00:01
Which of these is true of density curves? so we've got five options here.
00:05
Let's go through them.
00:07
So first of all, what is a density curve? you could call it a probability curve.
00:11
It's a curve that reflects a continuous probability distribution.
00:17
So it's a function or a continuous distribution.
00:30
What about a? is the normal curve a density curve? yes, it is.
00:35
The normal distribution is...
00:38
It looks like this, and it is a continuous probability distribution.
00:43
So it does have a density curve.
00:46
Does it have to follow the 68, 95, 99 .7 rule? so this is a rule that tells you approximately what percentage of your data set is within a certain interval.
00:58
68 % within one standard deviation of the mean, so about this.
01:02
95 % within two, 99 .7 % within three.
01:06
But it only applies to the normal distribution and things that look like it, so bell shapes or approximately normal.
01:14
It doesn't have to apply to every density curve.
01:18
For example, if i draw a uniform distribution, this is a density curve, even though it's a rectangle.
01:25
It's a function for a continuous distribution.
01:28
It does not follow the 68, 95, 99 .7 rule, but it's still valid.
01:34
So we can rule this one out.
01:37
They do not have to follow this rule.
01:42
And they'd be used to model sampling distributions.
01:45
Yes, they can.
01:46
So this is referring to the central limit theorem, which states that as sample size increases, sample means become more and more normally distributed...