00:01
So i've entered my data into list one and list two into my ti -ti -84.
00:06
And i have my time as time after 1960.
00:10
So we went zero all the way up to 40.
00:15
Let's see what the highest value was.
00:18
44, 45 was the highest one.
00:22
And then put the concentration of the carbon oxide in here.
00:26
And it asked me to find an exponential regression.
00:28
Now, i'm assuming you can just use your exp reg on this and that you don't have to show that you're finding the log of the list two and then finding a linear relationship between the x variable and the log of that and then converting it.
00:45
So i'm just going to hit on my calculator stat, calculate, and then that exp reg.
00:51
And we do find that the exponential regression has a pretty strong correlation coefficient, and that's actually between x and lod.
00:58
Of y but we have 313 .1596 and then times 1 .00416 and i'm going to stop there to the t and you wanted this value i think to be a c so we'll just do that as like a a c value now um the next part you wanted to determine the doubling time so i want to know how much time does it take for this type of growth to double? so how long does it take for that 1 .00416 to the t to double? and so i can take the log of both sides.
01:42
Doesn't matter whether i take the natural log or the common log, move my exponent down.
01:47
And so the time of doubling is going to be ln of 2 divided by the ln of that 1 .00416.
01:55
And let's see, i'm even going to give you a couple more decimal places.
02:00
And so natural log of two divided by the natural log of 1 .0041645.
02:12
This is very nitpicky with exponential models at times.
02:18
So this is to two decimal places i think you need.
02:21
It will take 166 .79 years is the time.
02:28
So it's going to take a long time.
02:30
And then on part c, you wanted to find what your model is going to predict, what your model is going to predict for 20, 24 and 2024 less than 1960, is going to be after 64 years...