Get practical, example-based learning of the intermediate skills associated with statistics: samples and sampling, confidence intervals, and hypothesis testing.
- As a person that loves statistics, or perhaps as someone who just appreciates statistics, you're probably comfortable with the basics: means, medians, standard deviations, probabilities, and normal distributions. They're all part of your stats vocabulary. But perhaps for you, stats appreciation is not enough. You want to collect your own data. You want to make reasonable predictions. You'd like to test statistical assumptions.
You've come to the right place because that is what this course is all about. My name is Eddie Davila, and I'm a university instructor with degrees in business and engineering. I write ebooks, and of course I develop online educational content. I'm a huge sports fan. I love to follow the entertainment industry. And I'm passionate about science and health. And I can tell you that in every important facet of my life, having a better understanding of statistics allows me to improve my performance and often to find a greater level of satisfaction whether I'm working or playing.
This course, Statistics Fundamentals Part Two, is the second of a three-part series that I'm hoping will empower you better to understand the numbers you will encounter in your life. In this course, you'll discuss the collection of data and the importance of the simple random sample. You'll look at confidence intervals. We'll explore what margins of error mean. We'll discover the importance of hypothesis testing in the fields of science, business, and beyond.
And I'll tell you, even if you know what many of these things are, I think you'll walk away with a new perspective. Actually, I'm hoping you'll never look at these concepts the same way again. You won't just understand the power of data and statistics. You'll know their inherent weaknesses too. Welcome to Statistics Fundamentals Part Two. Improved performance and increased satisfaction are just around the corner.
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.