00:01
Okay, so to see which one of these represents the line of best fit, what we need to do is look at the scatterplot that we've got and see where the line goes.
00:10
So for example, for y equals x minus 2, that's going to be a line which, if we had four quadrants, goes across one as it goes up one because its gradient is 1, but it crosses the y -axis at minus 2.
00:34
So we'd have that the line goes something like this.
00:41
Now obviously it might not be the whole section of this.
00:43
It might start, for example, it might just be that your data starts from x equals 4 and it goes up to say x equals 8.
00:56
So you might find that it's just this red section of the graph.
01:04
But all you need to do is to check if it's that one is to look at the, so we say this is a point on the graph, we can see that x equals 4 and y equals 2 and that is indeed x minus 2.
01:21
So you just need to make sure that all the points on your line of best fit satisfy y equals x minus 2 and it would look something like this.
01:29
If it was y equals 0 .6x plus 1 then instead it's going to cross the y axis at 1 here and the gradient this time is going to be 0 .6 .6 so that means for every 1 it goes across, it goes 0 .6 up.
01:55
So it's going to cross the x -axis at minus 5 over 3.
02:03
So this is y -equals 1.
02:07
And so you just need to check that any points that are in your dataset lie on this graph.
02:14
So you just put in the y -cordinate of the point, put in the x -cordinary, and check if this holds.
02:21
If it was going to be y -equals 0 .5x plus 2, that is going to cross the y -axis at plus two and have a 0 .5 gradient, which means that for every one it goes across, it goes a half up.
02:43
So for every two goes across, it goes one up.
02:46
So to get from here, that will be the next point...