Task Details/Description:

In your work as an analyst, you have been asked to analyse data on delivery

times for three pizza restaurants. You have been provided with a sample of 45

deliveries which includes data on the delivery time, the distance, and the

delivering (bsm952 bfm114 resit data.xlsx):

1.   Produce a single, neat, formatted table which features descriptive statistics around delivery times for each of the restaurants (you should use R to        generate the descriptive statistics, but can use your word processor to       generate your table) (5 marks)

2.   Produce an appropriately labelled visualisation which demonstrates the   overall distribution of delivery times (including all restaurants). Briefly       comment on the characteristics of the distribution and whether it matches your expectations. (10 marks)

Full marks will only be awarded for visualisations which are appropriately

labelled and presented.

3.   Use an appropriate test to determine whether, from our data, we can reject the idea that restaurant 1 and restaurant 3 deliver in the same amount of   time. Briefly explain your process and results. (25 marks)

The dataset also contains details on the distance of each delivery in the sample:

4.   Considering the entire sample, produce an appropriately labelled scatter plot with delivery time on the vertical axis and distance on the horizontal. Comment on any patterns you observe. (10 marks)

Full marks will only be awarded for visualisations which are appropriately labelled and presented.

5.   Using Ordinary Least Squares regression analysis, estimate a model with delivery time as the dependent variable and distance as the independent variable:

a.   Present your results in a table (10 marks)

b.   Comment on your results. Specifically, do they match your expectations? (15 marks)

6.   Run a second regression analysis, this time including variables which         reflect the pizza supplier. Report the results in a table and comment on       whether the inclusion of these variables improves the 'fit' of your model. (25 marks)

(Hint: think about the nature of the restaurant variable when running your regression)

The key to success in this assignment is to demonstrate that you understand the   answers you are presenting— make sure your explanations are clear and concise, and that any statistical terms you use are correctly employed (misuse of statistical  terminology will be penalised).  Presentation is important, so ensure that your        formulae, tables, and diagrams are properly presented, and that you follow the      rest of the submission guidelines .

Module Learning Outcomes Assessed:

This assessment covers both technical and theoretical material from the first part of the course. It partially assesses all learning outcomes associated with the       module:

Upon successful completion of the module, the typical student should:

•    Be able to identify, collect, describe, present, and manage economic data and  datasets from  a  range  of sources  to  address  a  specific  research question

•    Be  able  to  differentiate  between  sample  and  population  data,  and  to understand the key principles in inferential statistics

•    Be  able  to  identify  appropriate  statistical  methods  for  analysing  data relevant to the course content

•    Be able to demonstrate the ability to appropriately select, and use computer software to implement, a range of econometric approaches relevant to the course content

•    Be able to critically interpret the results of econometric analysis and use them to provide informed investment and policy recommendations

Presentation Requirements:

This is a postgraduate assignment, and the standard of your presentation should  reflect this. Work should be carried out using R, writing should be formal with       correct terminology, tables and diagrams should be neatly presented, formulae    should be written using a proper equation editor. Submissions which do not meet  the presentation requirements expected of postgraduate work will be penalised.

You must submit your answers as a single pdf file, via the Turnitin submission link which will be made available on Blackboard. The file you submit should consist of two parts:

1)  The main written answer which should feature all relevant results,          explanations, and visualisations. If you are unsure whether to include an explanation, you are encouraged to include one.

2)  Your clean R code, copied and pasted into an appendix. This is not a      dump of your code, but it is a tidy, complete, list of all the commands you used to elicit your answer.

No individual answer should require more than 200 words (this is a limit, not a      target) and excessive length will be penalised. Note that well-chosen                     mathematical notation or diagrams are an efficient way to circumvent the need for lengthy explanation.

General guidelines:

This is a postgraduate assessment and, as such, it is expected that you uphold appropriate standards of presentation:

•    Graphs and tables should be produced using computer software and should not be included as screengrabs of unadulterated R output

•    Formulae and equations should be produced using an appropriate equation editor with correct subscripts and accents

•    Language, descriptions, and explanations should be formal and should not resort to simplistic or childlike exposition

•    Short written sections should be concise and should be no more than 200 words each.

•    Text should be written in a non-serif font (Calibri, Arial, Helvetica), size 11,

1.5 line spacing.

•    R output is acceptable for reporting of regression output, but please ensure it is formatted in such a way that it is legible.

•    It is expected that your work is legible, written in formal academic language, and spelling and grammar checked.

Academic integrity

It is expected that you uphold the expected standards of academic integrity:

•   You produce your own work.

•   You do not plagiarise and that any sources you use in your answer are properly acknowledged in a properly formatted bibliography (a list of    sources relied upon, not just a list of sources specifically cited).

•    This is individual assessed work: conversations and collaboration with colleagues is kept at a general level and you do not share or generate specific answers to questions with one another.

Failure to adhere to guidelines or standards of academic integrity may result in a lower mark, a mark of zero for this work, or a mark of zero for the whole module.

Submission Date & Time:

Assessment available 12 noon, Tuesday 1st  November 2022

Assessment due 12 noon, Friday 4th  November 2022

Assessment Weighting for the Module:

20%

Assessment Criteria

This module is assessed via:

Mid-term assessment (20%) (this assessment)

Final project (80%)