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 Survey

Exploring consci

Generating via bootstrap

Constructing a CI

Why more bootstraps?

Interpreting a Confidence Interval

CIs and confidence level

SE with less data

SE with different p

The approximation shortcut

CI via approximation

Methods compared

Inference for a single parameter

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.

Hypothesis test for a proportion

Life after death

Generating from H0

Testing a claim

Making a decision

Intervals for differences

Death penalty and sex

Hypothesis test on the difference in proportions

Interpreting the test

Hypothesis tests and confidence intervals

Statistical errors

When the null is true

When the null is true: decision

Proportions: testing and power

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.

Contingency tables

Politics and Space

Understanding contingency tables

From tidy to table to tidy

Chi-squared test statistic

A single permuted Chi-sq

Building two null distributions

Is the data consistent with the model?

Alternate method: the chi-squared distribution

Checking conditions

The geography of happiness

A p-value two ways

Intervals for the chi-squared distribution

Comparing many parameters: independence

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.

Casestudy: verkiezingsfraude

Kennismaken met de Iran-data

Wie heeft er gewonnen?

Uitsplitsen per provincie

De eerste cijfer extraheren I

Goodness-of-fit

Goodness-of-fit-toets

Een p-waarde op twee manieren

Is dit bewijs van fraude?

En nu naar de VS

De Iowa-gegevens leren kennen

Het eerste cijfer extraheren II

Iowa testen

Fraude in Iowa?

Verkiezingsfraude in Iran en Iowa: nabespreking

Comparing many parameters: goodness of fit

GSS data

Iowa election data

Iran election data

Categorische gegevens zijn overal. Ze zitten in de nieuwste peilingscijfers, in de data die leiden tot nieuwe doorbraken in de genomica, en in de bergen gegevens die internetbedrijven verzamelen om je producten te verkopen. In deze cursus leer je technieken om het signaal van de ruis te scheiden; hulpmiddelen om te bepalen wanneer structuur in deze data interessante verschijnselen weerspiegelt en wanneer het slechts willekeurige ruis is.

Foundations of Inference in R

In deze cursus leer je statistische technieken gebruiken voor categorische data.

Inferentie voor categorische gegevens in R

In deze cursus leer je hoe je statistische technieken kunt gebruiken voor het werken met categorische gegevens.

Statistische conclusies in R

Fraude in Iowa?

Inferentie voor categorische gegevens in R

Praktische interactieve oefening