0:00:00.370,0:00:03.430 A very good question you might be asking at this point is how to 0:00:03.430,0:00:05.960 evaluate your linear regression. 0:00:05.960,0:00:09.650 One of the obvious things that you can do is visualize the regression on top of 0:00:09.650,0:00:11.340 your scatter points. 0:00:11.340,0:00:14.070 Another thing you can do is you can look at the errors that your 0:00:14.070,0:00:15.390 linear regression makes. 0:00:16.480,0:00:19.340 By errors, I don't mean mistakes, not exactly. 0:00:20.370,0:00:23.170 What I mean by errors is something like this. 0:00:23.170,0:00:26.150 Suppose I have my age and net worth data. 0:00:26.150,0:00:33.040 And it will be following along in some pattern, although not perfectly linear. 0:00:34.200,0:00:35.836 I fit my line to it. 0:00:35.836,0:00:38.909 And this fit is going to have associated errors. 0:00:40.620,0:00:44.750 In this context, errors is a technical term and that's the difference in 0:00:44.750,0:00:49.630 this example between the actual net worth of a particular person and 0:00:49.630,0:00:52.650 the net worth that's predicted by our regression line.