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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.

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4 Hours14 Videos53 Exercises6,263 Learners
4000 XP

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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.

    Play Chapter Now
    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.

    Play Chapter Now
    Hypothesis test for a proportion
    50 xp
    Life after death
    100 xp
    Generating from H0
    100 xp
    Testing a claim
    100 xp
    Making a decision
    50 xp
    Intervals for differences
    50 xp
    Death penalty and sex
    100 xp
    Hypothesis test on the difference in proportions
    100 xp
    Interpreting the test
    50 xp
    Hypothesis tests and confidence intervals
    100 xp
    Statistical errors
    50 xp
    When the null is true
    100 xp
    When the null is true: decision
    100 xp
  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.

    Play Chapter Now
    Contingency tables
    50 xp
    Politics and Space
    100 xp
    Understanding contingency tables
    50 xp
    From tidy to table to tidy
    100 xp
    Chi-squared test statistic
    50 xp
    A single permuted Chi-sq
    100 xp
    Building two null distributions
    100 xp
    Is the data consistent with the model?
    50 xp
    Alternate method: the chi-squared distribution
    50 xp
    Checking conditions
    50 xp
    The geography of happiness
    100 xp
    A p-value two ways
    100 xp
    Intervals for the chi-squared distribution
    50 xp
  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.

    Play Chapter Now
    Case study: election fraud
    50 xp
    Getting to know the Iran data
    50 xp
    Who won?
    100 xp
    Breaking it down by province
    100 xp
    Extracting the first digit I
    100 xp
    Goodness of fit
    50 xp
    Goodness of fit test
    100 xp
    A p-value, two ways
    100 xp
    Is this evidence of fraud?
    50 xp
    And now to US
    50 xp
    Getting to know the Iowa data
    50 xp
    Extracting the first digit II
    100 xp
    Testing Iowa
    100 xp
    Fraud in Iowa?
    50 xp
    Election fraud in Iran and Iowa: debrief
    50 xp

In the following tracks

Statistical InferenceStatistician

Datasets

GSS dataIowa election dataIran election data

Collaborators

Nick SolomonBenjamin FederJonathan Ng

Prerequisites

Foundations of Inference
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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Lloyds Banking Group

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Harvard Business School

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Decision Science Analytics, USAA