course
Inference for Categorical Data in R
AvanceradFärdighetsnivå
Uppdaterad 2021-12Börja Kursen Gratis
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RProbability & Statistics4 timmar14 videos53 exercises4,000 XP10,454Uttalande om prestation
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Genom att fortsätta accepterar du våra Användarvillkor, vår Integritetspolicy och att dina uppgifter lagras i USA.Älskad av elever på tusentals företag
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Förkunskapskrav
Foundations of Inference in R1
Inference for a single parameter
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.
Inference for Categorical Data in R
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