And that's why the dot product is also called the projection product,

because it takes the projection of one vector onto another.

We just have to divide by the length of R,

and if R happened to be a unit vector or one of

the vectors we used to find the space of length one,

then that would be of length one and our dot S would just be the scalar projection of

S onto that all that vector defining the axes or whatever it was.

Now, if I want to remember to encode something about R,

which way R was going into the dot product or into the project book product

could define something called the vector projection.

And that's defined to be R.S over mode R dotted with itself.

So R.R mode R squared,

that's R.S over R.R if you like because mode R squared is equal to R.R.

And we multiply that by the vector R itself.

So that is that's dot products just a number,

these sizes are just a number,

and R itself is a vector.

So what we've done here is we've taken the scalar projection R.S over R,

this guy that's how much S goes along R,

and we've multiplied it by R divided by its length.

So we've multiplied it by a vector going the direction

of R but it's been normalized to have a length one.

So that vector projection is a number,

times a unit vector that goes the direction

of R. So if R say was was some number of lengths,

the vector that will be R divided by its size,

say if that was a unit length vector I've just drawn there,

and the vector projection would be that number S.R, that adjacent side,

times a vector going in the unit length of R. So that's,

if you like the scalar projection also encoded with something about the direction of R,

just a unit vector going in the direction of

R. So we've defined a scalar projection here,

and we've defined a vector projection there.

So good job. This was really the cool video for this week,

we've done some real work here.

We found the size of a vector and we defined the dot projection product.

We've then found out some mathematical operations we can do with the dot product.

This distributes over vector addition and

associative with scalar multiplication and that its commutitive.

We then found that it finds the angle between two vectors,

the extent to which they go in the same direction,

or then it finds the projection of one vector onto another.

That's kind of how one vector will collapse onto another,

which is what we'll explore in the next two videos.

So, good work now's a good time to pause,

and try some examples,

but put all this together and give it all a workout on a bit of a try before we move on.