00:01
We have been given a probability distribution.
00:03
In part a, we want to know if it is discrete or continuous.
00:08
So a discrete variable can only take a fixed set of values, and there are gaps between those values.
00:20
A continuous variable can take any value in an interval.
00:26
There are no gaps between them.
00:28
If i ever give you two values on a continuous distribution and ask you, could you give me a value in between? you would say, yes, absolutely.
00:37
And then between those, absolutely.
00:39
There are never any gaps.
00:42
So any value in an interval.
00:44
I say that because, for example, if you look at weight, weight is continuous.
00:48
It could be anything, but it can only be something at least zero.
00:52
It can't be negative.
00:53
So this is discrete because it only takes a fixed set of values.
01:00
It can only be 14 or 15 or 16 or 17.
01:05
It can't be 14 .5 for example.
01:10
That's our first part.
01:14
Part b.
01:15
Make a histogram.
01:16
Ok, so we need our axes.
01:18
On the y -axis, i will put credits.
01:23
On the y -axis, we have the probability of that, probability of x.
01:30
And we need 14, 15, 16, and 17.
01:38
And the heights need to go up to 0 .65.
01:43
So i'm going to put 0 .2, 0 .4, 0 .6, 0 .8.
01:52
So 14 has a height of 0 .05.
01:55
Now we've said a histogram, but a histogram isn't really applicable here.
01:59
A histogram is good for continuous variables because the bars touch, so the values would flow into each other, there are no gaps.
02:07
But this is discrete, so i'm going to do a bar chart...