It would take us some time for me to walk you through the proof of that, but, so just trust me for now.

But there it is. We did the calculation of.

And we always refer to the rows first and the columns second.

Hi. Welcome back to Statistics One.

And the columns correspond to individual predictors.

Which is like in scalers, taking two times the inverse of two a half, with getting the result one, and here's the identity matrix.

lecture.

And to show you, I'll just sort of give you a glimpse of that.

left is a 4 by 2 matrix number of rows by number of columns.

So, now I have a matrix of deviation scores.

Function MDETERM returns the determinant of a given matrix. The matrix must be of type n x n.

So it turns out that we can multiply two matrices.

That's a fairly common representation for images for computer vision.

Function MMULT multiplies two matrices. Number of columns of the first matrix must be the same as row count of the second one. The result is a matrix.

Wrong number of matrix values.

That completes the dictionary, and now we go on to the loop, which is going to run generations times to change the string into a new string.

And then, remember that to do matrix multiplication, the two matrices have to be conformable.

Well you cross out row 1, column 3.

A has an inverse, exactly what matrices have an inverse is beyond the scope of this

The raw data matrix, the, The subjects and their scores.

That is plus 20 7.

Normal transformation, anisotropic filtering, skinning, lambertian, angle axis, vertex shader. Quaternion, texture map, diffuse, model view matrix, transpose of inverse, bones.

How much total fruit do I have?

I remember this exactly, this calculation, because I just thought wow.

In, in doing examples in'R', So a co relation matrix is a special matrix and the unstandardized version of that is the variance, co variance matrix is of special in statistics.

Number of cols

Number of columns

In other words, the number of columns in this matrix, so it's the number of n columns.

So, let's actually walk through an example.

We did that in descriptic statistics and we just did it by implying basic algebra.

That's how you would solve it in your head.

And to really see that in action you need to understand how the matrix algebra works.

We're just going to have to break out some algebra or let s can do here.

One thing you have to know for multiplication is for, in order to multiply two matrices they have to be what's called conformable.

let me do that in the same colour, we would call it a 3 by 2 matrix, 3 rows, and 2 columns.

Once it's run through the whole length of the string, then I make my string equal to the new string and the loop continues.

You can see I did a, the LM function for Linear Model.

Number of columns

The Fibonacci sequence.

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

This just means my

And the special matrices, like the identity matrix I want to tell you about, in the next and final video now linear algebra review.

So. In the next set of videos what I'm going to do is do a quick review of linear algebra.

And where this is sitting in the playlist you're not expected to know the algebra.

Now let's look at vectors and matrices.