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# Inference for Categorical Data in R

In this course you'll learn how to leverage statistical techniques for working with categorical data.

4 Hours14 Videos53 Exercises

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## 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.

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1. 1

### Inference for a single parameter

Free

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.

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The General Social Survey
50 xp
Exploring consci
100 xp
Generating via bootstrap
100 xp
Constructing a CI
100 xp
Why more bootstraps?
50 xp
Interpreting a Confidence Interval
50 xp
CIs and confidence level
50 xp
SE with less data
100 xp
SE with different p
100 xp
The approximation shortcut
50 xp
CI via approximation
100 xp
Methods compared
50 xp
2. 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. 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. 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.

### In the following Tracks

#### Statistical Inference with R

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Datasets

GSS dataIowa election dataIran election data

Collaborators

Andrew Bray

Assistant Professor of Statistics at Reed College

Andrew Bray is an assistant professor of statistics at Reed College. His interests are in computing, differential privacy, environmental statistics, and statistics education. He is a co-author of the infer package for tidy statistical inference.
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