Module Descriptor School of Computer Science and Statistics
|Module Name||Base Module (Post Graduate Certificate in Statistics)|
|Module Short Title||N/a|
|Semester Taught||Michaelmas Semester 1|
Tutorial hours: 11
|Module Personnel||Lecturing staff: Mimi Zhang|
On successful completion of the Base Module, students should be able to:
They will have a sound basis on which to develop further their statistical skills.
The base module is introductory and will lay down the foundations on which other modules will build. The fundamental statistical inferential ideas of significance tests and confidence intervals are the central topics. The various inferential methods will be unified through the concept of a statistical model, which is an abstract representation of the quantity we wish to describe. For example, we may choose to represent the weights of filled containers by a Normal distribution with a particular centre (mean) and measure of spread (standard deviation). This would allow us to introduce formal tests to determine when the process average weight changes.
Of course, the value of any formal procedure will depend on how well the underlying model represents the characteristics of the practical problem. When models are fitted, good statistical practice requires the assessment of the models used; this is done mainly by use of graphical procedures. These may be simple scatterplots of two characteristics of a number of individuals (e.g., heights and weights of a sample of people) to determine whether or not the assumption of a linear relationship between the two characteristics is reasonable. Alternatively, the graph might be a Normal probability plot (quantile-quantile plot) of residuals (differences between observed and predicted values) after a complex multiple regression model has been fitted to the data. Many questions can be answered by simple plots, so the course will emphasis practical methods that can be applied across many empirical disciplines.
Specific topics addressed in this module include:
|Recommended Reading List|
|Academic Year of Data||20178/19|