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Background

In this group assignment you will once again work on the petrol prices dataset from the individual assignment. However, you will now be given specific key business questions to address and you will be given additional data sources. You must present your results in a 10-page report to be uploaded to Canvas. You should also submit any Python code used to generate your visualisations.

The brief

You are working for a consulting company who have been asked to prepare a report that focuses on two key business questions (KBQs).

1) How do petrol prices faced by consumers in NSW depend on crude oil prices? When crude oil prices rise (or fall) do petrol prices respond immediately or with a lag?

2) Is any such effect observed uniformly throughout NSW, or do petrol prices in areas with (e.g. different levels of socioeconomic disadvantage) respond in different ways to crude oil prices?

Key Information

1. This is a group assignment. You may form groups of three or four students however we strongly recommend groups of three. Instructions to register your group can be found here. Anyone not registered by Friday 6th May will be placed into a group at random.

2. You are required to submit:

1. ONE written report (word or pdf format, through Canvas).

2. Python “.py” or Jupyter Notebook “ipynb” files. You may submit multiple files.

3. The late penalty for the assignment is 5% of the assigned mark per calendar day, starting after 23:59pm on 26 May 2023. We will be lenient with assignments that are a bit late due to slow uploads.

4. The main text of your report (including everything except for possible appendices) should have a maximum of 10 pages in 12-point Times New Roman (or Calibri) fonts and single line spacing, including all the plots, figures and tables (if any). Any cover page, appendices and/or a list of references are NOT counted towards the 10 page limit.

5. By 12^th May you should submit a progress report. This only requires you to submit details of early group meetings (in particular who attended and what each team member’s responsibilities will be). This will not be marked, but will be used as evidence if there are complaints about free riders in group.

Data

Fuel prices (same as assignment 1)

Retail prices of fuel at different service stations are collected and published by the NSW government and made available via the Fuel Check appLinks to an external site.. The data for your assignment were downloaded from data.gov.auLinks to an external site., a platform for sharing open source data. The data contain the following variables:

· Address: Address of the service station selling the petrol.

· Brand: Brand of the service station (e.g. Shell, BP, etc.).

· FuelCode: Type of fuel, (e.g. E10 is Ethanol 10, DL is Diesel). See hereLinks to an external site. for details.

· Postcode: Postal code of service station location.

· Price: Price for one litre of petrol (measured in cents).

· PriceUpdatedDate: Date that price was updated. This can be understood as the date for which petrol was sold at the stated price.

· ServiceStationName: Name of service station (usually Brand and Suburb).

· Suburb: Suburb where service station is located.

The data can be downloaded here Download here. The shapefiles can be downloaded from the links in the individual assignment.

Crude oil prices

The crude oil is a major input in the refining process, therefore its price is a major determinant of petrol prices. The file CrudeOilPrices.csv, contains data on trading of crude oil on global markets, were sourced from Yahoo financeLinks to an external site. and include the following variables:

· Date: The day of trading,

· Open: The opening price of a barrel of crude oil (in $US),

· High: The highest price during the trading day (in $US),

· Low: The lowest price during the trading day (in $US),

· Volume: The amount of crude oil traded (in “barrels” approx 160 Litres).

The data can be downloaded here Download here.

Socio Economic Index for Areas (SEIFA) Data

The socio-economic index for areas is developed by the ABS to measure socio-economic advantage and disadvantage in different areas of Australia. More information can be found hereLinks to an external site.. These data are available in the file SEIFA.csv, are derived from the 2016 census and include the following data.

· 2016 Postal Area (POA) Code: Post code

· Index of Relative Socio-economic Disadvantage Score: A score that measures socio-economic disadvantage (lower scores indicate more disadvantage).

· Index of Relative Socio-economic Disadvantage Decile: Decile of socioeconomic disadvantage score (1 = lowest (i.e. most disadvantaged), 10 = highest (i.e. least disadvantaged)

· Index of Relative Socio-economic Advantage and Disadvantage Score: Similar to above but with indicators for disadvantage and advantage.

· Index of Relative Socio-economic Advantage and Disadvantage Decile: Similar to above but with indicators for disadvantage and advantage.

· Index of Economic Resources Score: Similar to above but with indicators that measure economic resources.

· Index of Economic Resources Decile: Similar to above but with indicators that measure economic resources.

· Index of Education and Occupation Score: Similar to above but with indicators that measure education and occupational status.

· Index of Education and Occupation Decile: Similar to above but with indicators that measure education and occupational status

· Usual Resident Population: Population of post code area.

The data can be downloaded here Download here.

Academic integrity

While the University is aware that the vast majority of students and staff act ethically and honestly, it is opposed to and will not tolerate academic integrity breaches and will treat all allegations seriously.

Further information on academic integrity, and the resources available to all students can be found on the academic integrity pages on the current students website: https://sydney.edu.au/students/academic-integrity.html.

Further information for on research integrity and ethics for postgraduate research students and students undertaking research-focussed coursework such as Honours and capstone research projects can be also be found on the current students website: https://sydney.edu.au/students/research-integrity-ethics.html.

Compliance statement

In submitting this work, I acknowledge I have understood the following:

· I have read and understood the University of Sydney's Academic Integrity Policy 2022.

· The work is substantially my own and where any parts of this work are not my own I have indicated this by acknowledging the source of those parts of the work and enclosed any quoted text in quotation marks.

· I have acknowledged any assistance provided in preparing the work including the use of copy-editing, proof-reading, and automated writing and drawing tools (including artificial intelligence (AI), reference generators, translation software, grammar checkers, but not spell checkers).

· The work has not previously been submitted in part or in full for assessment in another unit unless I have been given permission by my unit of study coordinator to do so.

· The work will be submitted to similarity detection software (Turnitin) and a copy of the work will be retained in Turnitin's paper repository for future similarity checking. Note: work submitted by postgraduate research students for research purposes is not added to Turnitin's paper repository.

· Engaging in plagiarism or academic dishonesty in coursework will, if detected, lead to the University commencing proceedings under the Academic Integrity Policy 2022. and the Academic Integrity Procedures 2022.

· Engaging in plagiarism or academic dishonesty in research-focussed work will lead to the University commencing proceedings under the Research Code of Conduct 2013 and the Academic Integrity Procedures 2022.

· Engaging another person to complete part or all of the submitted work will, if detected, lead to the University commencing proceedings against me for potential student misconduct under the University of Sydney (Student Discipline) Rule 2016.