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Sampling in R
4.8(874)25k studentsINTERMEDIATEEnglish
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Course Overview
The Sampling in R course teaches core sampling methodologies critical for survey design and statistical inference. Using real-world datasets on Spotify songs, coffee ratings, and employee attrition, you will implement simple random, stratified, and cluster sampling techniques, construct sampling distributions, and calculate bootstrap confidence intervals in R.
Who Is This Course For?
Intermediate R users, researchers, and data analysts looking to build robust survey sampling and hypothesis testing workflows.
Key Takeaways
- Implement common sampling methodologies including simple random, stratified, and cluster sampling in R.
- Estimate population statistics and evaluate sampling error across different sampling frames.
- Understand the Central Limit Theorem by building sampling distributions.
- Utilize bootstrap resampling methods to estimate standard errors and confidence intervals.
About this course
Sampling is a cornerstone of inference statistics and hypothesis testing. It's tremendously important in survey analysis and experimental design. This course explains when and why sampling is important, teaches you how to perform common types of sampling, from simple random sampling to more complex methods like stratified and cluster sampling. Later, the course covers estimating population statistics, and quantifying uncertainty in your estimates by generating sampling distributions and bootstrap distributions. Throughout the course, you'll explore real-world datasets on coffee ratings, Spotify songs, and employee attrition.
Price
Free
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