Course
Sampling in R
IntermediateSkill Level
Updated 08/2024RProbability & Statistics4 hr15 videos51 Exercises4,000 XP24,430Statement of Accomplishment
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Prerequisites
Introduction to Statistics in R1
Introduction to Sampling
Learn what sampling is and why it is useful, understand the problems caused by convenience sampling, and learn about the differences between true randomness and pseudo-randomness.
2
Sampling Methods
Learn how to and when to perform the four methods of random sampling: simple, systematic, stratified, and cluster.
3
Sampling Distributions
Learn how to quantify the accuracy of sample statistics using relative errors, and measure variation in your estimates by generating sampling distributions.
4
Bootstrap Distributions
Learn how to use resampling to perform bootstrapping, used to estimate variation in an unknown population. Understand the difference between sampling distributions and bootstrap distributions.
Sampling in R
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FAQs
What sampling methods does this course cover?
You will learn four methods: simple random sampling, systematic sampling, stratified sampling, and cluster sampling, along with when each method is most appropriate.
What datasets are used for the exercises?
You will work with real-world datasets on coffee ratings, Spotify songs, and employee attrition to practice different sampling techniques and estimate population statistics.
Does this course explain bootstrap distributions?
Yes. The final chapter teaches resampling-based bootstrapping to estimate variation in an unknown population and explains how bootstrap distributions differ from sampling distributions.
What R packages will I use?
You will use dplyr for data manipulation along with base R and tidyverse tools. Prerequisites include Introduction to Statistics in R, so familiarity with basic statistical functions is expected.
How does the course measure the accuracy of sample statistics?
Chapter 3 teaches you to quantify accuracy using relative errors and to generate sampling distributions that show how much variation exists across repeated samples from the same population.
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