This tutorial provides a discussion of the limitations of Statistics 1 and introduces the primary topics covered in Statistics 2.
- In Statistics Fundamentals part one,…you saw means and medians, standard deviations,…probability, normal distributions.…You now have a nice foundation of statistics knowledge.…So let's jump into some new material.…What lies ahead in Statistics Fundamentals part two?…Well, it's time to start to put our foundational statistics…to use.…We'll be looking at three primary issues in statistics,…sampling, confidence intervals and hypothesis testing.…
In our sampling section,…we'll take samples of data,…calculate standard statistics like means or probabilities.…We will then look at the distribution of our results.…Of course, we'll also discuss…how much data needs to be collected…and how to properly collect this data.…So as we consider the issues of samples and sample size,…we're really looking to collect data…in order to find meaningful statistics…that will inform us about a population.…
These are called inferential statistics.…For example, what percentage of American adults…are in favor of the death penalty?…Without asking every American adult,…
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.