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Finance Discipline Group
UTS Business School
FINANCIAL METRICS FOR DECISION MAKING – SUMMER 2020
General Instructions and Information
This assignment accounts for 40% of students’ final grade for 25624 Financial Metrics
for Decision Making.
The assignment is to be undertaken individually.
The assignment is due on Friday the 5th of February 2021 (Week 11) by 5pm.
The assignment must be submitted via UTSOnline. You’ll need to provide a written
report and an Excel spreadsheet:
• The written report must be self-contained and formatted as a PDF file.
• Excel files will also be examined and will constitute 20% of the value of the
assignment. The Excel file should include all calculations.
The scope of this assignment is limited to  pages not including appendices and cover
sheet. Use standard fonts (think Calibri, Times New Roman, Arial) and standard font
sizes. There is no specific word count.
You are encouraged to use figures and tables when reporting your results.
The file names, for both the report and the Excel spreadsheet, will take the form:
“Name – Student number”. For example, if your name is Jane Doe and your student
number is 12345, then your file name will be “Jane Doe - 12345”. Please don’t write
the words “name”, “student number” or anything else in the file name.
All assignment-related questions should be posted to the Discussion Board on
This assessment will be graded on the quality of both, the written report and the
quantitative analysis in Excel.
Marks will be awarded 70% for content and analysis, and 30% for effectiveness of
communication and presentation.
Late submissions will be allocated a mark of zero with no exceptions unless via special
In the Assignment folder on UTSOnline, you’ll find the following files:
§ Cover Sheet: is the cover sheet you’ll need to fill in, sign, and submit along with your
§ Data: this Excel spreadsheet contains the following worksheets:
Cover: you’ll need provide your student details here.
Part 1 to Part 4: these worksheets contain the data (when applicable) for each
part and should be used to perform all relevant data analysis required.
Part 1 – Hypothesis Testing [10 marks]
The national average annual salary for a campus manager is $89,000 a year. A state official
took a sample of 25 campus managers in the state of New South Wales (NSW) to learn about
salaries in the state and see if they differed from the national average.
The data for this question is provided in the worksheet named ‘Part 1’.
a. [5 marks] Formulate the null and alternative hypotheses that can be used to
determine whether the annual salary mean of a campus manager in NSW differs from
the national mean of $89,000.
b. [5 marks] What is the p-value for your hypothesis test in part (a)? At a 5% significance
level, can your null hypothesis be rejected? What is your conclusion?
Part 2 – Modelling [40 marks]
Your boss, a real estate business manager, has approached you for financial advice. She is
interested in either purchasing or leasing a new car for her personal use. Aware of your
financial expertise, she has asked you to develop a Spreadsheet Model that allows her to
decide whether to buy or lease the vehicle.
The retail price of the car she is interested in is $50,000.
In the Buy Scenario, your boss would like to purchase the car by making an initial down
payment of $15,000 dollars and finance the difference with a conventional car loan to be
repaid monthly for 3-years at a 5% interest rate. The following table summarises the relevant
information for the Buy Scenario.
In the Lease Scenario, there is no initial down payment. Instead, your boss would like to use
a Finance Lease to rent the car for 3 years. At the end of this 3-year period, she plans to
purchase the car from the lease financier (lessor) by paying a residual value of $25,000. In
this scenario, to rent the car, your boss would have to pay a monthly rent of $850 for 3 years.
The following table summarises the relevant information for the Lease Scenario.
Note: A Finance Lease is a common way people can use a car without actually buying it. Under
a Finance Lease, the car belongs to the financier (lessor) who rents it out to the borrower
(lessee) in exchange for monthly instalments. At the end of the lease term, the lessee has the
option to claim ownership of the car by paying a residual value.
a. [5 marks] Lay out the decision-making problem, the alternatives, and the overall
criteria you would use to evaluate the different alternatives.
b. [5 marks] Carefully establish all the inputs and assumptions you would include in the
Spreadsheet Model for each scenario. If you include inputs/variables other than the
ones provided (e.g. interest rate on savings), justify your choices based on data from
the Australian market.
c. [10 marks] Based on your answers to a) and b), build a Spreadsheet Model which helps
your boss decide whether to buy or lease the vehicle. Make your spreadsheet selfexplanatory.
d. [5 marks] Perform What-If analysis for at least one of your inputs (e.g. down payment).
That is, show what would happen to your model’s output at, at least, three different
values of the chosen input. In your spreadsheet, highlight the section you would
present to your boss to help her with her decision-making problem.
e. [5 marks] Of all the inputs included in your model, which one do you think is the most
important in determining whether buying or leasing is the best option for your boss?
Provide an explanation.
f. [5 marks] Describe the model’s limitations and/or aspects that could be improved.
What other factors haven’t been considered?
g. [5 marks] Are there any cognitive biases you would suggest your boss to be aware of
when finally making her decision?
Part 3 – Simple Linear Regression [20 marks]
The Toyota Hilux is the top selling car in Australia. The price of a previously owned Hilux
depends on many factors, including the number kilometres (kms) travelled. To investigate the
relationship between a car’s kms and its sales price, data was collected on a sample of 20
used Hilux in Sydney.
The data for this question is provided in the worksheet named ‘Part 3’.
a. [2 marks] Create a scatter plot for this data with kms as the independent variable.
What does the scatter plot indicate about the relationship between price and kms?
b. [5 marks] Estimate a simple linear regression model with price as the dependent
variable and kms as the independent variable. What is the estimated regression model
c. [5 marks] Test whether each of the regression parameters (intercept and coefficient)
is equal to zero at a 5% significance level. Interpret the coefficients of the estimated
regression parameters and discuss whether these interpretations are reasonable.
d. [4 marks] Using the model estimated in part (b), calculate the predicted price for each
of the cars in the sample. Based on the difference between the true and predicted
prices, identify the two cars that were the biggest bargains.
e. [4 marks] Suppose that you are considering purchasing a previously owned Hilux that
has been driven 100,000 kms. Use the model estimated in part (b) to predict the price
for this car. Is this the price you would offer the seller?
Part 4 – Multiple Linear Regression [30 marks]
A financial institution has a large dataset of information provided by its customers when
they apply for a credit card. This customer information includes the following variables:
• Annual household income (in thousands of dollars)
• Household size (number of people)
• Number of years of post-high school education
• Number of hours per week watching television
In addition, the financial institution has records of the credit card charges accrued by each
customer over the past year.
The data for this question is provided in the worksheet named ‘Part 4’.
a. [5 marks] Plot histograms to contrast the distribution of annual credit card charges for
1) People with zero years of post-high school education vs. People with at least 1 year
of post-high school education, and 2) Female vs. Male. Describe the overall shape of
each histogram and comment on any observable differences.
b. [10 marks] Estimate a multiple linear regression model in which the dependent
variable is the credit card charges accrued by each customer in the data over the past
year, and the independent variables are all the variables the financial institution
collected when the customer first applied for a credit card (e.g. annual household
income). What is the estimated regression model (equation)?
a. Hint: For Gender, create a dummy variable that takes 1 if the customer is
female and 0 if male.
c. [15 marks] Interpret each of the regression coefficients and comment on both their
economic and statistical significance. For each significant regressor (at a 5%
significance level) provide a potential explanation for its statistical relationship with
the dependent variable.