Learn about the calculation of a standard error for sample means.
- Through the use of the central limit theorem,…we've seen how taking just a few random samples…can guide us in the direction of the population mean.…Of course when we use only a few samples to try…and figure out a population mean,…we understand that the average of our…sample means comes with a standard error.…So how do we figure out the standard error…for our simple random samples?…Let's say we're trying to figure out…how long it takes to get our coffee drinks…from our local cafe between 7 a.m.…
and 8 a.m. on a Monday morning.…We take samples on four different Mondays.…Our sample size for each of these samples is five.…Here is our data set for those days, times are in minutes.…So, for sample A you can see that the time…it took our five customers to get coffee…ranged from 0.6 minutes to 2.4 minutes.…
The average for sample A was 1.58 minutes.…If we take the average of the sample means,…we will find that the average time to…get a coffee drink was about 1.52 minutes.…And the standard deviation of those…four sample means is 0.25 minutes.…
Eddie Davila first provides a bridge from Part 1, reviewing introductory concepts such as data and probability, and then moves into the topics of sampling, random samples, sample sizes, sampling error and trustworthiness, the central unit theorem, t-distribution, confidence intervals (including explaining unexpected outcomes), and hypothesis testing. This course is a must for those working in data science, business, and business analytics—or anyone else who wants to go beyond means and medians and gain a deeper understanding of how statistics work in the real world.
- List the three primary issues addressed in Statistics Foundations: 2.
- Recognize two key characteristics associated with simple random samples.
- Apply the Central Limit Theorem to find the average of sample means.
- Analyze random samples during hypothesis testing.
- Assess individual situations to determine whether a one-tailed or two-tailed test is necessary.
- Define acceptance sampling.