After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
Categorical data is all around us. It's in the latest opinion polling numbers, in the data that lead to new breakthroughs in genomics, and in the troves of data that internet companies collect to sell products to you. In this course you'll learn techniques for parsing the signal from the noise; tools for identifying when structure in this data represents interesting phenomena and when it is just random noise.
Prerequisites
Curriculum
Course outline
1
Inference for a single parameter
In this chapter you will learn how to perform statistical inference on a single parameter that describes categorical data. This includes both resampling based methods and approximation based methods for a single proportion.
- The General Social Survey50 XP
- Exploring consci100 XP
- Generating via bootstrap100 XP
- Constructing a CI100 XP
- Why more bootstraps?50 XP
- Interpreting a Confidence Interval50 XP
- CIs and confidence level50 XP
- SE with less data100 XP
- SE with different p100 XP
- The approximation shortcut50 XP
- CI via approximation100 XP
- Methods compared50 XP
2
Proportions: testing and power
This chapter dives deeper into performing hypothesis tests and creating confidence intervals for a single parameter. Then, you'll learn how to perform inference on a difference between two proportions. Finally, this chapter wraps up with an exploration of what happens when you know the null hypothesis is true.
3
Comparing many parameters: independence
This part of the course will teach you how to use both resampling methods and classical methods to test for the indepence of two categorical variables. This chapter covers how to perform a Chi-squared test.
4
Comparing many parameters: goodness of fit
The course wraps up with two case studies using election data. Here, you'll learn how to use a Chi-squared test to check goodness-of-fit. You'll study election results from Iran and Iowa and test if Benford's law applies to these datasets.
R
Inference for Categorical Data in R
Course
Complete
