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AUTUMN SEMESTER 2022-2023

MTHS3003 - APPLIED STATISTICS AND PROBABILITY

Project Overview

Deadline: 16:00 Thursday 15���ℎ December 2022

The MTHS3003 project involves answering a personalised set of questions provided in MTHS3003_Coursework_XXXXXXXX.pdf where XXXXXXXX denotes your Student ID number. The file MTHS3003_Coursework_XXXXXXXX.pdf is provided in the coursework folder on Moodle along with your own personalised data XXX .

Check that the questions and data for your student number XXXXXXXX are provided as soon as possible. If your questions and/or data are missing inform me by email at: [email protected] as soon as possible and I will upload your questions and data within 24 hours of being informed. No extension will be given due to questions and/or data not being available.

The project involves writing a short report (maximum of 10 pages including any figures and appendices) to answer the five questions (tasks). Note that the questions are similar for all students but involve your personalised data. The marks for each task are given in MTHS3003_Coursework_XXXXXXXX.pdf and total 85 marks. A further 15 marks are available for overall presentation giving a total mark out of 100.

Report structure

An overview of the expected report structure is given below.

Introduction. Give an overview of what you are being asked to do and how you are going to approach the analysis.

Main body. You can either go through Task by Task or cover all Tasks at once. In either case you should follow a process along the lines of:

• State what question (task) you are answering. This should be written in terms that someone who is not statistically literate should be able to understand.

• Present the methods you are going to use to answer the question. (You do not need to repeat the lecture notes but give the key points.) For example, which variables will you use? What hypotheses will you test? What model will you fit?

• Present the results. Obviously you will use R to compute numerical results and plot graphs. Think carefully about how to extract and present only the relevant results. Where possible use graphs to illustrate the results. Remember to comment on whether the data satisfies a method’s assumptions.

• Interpret the results. What do the results mean in terms of the research question you asked? Interpret the results in terms that someone who is not statistically literate should be able to understand.

Conclusion. Summarise the most interesting findings from your analysis.  You  can also think beyond  the confines of what you were asked to do. For example: What are the strengths and weaknesses of your analysis? What would be worthy of further investigation?

R

All, of the tasks will require using R to answer the questions. Do not put R code or output directly into your report but summarise the statistical techniques used and the findings in tables, figures, equations or within the text as appropriate. Your R code should be submitted as a separate file and does not count in the page count for the report.

Report typesetting

Your report should be typeset and include figures and tables as appropriate. You are free to use, for example, Word, R markdown, LATEX  or other software of your choice. You should submit your report as a pdf file and submit a separate file with your R code either an R script file (.R) or an R markdown file (.Rmd). For example, if you use R markdown to write the report it suffices to submit the R markdown file along with the knitted pdf file.