This video provides a verbal and visual explanation of the abilities and limitations of confidence intervals.
- Let's create a 95% confidence interval…for an election poll where the voters…have two choices: Candidate A and Candidate B.…As you may have guessed, we'll be working with proportions.…Before we start creating a 95% confidence interval…for this scenario, let's recap a few things.…First, if we took a lot of voter samples,…the distribution would be approximately normal.…Second, the larger the voter sample size,…the smaller the variation,…and thus, the smaller the standard deviation…of the resulting distribution.…
Third, a 95% interval is one where we are 95% certain…that our interval which will be centered…at the sample's proportion will contain…the actual population proportion.…Now what we're going to do is take a single sample.…This single random sample might include 50 eligible voters,…but the resulting sample proportion…of eligible voters that favor Candidate A…is a single number we can call p-hat.…
This single sample proportion is a single dot…on this distribution.…Now the question is,…is it a dot that is close…
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.