STAT7055 Introductory Statistics for Business and Finance
Introductory Statistics for Business and Finance
STAT7055 Introductory Statistics for Business and Finance
Writing Time: 180 minutes
Reading Time: 15 minutes
Exam Conditions:
Central examination.
Students must return the examination paper at the end of the examination.
This examination paper is not available to the ANU Library archives.
Materials Permitted in the Exam Venue:
(No electronic aids are permitted, e.g., laptops, phones).
Calculator (non-programmable).
Two A4 pages with notes on both sides.
Unannotated paper-based dictionary (no approval required).
Materials to be Supplied to Students:
Script book.
Scribble paper.
Instructions to Students:
Please write your student number in the space provided on the front of the script book.
Attempt all 5 questions.
Start your solution to each question on a new page and clearly label each solution with the corresponding
question number.
To ensure full marks show all the steps in working out your solutions. Marks may be deducted for failure
to show working or formulae.
Selected statistical tables are attached to the back of the examination paper.
If a required degree of freedom is not listed in a statistical table, please use the closest degree of freedom.
Unless otherwise stated, use a significance level of α = 5%.
Round all numeric answers to 4 decimal places.
Question: 1 2 3 4 5 Total
Marks: 21 21 18 21 22 103
Question 1 [21 marks]
There are many things which can affect the price of a second hand car. Data was
collected on 105 second hand car sales. A multiple linear regression model was fitted
with sale price as the dependent variable (Y ), and the odometer reading (X1), the
odometer reading squared (X21 ), the age (X2) and an indicator of whether the car has
an automatic transmission (Z = 1 if the car has an automatic transmission and Z = 0
otherwise) as the independent variables. That is, the following model was fitted:
Y = β0 + β1X1 + β2X
2
1 + β3X2 + β4Z +
Note that sale price was measured in thousands of dollars (e.g., Y = 32 corresponds to a
sale price of 32 000 dollars), odometer reading was measured in thousands of kilometres
(e.g., X1 = 19.1 corresponds to 19 100 kilometres) and age was measures in years. The
regression output, which includes some missing entries, is displayed below:
Predictor Coef SE Coef T p-value
Intercept 57.5629 5.7896 9.94 0.0000
Odometer ? ? −2.03 0.0455
Odometer2 ? ? −1.25 0.2136
Age −0.1216 0.1441 −0.84 0.4009
Z −0.1707 0.6040 −0.28 0.7780
Analysis of Variance
Source DF SS MS F p-value
Regression ? ? ? ? ?
Residual Error ? ? ?
Total ? 5783.875