[MUSIC]
Okay, so that represented a kind of high level overview about this module,
as well as, other aspects that we're going to touch upon in this course.
But now let's delve into a specific case of simple linear regression and
talk about what this means.
So going back to our flowchart,
what we're gonna talk about now is specifically the machine learning model.
So that's that highlighted green box and everything else is grayed out so
you can forget about everything else for now.
We're just talking about our model and what form it takes.
So our simple linear regression model is just that.
It's very simple.
We're assuming we have just one input,
which in this case is, square feet of the house and
one output which is the house sales price and we're just gonna fit a line,.
A very simple function here not that quadratic function or
higher order polynomials we talked about before, just a very simple line.
And what's the equation of a line?
Well, it's just intercept plus slope times our variable of interest so
that we're gonna say that's wo + w1x.
And what this regression model then specifies is that each one of our
observations yi is simply that function evaluated at xi.
So that's w0 plus w1xI plus the error term which we called epsilon i.
So this is our regression model, and to be clear, this error, epsilon i,
is the distance from our specific observation back down to the line.
Okay, so the parameters of this model Are w0 and
w1 are intercept and slope and we call these the regression coefficients.
So that summarizes our simple linear regression model.
Very straight forward.
Very simple.
But we'll get to more complicated things later.