Quantitative Methods (M) & (UAC) Semester 2, 2025
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. The final report due date is Friday the 31st of October 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 George personally ([email protected]) – 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 a group project. You are only permitted to complete it in groups of 2 or 3 students. Individual submissions, and those from groups larger than 3, are not permitted. Importantly, make sure that you self-allocate into a numbered group on MyUni (you will be shown how to do this during classes in Week 9). When submitting your report, only 1 student from each group should upload the report to MyUni, under your unique group, with the names and student IDs ofall group members included on the front page.
● The project comprises 15% of your final Quantitative Methods (M) grade. The Grading Rubric is also available on MyUni, giving additional detail on the assessment breakdown.
● 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: 10 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 2020 and 2025. This data includes the price at which the house was sold, which of two agents sold the house (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 — “W&M” (0) or “A&B” (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
SOLD = year of last sale (2020 to 2025)
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: 4 pages)
You have been hired by Jenny, 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 Claygate, is 164 square metres large, is not near a shopping precinct and is 10 km from the town centre. She estimates that the house is about 11 years old and in a low crime area according to her experiences. Jenny 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:
You are expected to build a regression model of house prices. In doing so, make sure that you use an appropriate number of predictors to develop your estimates. Once you have constructed an appropriate model, use it to obtain and provide for Jenny’s house:
1. A point prediction of the sales price which it can be expected to fetch
2. A 95% interval prediction for this sale price
3. An estimate of the marginal effect of house size on this sale price
4. Financial advice on whether Jenny should use “W&M” or “A&B” to sell her house. “W&M” charges a commission of 2.8% whereas “A&B” charges a commission of 3.5% of the final sale price.
Jenny, who claims to have some knowledge of regression analysis, has stressed that she thinks you should use a regression model with an R2 of at least 88%.
Note: Task 1 directed you to take note of any peculiarities in the data set. There are other additional errors in the data set that you may not have picked up on in Task 1. These will only become clear to you once you start working on Task 2. Several problems can result if you fail to handle these issues correctly, so be mindful to address them, both in your regression application as well as your final report. If resolving any of the errors in the dataset requires you to make assumptions, make sure to clearly state your reasoning and approach in your report.
Task 3 - 20% of project grade (recommended length: 3 pages)
Please provide a reflective discussion on how you executed Task 2 of the project above. Specifically, consider the following:
1. Verify that your regression model does not suffer from any misspecification errors and provide the relevant regression diagnostics which support your findings.
2. If you found that your model is in fact partially misspecified in part (1) of Task 3 above, explain what you did to ensure that the misspecification only has a minimal impact on your results in Task 2 above. That is, explain how you corrected any misspecifications that occurred during your modelling.
3. Were there any other oddities in the data set or your model? Explain.
4. Is there anything else worth mentioning which is relevant to your work or to your results for Jenny?