Course overview
In this course, you’ll learn about single and multi-category chi-square tests, degrees of freedom, hypothesis testing, and different statistical distributions.
To learn about hypothesis testing and statistical significance, you’ll work hands-on with multiple datasets on 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 guided project in which you’ll work with data from the American TV show Jeopardy. You’ll analyze text and search for winning strategies. It’s a chance for you to combine the skills you learned in this course, and to showcase a fascinating project in your portfolio.
Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser.
Key skills
- Defining regular and multi-category chi-squared tests
- Performing significance testing to understand an outcome's importance
Course outline
Hypothesis Testing in Python [4 lessons]
Significance Testing 1h
Lesson Objectives- Explain how hypothesis testing works
- Define the relation between statistical significance and hypothesis testing
Chi-Squared Tests 1h
Lesson Objectives- Determine the statistical significance of a set of categorical values
- Generate the chi-squared distribution
- Define degrees of freedom
Multi-Category Chi-Squared Tests 1h
Lesson Objectives- Extend chi-squared tests to multiple categories
- Calculate the statistical significance of multi-category chi-squared tests
Guided Project: Winning Jeopardy 1h
Lesson Objectives- Answer questions using text data
- Apply chi-squared tests to real problems
Projects in this course
Winning Jeopardy
For this project, you’ll take on the role of a Jeopardy contestant looking for any edge to win. You’ll work with a dataset of 20,000 Jeopardy questions using Python and pandas to analyze question and answer text and uncover helpful patterns.
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