620-374: Sampling and Forecasting
Semester 2, 2008
This subject covers a range of important and generally applicable statistical methods. Students should develop the ability to employ these methods to implement a range of practically useful statistical analyses. The following three topics will be covered.
- Sample surveys: simple random sampling; stratified sampling; optimal allocation, post-stratification; cluster sampling; ratio estimation.
- Time series and forecasting: patterns in time series; simple methods for exploratory data analysis; smoothing techniques; decomposition, trends and seasonal variation; simple forecasting methods; models for time series: stationarity, autocorrelation, ARMA processes; estimation and model fitting.
- Re-sampling methods: jack-knife and the bootstrap; use of the bootstrap for exploring the sampling distribution of an estimator.
The subject develops the students' generic skills, including thinking critically and organizing knowledge; analysing data, interpreting results and presenting conclusions in a clear and comprehensible manner; using computers for data analysis and presentation; and solving problems.
Lecturer
Dr Owen Jones, room 221 Richard Berry building.
Contact details are available on the departmental web site.
Timetable
The teaching period runs from Monday 28 July to Friday 19 September then Monday 6 October to Friday 31 October
Lectures are Tue, Wed and Thu, 2:15-3:15 in Richard Berry room 213.
Tutorial is Thu 4:15-5:15 in Richard Berry room G70 (computer lab).
References and Course Material
See the web page for each sub-section for references and course materials.
For numerical work we will use Excel and the language R.
Here are links to some useful R Resources:
Assessment
Assessment consists of regular assignments worth a total of 20% and final 3-hour exam worth 80%.
A sample survey formula sheet will be provided for the exam.
Past exams:
SSLC Representative
The class representatives on the SSLC are Hao Min and Davis McCarthy.