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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!
Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser.
At the end of the course, you’ll complete a guided project that asks you to 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.
Key skills
- Creating testable hypotheses
- Defining regular and multi-category chi-squared tests
- Performing significance testing to understand an outcome's importance
- Handling probability density functions
Course outline
Hypothesis Testing in R [5 lessons]
Probability Distributions 1h
Lesson Objectives- Define a probability distribution
- Calculate probabilities
- Define a cumulative probability function
Hypothesis Testing 1h
Lesson Objectives- Identify wow hypothesis testing works
- Identify the relations between hypothesis testing, probability and statistics
Categorical Data and The Chi-Squared Test 1h
Lesson Objectives- Determine the statistical significance of observing a set of categorical values
- Generate the chi-squared distribution
- Employ R functions for the chi-squared distribution
Multi category chi-squared tests 1h
Lesson Objectives- Extend chi-squared to multiple categories
- Calculate the statistical significance of multi-category chi-squared tests
Guided Project: Winning Jeopardy 1h
Lesson Objectives- Expand your portfolio with a guided hypothesis testing project
- Employ chi-squared tests to solve real problems
Projects in this course
Winning Jeopardy
For this project, we’ll assume the role of a Jeopardy contestant analyzing a dataset of past questions, using chi-squared tests and text analysis in R to identify common categories and develop optimal strategies.
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