full impulse.

I'm sorry, the correct answer is Full Metal Jacket .

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So let me write that down.

It's going to be 2, 1, 1, 1.

It's just a little tedious.

It's the determinant.

So there you have it.

We are almost done.

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Dilithium chambers at maximum, Captain.

So B inverse is going to be equal to 1 over the determinant of B.

The inverse is shown like this a negative 1 in the superscript.

Let's calculate B inverse.

We go back to plus.

Matrix

matrix

Let me just quickly show you an example.

In order to get, what

By train?

What's the line for?

Just to say that again.

If you solve the problem, you have probably something like this.

And actually this step is fairly straightforward.

The answer, I think, is that they used every tool in the toolbox.