Learn how hypothesis tests help us investigate whether or not outcomes could have occurred by chance.
- Have you ever come upon situations, outcomes,…or events that just seem odd?…In a city made up of 51% women,…where jury pools are said to be chosen at random,…a certain jury pool of 50 people contains only eight women.…A national restaurant chain provides a game piece…for every drink a customer buys.…There are 10 prizes worth over $100,000.…Two of those prizes are won by relatives…of restaurant employees.…
Three employees from a particular chemical factory…with 400 employees are diagnosed with brain cancer…in a two-year period.…When you hear things like this, they make you think.…It doesn't seem right.…Is that even possible?…And if it is possible, how likely is it…that it could have happened at random?…Sometimes these questions and the related answers…could impact our careers and companies.…
They may help us make decisions.…They might influence our superiors to act.…Perhaps you work at a healthcare company.…Your company has developed a drug to treat the common cold.…It's reported that the average adult with the common cold…
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.