00:01
All right, so we have here two frequency histograms, and we want to estimate the mean and the median of each.
00:12
So let's start with the median because it's easier.
00:17
Okay, so we see that the frequency here is on the y -axis.
00:24
It's hard to read, but this is, it looks like 0 .4, 0 .3, 0 .3.
00:34
0 .2, maybe 0 .05, 0 .025, right? something like that.
00:44
Let's see.
00:44
That's 7 .8, yeah, approximately.
00:50
We wanted to add up to about 1.
00:52
We're just estimating.
00:54
Okay, so the middle is going to fall in the 0 .5 range.
00:59
So that's going to be 0 .4 goes up to here.
01:02
So it'll be up here.
01:05
So between 4 and 5.
01:11
So approximately like, well, i guess we're just, if we're using the lower, okay, so they do have decimal.
01:22
So we could say about 4 .5 in between.
01:26
All right.
01:28
And we could do the same thing for part b before i go to the mean.
01:36
So let's see, we have 0 .05, 0 .1.
01:43
This looks like 0 .15.
01:45
So that's going to be 0 .3, and then that's 0 .25, so it's 0 .55.
01:52
There's somewhere in here between 3 and 4, so about 3 .5.
01:58
All right.
01:59
And now we want the mean for each.
02:04
So the mean is going to be if we take the middle of each and multiply by the frequency.
02:11
So we could take 0 .4 multiplied by, what does this say? this is 3 .5 plus 0 .3 times 4.
02:21
4 .5 plus 0 .2 times 5 .5 plus 0 .05 times 6 .5 plus 0 .025 times 7 .5.
02:42
Something like that.
02:49
All right.
02:50
So 0 .4 times 3 .5 is 1 .4 .3 times 4 .5.
02:59
It's going to be 1 .35.
03:04
5 .5 times 0 .2 is 1 .1 .5 .5 times 0 .2 is 1 .1 .5 .5.
03:07
That's just a fifth of .05 times 6 .5 is going to be .325.
03:15
And 0 .025 times 7 .5 is .1875.
03:23
All right...