To provide an overview of basic sampling and estimation methods.
One of the pre-requisites for STAT6091 and STAT6094
Aims and Objectives
Having successfully completed this module you will be able to:
- Demonstrate knowledge and understanding of the basic methods in common use for sampling from finite populations, including the most common sampling designs, and how to estimate finite population parameters and how to assess the estimation errors
The course is divided into the following main topics:
- Introduction: need for samples, terminology, notation, estimation strategy, survey errors, probability sampling
- Simple random sampling (with and without replacement): probability functions, central limit theorem, bias of estimators, inclusion probabilities, standard errors, finite population correction, variance estimation, confidence intervals, proportions, domains, ratio estimation
- Unequal probability sampling: Horvitz-Thompson estimator, design based inference
- Stratified sampling: estimation, variance estimation, sample allocation, post-stratification
- Systematic sampling: estimation, variance estimation
- Multi-stage sampling: cluster sampling (equal and unequal size), estimation, variance estimation, design effects, sample size allocation
Learning and Teaching
Teaching and learning methods
Depending on feasibility, teaching may be delivered face to face intensively over a week, or online using a mixture of synchronous and asynchronous online methods, which may include lectures, workshop activities, exercises, and videos. A range of resources will also be provided for further self-directed study.
|Total study time||100|
Resources & Reading list
Cochran, W. G. (1977). Sampling Techniques. New York: Wiley.
Lohr, S.L. (1999). Sampling Design and Analysis. Pacific Grove: Duxbury Press.
This is how we’ll formally assess what you have learned in this module.
This is how we’ll assess you if you don’t meet the criteria to pass this module.
Repeat type: Internal & External