ECON20003 – QUANTITATIVE METHODS 2 Second Semester, 2023 Assignment 3
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ECON20003 – QUANTITATIVE METHODS 2
Second Semester, 2023
Assignment 3
Due date and time: Wednesday 18 October, 10:00AM
Please read the following instructions carefully before starting to work on the assignment.
• This assignment is worth 5% of the final grade for QM2.
• This assignment must be submitted online via the LMS by 10:00AM on Wednesday 18 October.
• Students may work alone and submit their own assignment answers, if they wish to do so, or they can work on the assignment in pairs. In the latter case, each assignment pair must submit only one set of assignment answers and both students of the pair will receive the same mark for their assignment. It is not allowed to form assignment groups of more than two students.
• Please note that the assignment submission process has two stages: 1. Registering your assignment group (only if you work in a pair), and 2. Submitting the assignment online via the LMS.
Students who intend to work on the assignment in pairs must register their groups. To do so, click the “People” link and then the Groups tab in the Canvas course navigation menu. The group names (set by default) are A3 1, A3 2, A3 3, etc. Every assignment pair MUST register as one of these created groups for submitting the assignment and not create a new group.
Students making individual submissions do not need to register.
• Answer the assignment questions using Microsoft Word. Make sure to include a cover page in the document with the student ID, the name, and the tutorial group of each group member. Convert the whole file to PDF before submitting it online via the LMS. After you submit, you will be given an opportunity to view your Turnitin score. There is no threshold score you need to meet but make note of any similarities with external sources, and in particular large portions of matching text. Review your assignment accordingly. Assignments that are flagged for plagiarism from external sources by Turnitin will be reviewed by the unit coordinator for plagiarism.
• Do not forget to preview your assignment after uploading it on the LMS to ensure that you have indeed uploaded the correct and complete assignment, and that its formatting is in order as in the original document. Submissions that are late because of formatting issues or because a version is incomplete, will not be accepted.
Assignment Tasks and Questions
(35 marks: 5 + 4 + 2 + 2 + 3 + 4 + 3 + 4 + 2 + 3 + 3)
You have been hired to consult an online real-estate start-up and they interested in the factors that determine auction prices of residential real-estate in the Metropolitan
Melbourne area.
They have provided you with historical ‘web scrapped’ data from domain.com.au.
You can find this data in the Excel file ‘ Dataset Assignment 3.xlsx’ .
Database Key Details*
Suburb: Suburb name
Address: House address
Rooms: Number of rooms
Price: Price in dollars
Distance: Distance from CBD
Postcode: Suburb postcode
Car: Number of carspots
BuildingArea: Building Size
Regionname: General Region (West, North West, North, Northeast ...etc)
*Data Acknowledgement: Tony Pinto “Melbourne housing clearance data from Jan 2016”
(a) Conduct a preliminary analysis of the dataset. Start with a univariate description of the variables both numerically and graphically. Then, using bivariate analysis focus on analysing how the different characteristics of real-estate properties affect
the house Price.
(b) Suppose you intend to estimate a multiple linear regression model of Price using this dataset. Choose variables that are sensible predictors of Price and write out the population regression equation using the actual variable names. Do you expect the slope parameters to be positive or negative? Explain your expectations, or if you are undecided, explain why.
(c) Estimate the population regression model in part (b) with R. Present your raw output. Write out the sample regression equation using the actual variable names.
(d) Interpret the coefficient of determination. Is the model likely to be useful in predicting Price? Explain your answer.
(e) Test the overall significance of the model at the 5% significance level. What are the hypotheses, the statistical decision and the conclusion? Be precise.
(f) Test appropriate hypotheses concerning the significance of the slope coefficients using t-tests at the 5% significance level.
(g) What do the statistically significant slope coefficients suggest? Interpret your results.
(h) Does the homoskedasticity and normality assumption seem to be satisfied? Whyor why not?
(i) Determine whether imperfect multicollinearity is a concern in the given model? If multicollinearity is a concern, propose a solution to address the problem.
(j) Do you think your model is well specified in terms of its polynomial terms? What relationships might benefit from a non-linear structure? Consider how such non- linearities ought to feature in the model. Then conduct a Ramsey Regression Equation Specification Error Test (RESET) and interpret its results.
(k) Show, with a single example, how you can use your model to make a point and interval prediction using a combination of values for your independent variables.
Interpret your results.
2023-10-18