00:01
So for the first part of the problem here, we want to determine what our bin width should be.
00:06
Now, typically, our bin width is approximately equal to the range, divided by the number of desired bins, or classes, where we know that we want five classes.
00:19
In this case, let's see here.
00:25
What i'll do is first go for that kind of canonical method, 992 minus 133, divide by five.
00:32
See what we get.
00:33
So we get a result of 171 .8.
00:35
That's not a nice value.
00:37
So what i'll do instead is round our two different values for the end points into things that would be a little bit nicer to work with.
00:47
Since i've rounded both to be multiples of five, this means that we should get an integer multiple of five as our bin width.
00:54
So let's set our bin width to be 173, which means that our lowest bin is going to be starting at 103.
01:02
30.
01:03
So we have our different bins or classes.
01:09
So we have class, class limits, frequency, relative frequency, and cumulative frequency.
01:25
For class one, we would have that our lower limit, as i mentioned, would be 130.
01:29
And now i'll note that there are different standards for how to write the class limits.
01:35
My personal preference is to write them such that the endpoints are included when we're dealing with values that are integer as we are in this case.
01:45
So we'd have that the lower bound for the first class limit is 130, then the lower bound for the next class limit would be 303.
01:54
So that means that the upper bound for the first class limit should be 302.
01:58
Looking at our data, we can see that 133, 175, 189, 195, and 210 all fall into that first bin.
02:08
So the first bin, or first class, has a frequency of five.
02:12
Then for the second class, as i said, lower limit would be 303...