[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.