Sampling methods
Undergraduate · Math
Syllabus focus
Topics typically covered
Click a topic for the full text and related unit practice.
Undergraduate Sampling methods — outline derived from course README sections and typical US statistics syllabi (OpenIntro / standard OER where applicable).
Sampling concepts
- Target population and sampling frame — Target population and sampling frame
- Sampling units — Sampling units
- Simple random sampling: estimates — Simple random sampling: estimates
- Variance — Variance
- Stratified sampling: optimal allocation — Stratified sampling: optimal allocation
- Systematic sampling — Systematic sampling
- Cluster and multistage sampling — Cluster and multistage sampling
Estimation and inference
- Ratio and regression estimators — Ratio and regression estimators
- Design-based vs model-based inference — Design-based vs model-based inference
- Confidence intervals for finite populations — Confidence intervals for finite populations
- Sample size determination for surveys — Sample size determination for surveys
- Two-stage sampling variance formulas — Two-stage sampling variance formulas
Nonresponse and weighting
- Unit and item nonresponse — Unit and item nonresponse
- Post-stratification — Post-stratification
- Raking (introduction) — Raking (introduction)
- Calibration estimators (overview) — Calibration estimators (overview)
- Total survey error framework — Total survey error framework
Study units
Each unit includes a study guide, worksheets, review, practice test, and answer key. One unit is free; subscribe for the full class.
- Sampling concepts
Target population and sampling frame
Coming soon - Estimation and inference
Ratio and regression estimators
Coming soon - Nonresponse and weighting
Unit and item nonresponse
Coming soon
Notes
Undergraduate Sampling methods — outline derived from course README sections and typical US statistics syllabi (OpenIntro / standard OER where applicable). Topic outline: `content/topics/undergraduate/sampling.json`.