Inference for Numerical Data in R
In this course you'll learn techniques for performing statistical inference on numerical data.
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Course Description
In this course, you'll learn how to use statistical techniques to make inferences and estimations using numerical data. This course uses two approaches to these common tasks. The first makes use of bootstrapping and permutation to create resample based tests and confidence intervals. The second uses theoretical results and the t-distribution to achieve the same result. You'll learn how (and when) to perform a t-test, create a confidence interval, and do an ANOVA!
- 1
Bootstrapping for estimating a parameter
FreeIn this chapter you'll use bootstrapping techniques to estimate a single parameter from a numerical distribution. - 2
Introducing the t-distribution
In this chapter you'll use Central Limit Theorem based techniques to estimate a single parameter from a numerical distribution. You will do this using the t-distribution. - 3
Inference for difference in two parameters
In this chapter you'll extend what you have learned so far to use both simulation and CLT based techniques for inference on the difference between two parameters from two independent numerical distributions. - 4
Comparing many means
In this chapter you will use ANOVA (analysis of variance) to test for a difference in means across many groups.
In the following tracks
Statistical InferenceDatasets
Chp1-vid1-boot-dist-noaxes-paranthesesChp1-vid1-bootsamp-bootpop.001Chp1-vid1-manhattan-rentsChp1-vid2-boot-dist-withaxesChp1-vid2-perc-method.001Chp1-vid2-perc-method.002Chp1-vid3-boot-test.001Chp3-vid3-hrly-rate-citizen-smallerChp3-vid3-hrly-rate-citizenChp4-vid1-class-barChp4-vid1-wodrsum-histGss moredaysGSS dataManhattan rent dataRunners.001Tdistcomparetonormaldist
Nick Carchedi
Nick Solomon
Prerequisites
Foundations of Inference
Mine Cetinkaya-Rundel
Associate Professor at Duke University & Data Scientist and Professional Educator at RStudio
Mine is the Director of Undergraduate Studies and an Associate Professor of the Practice in the Department of Statistical Science at Duke University as well as a Professional Educator at RStudio. Her work focuses on innovation in statistics pedagogy, with an emphasis on computation, reproducible research, open-source education, and student-centered learning. She is the author of three open-source introductory statistics textbooks as part of the OpenIntro project and teaches the popular Statistics with R MOOC on Coursera.
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