00:01
Again, we have four data points, and we want to figure out the linear regression for the line that best fits through those data points.
00:12
And we have minus five, minus three, minus four, minus two, minus two, minus one, minus one, one.
00:18
So we have n equals four data points.
00:21
Some of the products gives us 15 plus eight plus two minus one or 24.
00:28
Some of the x's is minus 12.
00:32
Some of the y values is minus 5.
00:34
And some of the x squared values gives us 25 plus 16 plus 4 plus 1 or 46.
00:42
So a is n 4 times this summation, 24, minus this times this.
00:53
And divided by n times the sum of the x squared.
00:59
46 minus this term squared.
01:05
And doing out the math, we get that that winds up being nine -tenths, so 0 .9.
01:09
So our slope is 0 .9.
01:12
And we can see that that is indeed what our spreadsheet tells us it should be.
01:17
Now, b is 1 over n times the quantity of the summation of y's, minus um let's see here minus it's got to do that wrong uh did i write that wrong should be a minus so this should be a plus yeah i wrote this should be a minus sign in here a big raising dot minus in there um should that is that right right here um let's see here let's see here this should be this is um is your point yeah that's right and how did i what how did i get this let's see here we got a five yeah this should be a plus is that a plus in the formula no it's not a plus in the formula so how should it be how did we get a plus in here to get this thing let's see here we're what did i do wrong? you'll probably figure it out by now, but i'm still trying to.
02:40
So summation of the y is this.
02:43
A is that.
02:46
An summation of the x is this.
02:50
Oh, maybe it is right.
02:51
Maybe, maybe i'm just not...