# 这是一篇来自英国的关于完成以下单独任务数据分析和统计方法的代写

XYZ is a student from LSE with the profifile pseudo-randomly generated using the R function XYZprofile (with the argument being the numerical value of your 9-digit LSE Student ID).

This function is contained in the fifile XYZprofile.r on Moodle (under the Section

“Project”). For example, if your LSE student ID is 202212345, then you should use the following code:

> # Replace the number below by your LSE ID

> ID = 202212345

> # Then copy XYZprofile.r into your R working directory

> source(“XYZprofile.r”)

> # Now run the function XYZprofile with argument ID

> XYZprofile(ID)

You should get the following output:

The profile of XYZ:

– Age: 25

– Gender: Female

1.2 Problem Description

XYZ has been learning driving for some time, and is thinking of taking the practical car test in UK.1 . There are two sensible options for XYZ:

1. either take the practical test at the nearest test centre to his/her home;
2. or take it at the nearest test centre to the LSE.

Note that in XYZ’s generated profifile, the entry “Home address” actually gives the name of the nearest test driving test centre to XYZ’s home. In addition, the test centre closest to the LSE is Wood Green (London). 2 XYZ thinks his/her driving skill is about average. It is widely believed that the driving test routes around some centres are probably more diffiffifficult than others (e.g. there are far less bus lanes, roundabouts and cyclists in rural areas than in London).

XYZ knows that you (who is his/her best friend) are taking ST447 this term. XYZ wants to rely on your data analysis skills, and would like you to answer the following questions:

1. What is XYZ’s expected passing rate at the nearest test centre to his/her home?
2. What is XYZ’s expected passing rate at the nearest test centre to the LSE?
3. Of these two locations, where should XYZ take the test? Is there any evidence to (statistically) support this suggestion?

2 Project Details

2.1 Data Source

The dataset DVSA1203 is available at

https://www.gov.uk/government/statistical-data-sets/car-driving-test-data-by-test-centre which contains information on car pass rates by age (17 to 25 year olds), gender, year (2007-2022) and test centre.

This dataset is also available on Moodle (under the Section “Project”). Note that this dataset is of “*.ods” format, so some data preparation might be required.

2.2 Methods

Our intention is to simulate a real-life scenario, so this problem is open-ended. You could choose whatever method you believe that makes most sense. For example, you could either combine many years of data, or just based your analysis on data from a particular year,or investigate the yearly trend, etc; you could use logistic regression or just the Wald test,etc. However, no matter what you choose to do, you will need to brieflfly justify your choice(of data, method, etc) in the report.

2.3 Final Report

Your fifinal report needs to be understood by a non-expert in statistics (as XYZ has limited previous training in statistics).

1. the profifile of XYZ you used for this analysis;
2. brieflfly explanation of the data you used, methodology, as well as the assumptions behind the scene;
1. the relevant R code (with enough comments) so that XYZ could mimic your analysis;
2. the strengths (and potential weaknesses) of your approach to this particular problem.

Your report should be word-processed by, for example, Microsoft word, latex or Rmarkdown). There is a strict page limit for the report (maximum 8 A4 sides, including fifigures,tables and relevant R code). You should use an 11 point standard font (for example, times new roman) and 1.5 spacing.

Note: The limit of 8 pages is the upper bound of the length. A well-structured and clearly-written report can be much shorter than that.

Please ensure that your report has a title and your 5-digit candidate number (i.e. the number to be used in exams, available on LfY). It should be anonymous, as your name must not appear anywhere in the report.

Save your report as ”xxxxx.pdf”, where xxxxx stands for your 5-digit candidate number,and submit it to Moodle by 16:00 on Friday, 2 December 2022.

Late submission entails penalties: 5 marks (out of maximum 100) will be deducted for every half-day (12 hours). This will result in a maximum penalty of 10 marks for the fifirst 24 hours. Then further 5 marks will be deducted per 24 hour period thereafter.

Submissions after 9 December 2022 cannot be accepted.

3.2 Assessment Criteria

We will mark your report by its correctness, concreteness, clarity and conciseness.

In addition, you could show a critical awareness of any weaknesses in the analysis you present and discuss possible extensions and improvements. See also a separate fifile ‘Assessment Criteria’ for more details.

3.3 Plagiarism

Plagiarism is taking someone else’s work or ideas and passing them offff as your own (adapted from Concise Oxford Dictionary defifinition). This arises in course work as sections of text lifted from books, internet sources or someone else’s work and submitted as your own work.

This is a very serious offffense that is quite easy to detect. Plagiarism will result in instant failure (mark 0).

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