00:01
So here for this linear regression problem, we have the data.
00:05
We have the response variable n and the predictor t.
00:12
So we can use the list squares to get the estimates of the parameters.
00:18
We have one intercept.
00:19
We have the slope here.
00:22
So this from the list squares, suppose we have a matrix.
00:31
So the first column is 1 -1 .1.
00:35
And the second will be the predictor values.
00:40
For example, we have 5, 16, 17, blah, blah, blah, plus 1 as 17 as well.
00:47
So we have this matrix.
00:54
And then we have the observed vector y.
00:59
We have the nicotine liquiding amount .4, 1 .2, blah, blah, blah.
01:07
Last one is, let me have a look...