MATH3802/MATH5802M practical 2023/24

Background

•  The MATH3802 and MATH5802 modules are assessed by an examination (80%) and a practical (20%). This is the practical, worth 20% of your final module mark.

•  Comments on this sheet which are only relevant to MATH5802M students are indicated by italic font.

•  Reports must be clearly marked with your name, student ID, and module and should be no more than six sides of A4 paper in length (eight sides for MATH5802M). If you want to submit e-files,please name your report file as StudentID-Name-MATH3802 or StudentID-

Name-MATH5802.

•  Practical is given in week 6 (6th-10th Nov).  You will have two hours to work on your practical in the supervised session.  My colleagues will be available to answer general questions, but not technical questions.

•  You can share ideas with other students, but the work you hand in must be your own.

•  Please include a signed Declaration of Academic Integrity Form.

•  You must hand in your solutions by 5pm on Friday, 24th Nov 2022.  Please hand in an electronic version via Minverva ! Assessment and Feedback ! Submit My Work !

Turnitin. Late work will be penalised at the rate of 5% of the available marks per calendar day.

Data

The data are taken from a database about slave voyages. Specifically, we will consider theyearly estimated number of slaves who embarked on a voyage across the Atlantic Ocean — on a ship under a British flag.

For  this  practical  you  will  need  to  obtain  the  data  set  X    =    (X1; : : : ; Xn)  from  the file “slavery.RData”  which  you  can  download  from  Minerva.     Once  you  have  the file “slavery.RData” saved in the folder where you are running R, use the command load("slavery.RData") to read the data into R. This will give you a data frame called xf which has two components: xf$year, and xf$num. (Try summary(xf) or str(xf).)

The task

The task of this practical is to analyse these data, using the following list of points as a guide. Your analysis should consider the points below, but your report should not be laid out as “answer 1”, “answer 2”, . . . . Instead, structure your report in whichever way you prefer to make it easy to read.

•  Plot the data (the variable num we will denote by X) and examine any prominent features. Comment on your findings. Use linear regression to remove any linear trend or seasonal effects which you believe to be present in the data.  Denote the residuals after removing the trend and/or seasonal effects by Y.