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MATH6006 Statistical Methods (2023-24)

Coursework: Statistical Data Analysis

Obtain a suitable set of data (40 or more data points). You may obtain this data set from books (although a worked example in a textbook is not acceptable), scientific papers, the web (again, this should not be a worked example), collect an original set of data by yourself, or obtain it from friends in other disciplines. The subject matter should be one that interests you and one which you will be able to interpret easily. If you are unsure about the suitability of your data set, discuss it with me before you start the analysis.

Use MINITAB (or R if you prefer) to carry out some statistical analysis (ap-ply methods covered in MATH6006 or beyond). These could include, for exam-ple, graph plotting, hypothesis testing, simple/multiple linear regression, and/or ANOVA. Produce an A2 poster displaying your data and outlining some of your results. Produce an accompanying technical report. This should include technical details for the interested reader and as such should be concise (no more than 10 pages in total). The poster should be aimed at a lay person with some very ba-sic statistical knowledge. The technical report should be aimed at someone with experience of working with all of the techniques covered in this module. The assess-ment criteria attached gives details of how the poster and the report will be marked.

Submission:

❼ All relevant files, that is poster, technical report, MINITAB analysis together with your data set, should be submitted electronically via Blackboard desig-nated site by 23:59, February 16, 2024.

The deadline is strict. However, late submission up to five days after the deadline is also accepted, but with 10% penalty each day (e.g., for work submitted on the fifth working day after the deadline it will only receive 50% of the total credit awarded). Therefore, no credit will be given for late submissions more than five days after the deadline.

Your report should be computer processed by using certain software of your own choice. Ensure that you take frequent and multiple backups of your work, since excuses concerning lost or corrupted files will not be treated sympathetically.

You may read the relevant section of the course handbook for general informa-tion concerning penalties for lateness and plagiarism.