In our Hypothesis Testing in R course, you will learn about advanced statistical concepts such as significance testing and multi-category chi-square testing for more powerful and robust data analysis.

You'll learn about a single and multi-category chi-square tests, degrees of freedom, hypothesis testing, and different statistical distributions. And you'll work hands-on with multiple datasets to learn statistical concepts.

To learn about hypothesis testing and statistical significance, you'll work with weight loss data. Are patients losing weight due to pure luck, or is it a diet pill? You’ll run the numbers and find out!

At the end of the course, you'll complete a portfolio project in which you'll work with Jeopardy data to analyze text, searching for winning Jeopardy strategies. It’s a chance for you to combine the skills you learned in this course, and when you finish it you’ll have a fascinating project to showcase in your portfolio and an interesting conversation starter for data science networking — who doesn’t want to know how to win at Jeopardy?

By the end of this course, you'll be able to:

  • Learn how to handle probability density functions.
  • Learn how to create testable hypotheses.
  • Learn how to decide which hypotheses to support from your data.

Learn Hypothesis Testing Fundamentals

Probability Distributions

Learn about how probability is spread through different events through probability distributions functions.

Hypothesis Testing

Get an introduction hypothesis testing with the two sample independent t-test

Categorical Data and The Chi-Squared Test

Get an introduction to the chi-squared test and the chi-squared distribution for examining categorical data.

Multi Category Chi-Squared Tests

Learn how to apply the chi-squared test across multiple categories.

Winning Jeopardy

Learn how to analyze text while figuring out strategies to win at Jeopardy.