Build your ultimate AI agent
Course Description
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.
Prerequisites
Curriculum
Course outline
1
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.
- Sampling and point estimates50 XP
- Reasons for sampling50 XP
- Simple sampling with dplyr100 XP
- Simple sampling with base-R100 XP
- Convenience sampling50 XP
- Are findings from the sample generalizable?100 XP
- Are these findings generalizable?100 XP
- Pseudo-random number generation50 XP
- Generating random numbers100 XP
- Understanding random seeds100 XP
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.
R
Sampling in R
Course
Complete
