Section A: Discussion Questions
- Explain the confusion matrix in classification methods and provide an example on how you interpret
the its number? - Give two practical examples on applications of classification methods in supply chain and logistics.
Provide detail explanations. You need to explain why you think classification can be used in those
cases, you do not need to provide data or solvethem. - Assume one of the explanatory variables (named X1) in your logistic regression is a categorical
variable with the following levels: low, average and high, and another explanatory variable (named
X2) is also categorical with the following levels: Sydney, Melbourne and Brisbane. Explain how you
will use them in developing your logistic regression model. How many coefficients you will have in
your final model?
(1.6+2.4+2.4 = 6.4 marks)
Section B: Quantitative Questions - There are 500 client records in the first worksheet of the Excel file (provided for this assessment)
who have shopped many special products from an e-Business website. Each record includes data
on types of product purchased (between 1-5), purchase amount ($), age, gender, family size of the
customer, whether the client has a membership and whether the customer has a discount card.
a) Explain the steps on how to develop a KNN model to predict which customers will spend morethan
$1000. (Write your answer as: Step 1- … Step 2- … and so on. You do not need to run any software
and report the results, for example for Step 1 you can write cleaning the data that means ….)
b) Develop a regression model to predict the spend amount of a new female customer with age of 28
who is living in a family with size 3 and is not a member and hold a discount card type.
(3.2+3.2=6.4 marks) - A company provides maintenance service for washing machines in Victoria. The collected data
are presented in the Excel file (second worksheet).
a) Assume the manager asked you to analyse the data and provide him some insights and
recommendations. The report should not exceed 2 pages.
b) Build a model to predict the repair time for a future booking service than needs to be done by John
and it is an Electrical repair. Do you suggest this service to be assigned to the morning shift or
afternoon shift?
c) What other data you recommend to the manger to be added into this dataset in future for better
analysis and what kind of analysis you think will be useful based onthem.
(3.2+2.4+1.6 = 7.2 marks) - In worksheet 3, a dataset from blood bank is presented. The data are recorded for apheresis blood
donation made by a group of donors of a period of time. The donor ID is unique for each donor. A
donor might have donated more than once in this period. At each donation, the blood total protein
level of the donor has been recorded. Use the dataset to answer the following questions:
a) There are some missing values for blood type. Think how you can fill in the missing values.
Explain your approach (step by step) and also apply your approach and try to fill the missing
value as much as possible in. (save the results in an Excel worksheet in and name it Question 3
Part a.)
b) Calculate the average of total protein for each blood type. Explain your approach (step by step).
Report them in a worksheet and name it Question 3 Part b.
c) Calculate the range of totalprotein for each blood type. Explain your approach (steps by step).
Report them in a worksheet and name it Question 3 Part c.
d) Is total protein declining by age?
e) Present two best visualisation tool for this data that you think provide useful information?
(2+1.2+2+1.2+1.6= 8 marks) - The data presented in worksheet 4 is the results of a 4-year study conducted to assess how
age, weight, and gender influence the risk of diabetes. Risk is interpreted as the probability
(times 100) that the patient will have diabetes over the next 4-yearperiod.
a) What predictive model you suggest to relate risk of diabetes to the person’s age, weight and the
gender. Why?
b) Develop an estimated multiple regression model that relates risk of diabetes to the person’s age,
weight, gender and lifestyle. Present the regression formula as a mathematical equation. Interpret
the coefficients of the regression and comment on the strength of theregression.
c) What is the risk percentage of diabetes over the next 4 years for a 59-year-old man living in a small
town with 72 kg weight?
(3.2+2+2= 7.2 marks) - Matthew has a new job as business analyst. He plans to invest 10 percent of his annual
salary after the tax into a retirement account at the end of every year for the next 30 years.
Suppose that annual return is 5%, and his current salary before tax is 85k which grow 3% per
year. The tax will apply as 15% on the salary up to 50k and it is 20% for the salary interval of
50k and 80k and the tax rate will be 25% for the remaining salary more than 80k (for example
if his salary will be 105k, he is paying 15% tax on his first 50k and 20% in the next 30 k and
25% on his next 25k of his salary). then:
a) Create a spreadsheet which shows Matthew the balance of retirement account for various levels of
annual investments and returns.
b) If Matthew aims to gain $1,100,000 at the end of the 30th year, what percentage of his salary he
should put in the investment annually.
Sample Solution
