MAS370 Sampling Theory and Design of Experiments
Note: This is an old module occurrence.
You may wish to visit the module list for information on current teaching.
|Semester 2, 2014/15||10 Credits|
|Lecturer:||Dr Kevin Walters||uses MOLE||Reading List|
|Aims||Outcomes||Teaching Methods||Assessment||Full Syllabus|
The results of sample surveys through opinion polls are commonplace in newspapers and on television. The objective of the Sampling Theory section of the module is to introduce several different methods for obtaining samples from finite populations. Experiments which aim to discover improved conditions are commonplace in industry, agriculture, etc. The purpose of experimental design is to maximise the information on what is of interest with the minimum use of resources. The aim of the Design section is to introduce some of the more important design concepts.
Prerequisites: MAS205 (Statistics Core); MAS273 (Statistical Modelling) recommended
No other modules have this module as a prerequisite.
- This course deals with two different areas where the important features are the planning before the data are collected, and the methods for maximising the information which will be obtained. The results of sample surveys through opinion polls, etc., are commonplace in newspapers and on television. The Sampling Theory component of the course introduces several different methods for obtaining samples from finite populations and considers which method is most appropriate for a given sampling problem. Experiments which aim to discover improved conditions are commonplace in industry, agriculture, etc. The purpose of experimental design is to maximise the information on what is of interest with the minimum use of resources. The Experimental Design component of the course introduces some of the more important design concepts.
- To consolidate some previous mathematical and statistical knowledge.
- To introduce statistical ideas used in sample surveys and the design of experiments.
- To study the use of statistical methods in the design and analysis of computer experiments.
Lectures, problem solving
20 lectures, no tutorials
One formal 2 hour written examination. Format: all questions compulsory
1. Experimental Design
- A review of linear models: matrix notation; least squares estimation; orthogonality; prediction; confidence regions.
- Optimality criteria: D-optimal, G-optimal, V-optimal and A-optimal designs.
- Completely randomised designs and randomised block designs.
- Latin squares and balanced incomplete block designs.
- Factorial designs: complete factorial designs; fractional factorial designs; screening experiments.
- Designs for mixture experiments.
- Continuous and exact designs, and the Generalised Equivalence Theorem.
- Simple random sampling.
- Stratified sampling.
- Cluster sampling.
- Ratio and regression estimators.
- Capture-recapture sampling
- Questionnaire design.
- Uncertainty in computer model inputs: uncertainty and sensitivity analysis.
|B||Barnett||Sample Survey; Principles and Methods||519.6 (B)||Blackwells||Amazon|
|B||Box, Hunter and Hunter||Statistics for experimenters: design, innovation, and discovery||519.5(B)||Blackwells||Amazon|
|B||Morris||Design of experiments: an introduction based on linear models||001.434 (M)||Blackwells||Amazon|
|C||Atkinson and Donev||Optimum Experimental Designs||519.52 (A)||Blackwells||Amazon|
|C||Box and Draper||Empirical model building and response surfaces||519.52 (B)||Blackwells||Amazon|
|C||Cornell||Experiments with mixtures||519.52 (C)||Blackwells||Amazon|
|C||Cox and Reid||The theory of the design of experiments||519.52 (C)||Blackwells||Amazon|
|C||Goos and Jones||Optimal design of experiments : a case study approach||670.285 (G)||Blackwells||Amazon|
(A = essential, B = recommended, C = background.)
Most books on reading lists should also be available from the Blackwells shop on Mappin Street.