00:01
So we're looking at the length of deer mice, and this is a normal distribution with a mean of 83 mm and a standard deviation of 7.
00:11
And we're looking at samples of 25.
00:15
So i'm going to refer to the central limit theorem, which states that as sample size increases, sample means become more and more normally distributed compared to the population.
00:26
Since we have a normal population here, and you can't get more normal than normal, if i took every sample of this size, took the sample means and plotted them out, i would just get another normal distribution.
00:39
The mean of the means is the same as the population mean, so 83.
00:45
The standard deviation of the sample means, or standard error, is sigma over root n.
00:53
So 7 over 5 is 1 .4.
00:55
So i have the original distribution and the sampling distribution, which is narrower.
01:06
So there's a depiction of them.
01:09
Part c.
01:12
We want to look at a length, so that 10 % of all deer mice have a length below this.
01:18
So we're looking at individuals, and there's going to be some value where 10 % of them are below that.
01:24
So that's the 10th percentile for the population.
01:28
To find this, you need something with the normal distribution built into it.
01:34
That could be software like excel.
01:36
I'm going to use my ti -84 calculator with the inverse normal function.
01:43
It has three inputs.
01:45
Area, mean, standard deviation.
01:47
I put in the cumulative area, so here that would be 10%, or 0 .1, with the mean and standard deviation.
01:53
We're looking at all mice, so i put 7 there.
01:58
And that gives me 74 .03, to two decimal places.
02:03
That's in millimetres.
02:05
Part 4...