0:00:00.012,0:00:05.723
We could really group this data any way we want. In fact, we could only have two
0:00:05.735,0:00:11.421
rows, one for the number of students younger than 50 years old, and one for the
0:00:11.433,0:00:16.955
number greater, but we don't have to. We could even have one row for each age.
0:00:17.074,0:00:22.236
So here, we could have 10, 11, 12, and then we would count to see how many
0:00:22.248,0:00:28.013
students are aged 10, how many students are aged 11. But this is not the most
0:00:28.025,0:00:34.155
convenient way. That's because, the frequency would most likely be 1 for all of
0:00:34.167,0:00:39.676
them, maybe 2 for a few. So, instead of doing that, how about we choose an
0:00:39.688,0:00:46.235
interval for each row? For example, 0 to 19 and then 20 to 39, then we can count
0:00:46.247,0:00:52.625
how many students are between ages 0 and 19, between 20 and 39, etc. This is
0:00:52.637,0:00:59.386
called the interval or bin or bucket. For the most part, we'll either call it an
0:00:59.398,0:01:05.652
interval or the bin and in this case the bin size is 20. That's because it
0:01:05.664,0:01:06.593
includes 0.