00:01
In this video, we're going to look at logarithmic transformations and the power law model.
00:06
We can use a logarithmic transformation to determine if the power law is a good, the power law model is actually a good fit for our data.
00:15
Now, the power law formula is right here.
00:20
Y equals alpha -x to the beta.
00:23
And in our particular example, x represents the time in microseconds, and y equals the amps built up.
00:32
The circuit at time x.
00:35
So here are x's and here's our y.
00:37
First thing we're asked to do is we're asked to find x prime and y prime.
00:41
These are our transformations.
00:42
So x prime will be log of x and y prime will be log of y.
00:46
And then we're asked to graph.
00:51
Actually we're asked to find the regression model for the graph of x prime y prime.
01:05
So i would find log of 2.
01:07
This would be my first x and then i'm going around it so log of two again on the calculator log two and then i would also need to find log 1 .81 and i can do that for each of my values and then i would graph that information but there's actually a faster way to do it so i'll go back to my calculator but this time i'm going to go to my lists so stat number one edit and notice i already have the information in l1 and l2.
01:58
What i'm going to do on l3 and l4 is figure out the logs of those values.
02:04
So i'm going to type log 2, so log of my original x value, and press enter, and you can see this automatically calculates it for me.
02:14
So i don't have to go through and do it all by hand.
02:18
Log 4, log 6, log 8, log 10.
02:26
And remember the log of 10 is 1 because those are inverses.
02:30
All right, i'm going to pause the video and then i'm going to enter in the logs of the y values.
02:37
And when i come back or in a second, you will see the results of that.
02:44
So now i have the log of my y, each y value entered into l4.
02:49
So to find the regression model, select stat, cursor over to calc, choose number 8, the linear regression model in the form of a plus bx.
02:57
But i don't want l1, l2, i want l3 and l4.
03:01
So second three will put l3 under my x list.
03:05
Second four puts l4 under my y list, cursor down to calculate.
03:11
And now i have the x value, or the a value, the b value, and my correlation coefficient.
03:17
So a is 0 .128, b is 0 .492, depending again on how you need to round.
03:22
And r is 0 .987.
03:25
So my model will be y prime is approximately 0 .128 plus 0 .492x prime.
03:40
So there's my linear regression model...