00:01
All right, nice little set of conceptual questions here.
00:05
So these are true false, by the way.
00:08
The z statistic is used to estimate the mu, which is the population mean, when both standard deviation sigma is known and unknown.
00:16
That's false.
00:17
Well, it's false because we only use the z statistic when the sigma is known.
00:28
If you don't know it, we use something, we use other things.
00:32
Mostly the t distribution.
00:37
Number two, the point estimate for p is 1 plus e, like an estimator.
00:42
The point estimate is a proportion.
00:45
Sometimes given as p hat is like x over n or some proportion or some percentage.
01:05
Three, the following are types of inferences.
01:09
False.
01:13
Proportion, statistic, estimation, sampling, testing, regression are not all inferences.
01:18
Inferences where you're looking at the data and trying to determine what it says and it's that trying to determine what's telling us that's inferences that's inferring so proportion and statistics these are statistics these are just you observe this you don't do it you haven't done anything with it you just observe this you infer with it but that's not the inference the inference is like your estimation i you know your confidence intervals your regression um you know your confidence intervals your regression um these are inferences right here.
01:55
Sampling, that's another thing that's not inferences.
01:58
These are just right here, these three proportion statistic and sampling.
02:02
Those are statistics, whereas estimation and testing regression, these are inferences here.
02:22
Up here, these are what you're, the tools you're inferring with, so these are not inferences.
02:30
Blank says that when n is sufficiently large, the distribution is approximately normal.
02:34
That is called the central limit theorem...