paid course

Inference for Categorical Data

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

  • 4 hours
  • 14 Videos
  • 52 Exercises
  • 451 Participants
  • 3,900 XP
Andrew Bray
Andrew Bray

Instructor at DataCamp

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Collaborator(s)
  • Nick Solomon

    Nick Solomon

  • Benjamin  Feder

    Benjamin Feder

  • Chester Ismay

    Chester Ismay

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.

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

Course Outline

  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.

  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.

Course Instructor

Andrew Bray
Andrew Bray

Instructor at DataCamp

See More
Collaborator(s)
  • Nick Solomon

    Nick Solomon

  • Benjamin  Feder

    Benjamin Feder

  • Chester Ismay

    Chester Ismay

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