vimeo.com/.../430695849

Next thing we have to do
is to take our acts.

For each x value,
we have to subtract the mean of x,

and then square it and sum,
and we will have this 'get_ssxx',

which is the sum of squared x.
What this will do here

is to take the mean of x
using numpy and will say x minus the mean.

So it will be element wise to
each value of x in the list.

It will have the mean taken away,
then we're going to square it

and we're going to sum the whole thing
and will set a new attribute 'self.ssxx'.

And so if we make an instance of
linear regression,

pass in x and y and then
we call this method,

we will see we can now
access this attribute 'ssxx'.

The next thing we will do is
we will get some of the squares

using x and y,
and what we will do here is

take x minus the mean of x,
like we did up here.

Now we're going to take
y minus the mean of y.

And then we're going to
elementwise multiply them,

meaning this right here will be
a collection of data.

So we're going to multiply
the index position item zero.

by the index position item of zero
in this collection of data,

and do that for each element
in these collections of data,

and these containers,
and then will sum them up.

That will give us
the sum of squares of x and y.

So now,
if we run this...

...you'll see that we now have
bound a value to 'ssxy'.