00:01
So before i get to the answers here, let's remind ourselves what's going on.
00:04
So first of all, we have n is equal to 30, which tells us that the central limit theorem applies, right? the central limit theorem is one of the holy grails of statistics.
00:15
And the central limit theorem tells us what the sampling distribution is, right? the central limit tells us that the sampling distribution of the mean, right, the sampling distribution of x bar, is normal with the true underlying mean, mu, and sigma over root n, right? this is what the central limit theorem says.
00:43
And in particular, we know something about what the normal distribution looks like, right? it will be normal, right? whenever you have a big sample size, the sampling distribution will be normal.
00:55
And if it's normal, that means it will be symmetric, right? and bell -shaped, right? the normal distribution looks like, well, that's not my best normal distribution, like that, right? it's got that symmetric bell -shaped look to it.
01:15
So here, i would immediately start rejecting some of these distributions for being skewed, right? if i look at b, b to me looks right skewed.
01:30
You see that it's not symmetric around the mean, the whole idea of the normal distribution that is symmetric, left and right.
01:39
So i don't buy that b would be plausible, right? similarly, you can see for a, it's got the wrong mean.
02:03
It looks to me like, right, if the true, if true proportion is 0 .6, proportion is equal to 0 .6 proportion.
02:17
And in this case, it looks like a 0 .62 because b, c, and d are all centered around 0 .62.
02:25
You know that a is just located at the wrong place, right? a looks kind of symmetric, but a is simply at the wrong place...