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In this course, you will :
- Learn what sampling is and why it is useful, as well as the issues caused by convenience sampling and the distinctions between true randomness and pseudo-randomness.
- Learn how and when to use the four random sampling methods: simple, systematic, stratified, and cluster.
- Learn how to use relative errors to quantify the accuracy of sample statistics and how to generate sampling distributions to measure variation in your estimates.
- Learn how to use resampling to perform bootstrapping, which is used to estimate variation in a population that is unknown.
- Recognize the distinction between sampling distributions and bootstrap distributions.
Syllabus :
- Bias Any Stretch of the Imagination
- Don't get theory eyed
- The n's justify the means
- Pull Your Data Up By Its Bootstraps
