Inference for Categorical Data in R

In this course you'll learn how to leverage statistical techniques for working with categorical data.
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4 Hours14 Videos53 Exercises5,829 Learners
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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.

  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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  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.
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  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.
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  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.
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In the following tracks
Statistical InferenceStatistician
Collaborators
Nick SolomonBenjamin FederJonathan Ng
Andrew Bray Headshot

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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