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Quantitative Methods (M), (UAC) & (H) Semester 2, 2023

Major Project

Project Instructions

· The project is separated into 3 interrelated tasks. All 3 tasks are due at the same time and should be prepared as a single report. You have a one-month duration to finish this major assignment. The deadline for the final report is Monday, 6th November, at 5pm Adelaide time (GMT +09:30). For students with an access plan, the extended deadline is Saturday, 11th November, at 5pm Adelaide time (GMT +09:30).

· Please submit your reports online via MyUni by uploading a single file in Microsoft Word format (either .doc or .docx) through the Assignments tab and associated link. If your assignment is late, please email it to Xiaohui ([email protected]) and Ethan ([email protected]) personally– Do not submit via MyUni if your assignment is late.

· Tasks 1, 2 and 3 most closely relate to Topics 2, 8 and 9 as detailed in the course outline.

· This is an individual project (you can discuss it with other students but everyone needs to make an individual report submission).

· The project comprises 50% of your final Quantitative Methods (M) grade. The Grading Rubric is also available on MyUni, giving additional detail on the assessment breakdown:

§ Headline Regression Model Derivation 10%

§ Task 1: Data Summary 20%

§ Task 2: Regression Model – Build and Use 40%

§ Task 3: Regression Model – Evaluation 20%

§ Report Structure and Written Presentation Quality 10%

· Your final report will be processed through Turnitin (including Turnitin’s AI writing indicator) as a check for plagiarism so please ensure that you only present your own work. In addition, your final report needs to meet all of the following criteria:

§ Font: Times New Roman

§ Font size: 12 point

§ Page margins: 2.54cm all around (Normal)

§ Page Limit: 8 pages only (A4, single sided). The page limit should not be exceeded for any reason (i.e., not for appendices, raw data, STATA coding)

Appropriate font, font size, page margins, and page limit are all graded against ‘Report Structure and Written Presentation Quality’ (10%).

· Further Advice:

§ In total, your title, table of contents, and a short introduction should take 1 page (max).

§ Ensure your report is free from spelling and grammatical errors.

§ Ensure your report is clear and well-structured.

§ Ensure your report is written in context and answers questions in context.

§ Make sure it is clear which model is your “Headline Regression Model” (Final answer)


Junction is a small town with two suburbs. The data file “Major Project – Data Set” contains data on 535 houses sold in Junction between 2018 and 2023. This data includes the price at which the house was sold, which of two agents sold the house: Big Block (B&B) or Rapid Realty (R&R) (all houses are sold through an agent by law), the year in which the house was sold as well as data on various characteristics of each house sold (age, size, number of stories etc.). These characteristics serve as possible explanatory variables of sale price.

Data definitions follow:

OBS

=   observation

AGE

=   age of house in years

SHOPS

=   1 if house is close to a shopping precinct, 0 otherwise

CRIME

=   crime rate of the suburb within which the house is located

TOWN

=   distance in kilometres to the town centre

STORIES

=   number of dwelling stories

OCEAN

=   1 if house has an ocean view, 0 otherwise

POOL

=   1 if house has a pool, 0 otherwise

PRICE

=   price at which the house was sold (in dollars)

AGENT

=   selling agent – “B&B” (0) or “R&R” (1)

SIZE

=   size of the house in square metres

SUBURB

=   Mayfair (0) or Claygate (1)

TENNIS

=   1 if house has a tennis court, 0 otherwise

STUDY

=  1 if house has a separate study room, 0 otherwise

SOLD

=   year of last sale (2018 to 2023)

Your tasks

Task 1 – 20% of project grade (recommended length: 2 pages)

You are required to provide a comprehensive summary of the data set contained in the “Major Project – Data Set” file. How you choose to do this is entirely at your discretion. However, it is recommended that you consider using both summary statistic and graphical methods while also noting any peculiarities within the data set.

Task 2 (including Headline Regression Model) – 50% of project grade (recommended length: 3 pages)

You have been hired by Jessica, the wealthy owner of a house on Elm Street in Junction (not included in the data set) to predict the price at which her house will sell. Her house has two stories, is in Mayfair, is 172 square metres large, is not near a shopping precinct and is 16 km from the town centre. She estimates that the house is about 8 years old and in a low crime area according to her experiences. Jessica inherited the house from her uncle and is therefore unsure when it was last sold. Some other features of the property can be seen below: