MATH 3820代写、R代做、编程语言调试、代写data留学生

日期：2020-04-29 11:27

1 MATH 3820 coursework

General assessment information

This coursework is included in the assessment for MATH3820, and counts 20% towards

your overall mark for the module.

Your report should be handed in on, or before, Monday 11th May, via my letterbox on

Level 8.

You are encouraged to collaborate with other students, but the work that you hand in

must be done independently.

The analyses in the coursework should be performed using the statistics program R. You

will have an opportunity to work on related problems before the practical session, which

will be held on Tuesday 28th April (12:00-14:00) in E.C.Stonor Cluster 6.68. The data

needed for the coursework are available from Minerva.

If you have any questions about the coursework, please email me.

Background to the problem

A few years ago a university took the decision to record all lectures and provide videos

of the lectures to the students through their Virtual Learning Environment. There is a

concern that the amount of hard drive space to store all the hours of lectures might far

exceed what is currently available. Soon, the university decision makers will need to

decide if it will be necessary to expand the hard drive capacity to enable video storage

for years to come.

An IT specialist has been asked by the university to perform an analysis of some data on

video lengths so that the university decision makers can decide whether to invest in new

disk space. In particular, he/she will get data on the length of videos (rounded to the

nearest 10 seconds) from one lecturer to use in his/her analysis. Video lengths of

individual lectures are controlled by the lecturers, but (prior to starting the analysis) the

IT specialist knows that lectures are supposed to be 50 minutes long and should not

exceed 1 hour.

For the purposes of this coursework, you will assume the role of the IT specialist.

Analyses you need to perform

1. Propose a data generating model for the data that are going to be collected (i.e.

construct a likelihood for the length of videos rounded to the nearest ten seconds)

and consider any assumptions that you are making.

2. Elicit your own beliefs about the parameter(s) of the data generating process, and

produce your prior distribution for the parameter(s) based on your beliefs.

2 MATH 3820 coursework

3. Propose a second prior distribution that might encode ignorance in the

parameter(s).

4. Find the data, which are available on Minerva. Use a sampling method to update

both prior distributions in the light of the data. Calculate the posterior means and

variances based on the different posterior samples.

5. Use each of the two posterior distributions to give a distribution over how long the

next video produced by this lecturer will be.

Writing up

You are required to write a short report outlining the analyses you have performed

(including discussions of how appropriate the techniques were) and explaining the

results.

The report should be aimed at someone who has a basic knowledge of Bayesian statistics

and who needs convincing of your modelling and numerical integration choices.

The report should contain relevant plots such as time series plots and histograms (which

should be explained in the report). These can be copied from R, but the report should not

contain any R commands or other output directly copied and pasted from theR console.

The aim of this practical is to explain the analyses that you have performed and giving R

commands does not do this.

The report should be word processed and should not exceed 6 sides of A4 (single spaced

with at least 11 pt font size) including any plots that you wish to reproduce.

In addition to the report, attach an appendix that includes all of the R commands that

you have used. There is no need to reproduce any plots in the appendix, and the

appendix does not count towards the number of pages in your report.