Your Tasks
This coursework comprises a machine learning task, a frequentist statistics task and a Bayesian statistics task (which also contains some frequentist analysis). You need to produce a report of your work following the instructions below. Please note that one report is required. Please do not submit a separate report for each task. Your single report should contain a description of your work for all three tasks.
Your report should contain well presented and annotated R or BUGS type code for all of your analyses.
The page limit is thirty pages, including code and figures. Please do not submit an additional appendix as it will not be considered. Reports that contain irrelevant or uninteresting discussion or code will be penalized.
It is not necessary to repeat figures or code that are very similar.
Stars indicate the relative importance of the individual parts, with more stars indicating that the part is more important. They are included as an indicative guide only.
3.1 Machine Learning Task
For questions about this task, please refer to Dr Malgorzata Wojtys.
Cushing’s syndrome is a collection of medical symptoms due to prolonged exposure to a hormone named cortisol. Over-secretion of cortisol by the adrenal gland may be due to various causes. Being able to determine the cause of the problem allows medical doctors to offer effective treatment to their patients.
Your task is to construct classification rules that predict the type of the syndrome, which indicates the cause, based on urine test. For this, you will use the data set cushings.txt, available from the MATH501 DLE site. The file contains information about urinary excretion rates of two steroid metabolites:
Pn - urinary excretion rate (mg/24hr) of Pregnanetriol,
THC - urinary excretion rate (mg/24hr) of Tetrahydrocortisone.
The data are collected for 150 patients who have one of three types of the syndrome coded as a (adenoma), b (bilateral hyperplasia) or c (carcinoma), as recorded in the variable Type. The index number of each patient is stored in the variable Patient.
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