ECMT1010 Introduction to Economic Statistics
Due: 11.59PM Friday 4 November 2022
Instructions
i. The consequences of engaging in plagiarism and academic dishonesty, and the process by
which they are determined and applied, are set out in the Academic Honesty in Coursework
Policy 2015 available at http://sydney.edu.au/policies (enter ‘academic honesty’ in
Search).
ii. Enter answers using the Word template available under the ECMT1010 Canvas module ‘As-
signment’.
iii. Save your answers as a .DOCX or .PDF file named 123456789.docx where 123456789 is
your 9-digit University of Sydney SID. Do not put your name on your answers. Do not include
a cover sheet.
iv. Submit the electronic copy of your answers under the Canvas module ‘Assignment’. Work
not submitted on or before the due date is subject to a penalty of 5% per calendar day late.
Work submitted more than 10 days after the due date, or after the return date, will receive a
mark of 0.
v. Use your assigned data set (see below) available under the Canvas module ‘Assignment’.
Enter your data set number (#) using the box provided in the Word template. Use of the
wrong data set will automatically be investigated for academic dishonesty.
vi. There are 10 questions worth 2 marks each for a maximum of 20 marks. The assignment is
anonymously graded (provided you don’t put your name on it).
vii. Answer all questions. Show numerical answers to 3 decimal places. Carry out all tests using
a 5% level of significance. There is no need to give any detail on computation.
viii. When communicating statistical results, it is important to be accurate and concise. Keep
your comments, conclusions, comparisons, etc., to one or two sentences. Excessively long
responses indicate a lack of understanding and will be penalised accordingly.
Page 1 of 3
Aim: The assignment illustrates the use of various statistical methods and software (e.g., Excel,
StatKey) to analyze economic data.
Data description: Your assigned data set consists of automobile price, mileage, and age infor-
mation from a randomly-selected sample of 20 used Ford Mustangs (https://en.wikipedia.
org/wiki/Ford_Mustang).
Your assigned data set is available under the Canvas module ‘Assignment’ in the Excel file
Mustangs#.csv (where # is the last digit of your SID). The file contains 3 columns and
21 rows.
The first row contains the variable names; the remaining rows contain the automobile data.
The Miles column identifies the total mileage (in thousands of miles) displayed on the
odometer at the date of sale, Age is the age of the car in years (calculated as the difference
between the year of sale and year of production), and Price is the final sale price of the
car (in thousands of US dollars).
QUESTIONS
1. Using appropriate software, produce a scatterplot of Age against Miles in your sample. Com-
pute the sample correlation. What do you think is the reason for the association between the
two variables? [2 marks]
2. Set up the null and alternative hypotheses to test whether there is a statistically significant
linear association between Age and Miles, taking care to define your notation clearly. [2
marks]
3. Test whether there is a statistically significant linear association between Age and Miles, show-
ing all your steps and clearly stating your conclusion. [2 marks]
The U.S. Department of Transportation’s Federal Highway Administration states the average per-
son drives around 13,500 miles every year.
4. Use your data to test whether your data support the Department of Transportation’s hypothe-
sis. State your assumptions, set up the null and alternative hypotheses, carry out the test, and
state your conclusion. [2 marks]
5. Is there any reason to doubt your test result on the average mileage? Explain. [2 marks]
To estimate the relationship between Mustang price and mileage you set up the appropriate re-
gression model.
6. Write down the population regression model taking care to define your notation clearly. Using
appropriate software, estimate the regression model and report your results. [2 marks]
7. Interpret the regression slope estimate. [2 marks]
Page 2 of 3
8. Test whether the independent variable is an effective predictor in the estimated regression
model. Make sure you report your null and alternative hypotheses, the test statistic, decision
rule, and conclusion to the test. [2 marks]
9. Suppose instead of estimating the relationship between price and mileage you estimated the
relationship between price and vehicle age. Using appropriate software, estimate the regres-
sion model and report your results. [2 marks]
10. In statistics, a proxy variable is a variable that is not in itself directly relevant, but that serves
in place of an unobservable or immeasurable variable. In view of your analysis above, do you
think that vehicle age is a suitable proxy for mileage? Explain. [2 marks]
Due: 11.59PM Friday 4 November 2022