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COMM1190: DATA, INSIGHTS, AND DECISIONS

创建日期：2024-05-31 17:49:18

所属分类：计算机computer science

标签： COMM1190

DATA, INSIGHTS, AND DECISIONS

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COMM1190: DATA, INSIGHTS, AND DECISIONS

PRACTICE QUESTIONS
QUESTION 1 (30 marks)
PART A 18 MARKS
Concerning the charts below, answer all of the following questions.
a) The bar chart below presents the changes in the variable “Attrition” based on the
two variables “Yearsatcompany” and “Gender”. Summarise two facts based on
your interpretation of this bar chart.
Note: “Attrition” is a categorical variable (yes/no), denoting if an employee leaves
the company or not; “Yearsatcompany” is a numerical variable, denoting the
number of years an employee has worked at the company; “Gender” is a
categorical variable (Female/Male).
[max 120 words] (8 marks)
b) The scatter plot below presents the correlation between the horsepower of a car
and its capacity to travel on the highway. Based on the chart, formulate a
descriptive problem, a predictive problem, and a prescriptive problem that
can be addressed using the scatter plot below. Note: “Horsepower” denotes the
power that a car engine produces; “MPG(Highway)” denotes how far a car can
travel for every gallon of fuel it uses on the highway.
[max 180 words] (10 marks)
PAGE 2 OF 8
PART B 12 MARKS
Which features of this graph are redundant or irrelevant?
[max 200 words]
PAGE 3 OF 8
QUESTION 2 (35 marks)
PART A 16 MARKS
You are examining the relationship between the concentration of substance A from
measurements of peak area and the percentage of colour B. You have observed the
following data points: (x_i, y_i) where i=1,2,…,n, and x_i and y_i represent the
percentage of colour B and the concentration of substance A, respectively. Here, 0 ≤
x_i ≤1 and x_i=0.5 means that the percentage of colour B is 50%.
Concerning the information above, answer all of the following questions.
a) Suppose you want to fit a simple linear regression model to the dataset by treating
the percentage of colour B as the predictor and the concentration of substance A
from measurements of peak area as the response. Write down the mathematical
equation of a simple linear regression model.
(3 marks)
b) The following table presents some of the statistics from the above fit (the linear
regression model):
Coefficient
Estimate
Standard
Error
t-statistics p-value
Intercept 0.0729 0.0279 2.6129 0.017
Percentage
of Color B
10.77 0.27 39.8889 0.000
If you want to test whether there is a relationship between the predictor (Percentage
of Color B) and the response (Concentration of Substance A from Measurements
of Peak Area), what is the null hypothesis, and what is your conclusion based on
the output in the above table? Justify your conclusion.
[max 60 words] (4 marks)
c) Based on the output in the table in Q2b), provide an interpretation of the coefficient
associated with the Percentage of Colour B.
[max 50 words] (3 marks)
d) To assess the quality of the fit of the linear regression model, you want to examine
whether the residuals for the data on the concentration of substance A from
measurements of peak area follow a normal distribution. Name one graphical
method from this course that you can use to perform this task and briefly describe
in words how you can visually check this.
Note: residuals refer to the differences between the observed values and the fitted
values using the above linear regression model.
[max 60 words] (3 marks)
PAGE 4 OF 8
e) There are two samples: the percentage of colour B in sample 1 is 10% and the
percentage of colour B in sample 2 is 50%. Compare the average concentration of
substance A from measurements of peak area in the two samples by calculating
the ratio of them based on the fitted linear regression model.
(3 marks)
PART B 19 marks
A medical experiment has been carried out to build a model for predicting a
deformation D in young patients after a certain type of medical surgery. The dataset
includes the following information for each patient under study: D deformation
(deformation or normal), Age (in months), Number (the number of parts involved), and
Position (the position of the topmost part operated on).
Concerning the information above, answer all of the following questions.
a) Suppose you want to fit a logistic regression model with three predictors: Age,
Number, and Position. Write down the mathematical equation of the logistic
regression model.
(3 marks)
b) You are given the following output after fitting the logistic regression model in Q3(a).
Coefficient estimate
Intercept -2.04
Age 0.01
Number 0.41
Position -0.21
Explain whether you predict that a young patient with the characteristics (i.e., Age
=1, Number =2, Position = 10) will have deformation D.
[max 80 words] (4 marks)
c) Suppose you also fitted the classification tree below:
PAGE 5 OF 8
Based on the above classification tree, would you predict a young patient with the
following characteristics (i.e., Age =1, Number =2, Position = 10) will have
deformation D? Justify your answer.
[max 60 words] (3 marks)
d) Using the table in Q3b) and the graph in Q3c), comment on the consistency of
results from the logistic regression and the classification tree from two aspects.
[max 80 words] (4 marks)
e) The tables below show the confusion matrices for the classification tree and the
logistic regression. Compare the two classification approaches by making full use
of the confusion matrices and explain which approach you would prefer for
predicting D deformation in young patients.
Classification tree
Predicted Normal Predicted Deformation
Actual Normal 53 11
Actual Deformation 2 15
Logistic regression
Predicted Normal Predicted Deformation
Actual Normal 52 12
Actual Deformation 10 7
[max 100 words] (5 marks)
PAGE 6 OF 8
Question 3 35 MARKS
A multinational hotel and resort group has recently opened three new holiday resorts
in Australia. They are Resort A, Resort B, and Resort C. The business plan was to
position the resorts as “upmarket” complexes with a range of facilities, including spas,
boat and bike hire, beauty and massage services, restaurants, and a small range of
boutique shops. Customers would be charged separately for these extra services if
they used them, and the plan was to generate considerable revenue over and above
the accommodation charges. The Head Office of the company has decided to explore
different ways to promote this type of extra spending but has left it to the resort
managers as to how they gather relevant evidence. Ultimately, Head Office will decide
on how promotion is best achieved based on the evidence from each of the resorts.
Two modes of promotion are being considered. Call these treatments BOOK and TV:
• BOOK: customers are provided with a glossy booklet explaining the available
facilities when they first check in to a resort;
• TV: whenever the television in their rooms is turned on, and before customers
could watch anything else, advertisements would run providing the same
information contained in the booklet.
Regarding the information above, answer all of the following questions.
a) In Resort A, the manager decides to use two one-month periods to gather evidence.
In the first one-month period, all customers are allocated to the BOOK treatment.
In the second one-month period, all customers are allocated to the TV treatment.
The key outcome, denoted by Expend, is defined as the total amount of dollars
spent per booking per day over and above accommodation. At the end of the
second period, the manager calculates the difference in means of Expend for
customers in the two treatments. Explain why this may not be a good approach to
estimate the difference in the causal effects of the two treatments on Expend by
including at least two criticisms.
[max 200 words] (8 marks)
b) In Resort B, the manager decides that all customers, over one month, are allocated
to either the BOOK or TV treatment depending on whether their booking number is
odd or even. Define to be a dummy variable equal to 1 if the i
th customer was
allocated to the BOOK treatment and equals zero if they were in the TV treatment.
Consider the regression = 0 + 1 + . Using data on the 200
customers who stayed at Resort B over the one month of the experiment produces
OLS estimates for this model given below.
How do you interpret the magnitudes of the estimates of 0 and 1? Is the estimate
of 1 statistically significant? Provide an overall interpretation of the difference
between the treatments, BOOK, and TV, that would be suitable for reporting to
senior management.
Note: The 97.5th percentile of a standard normal distribution is 1.96.
PAGE 7 OF 8
̂
= 260.8
(4.8)
−
22.7
(7.2)
= 200, 2 = .048, ( . )
[max 200 words] (10 marks)
c) In Resort C, the manager uses the same approach as in Resort B, except that the
allocation to either the BOOK or the TV treatment was decided by check-in staff
when a customer arrived. The table below provides the sample means, separately
for the two treatment sub-samples, for the 398 customers who stayed at Resort C
over the one month of their experiment.
i. Use these sample means to estimate 1.
[max 50 words] (2 marks)
ii. Based on the sample means for the customer characteristics, discuss whether
you think randomization into the two treatments was successful or not.
[max 80 words] (3 marks)
iii. Use this discussion to provide an argument for why the difference in the causal
effects of the two treatments on Expend is possibly biased using the Resort C
approach.
[max 80 words] (3 marks)
Table: Sample means for key variables divided into the two treatment groups
Variables and definitions BOOK TV
BKd
= 1 if assigned to BOOK, =0 if assigned to TV
1.00 0.00
Income
= 1 if family income > $100,000, = 0 otherwise
0.68 0.79
People
Number of people in the booking party
3.53 4.42
Length
Length of stay in days
4.68 4.81
Age
Age in years of the person making the booking
47.2 47.8
Expand
Expenditure ($) per booking per day over & above
accommodation
231.8 273.2
Observations 178 220
d) In reviewing the results for Resort B, Head Office is surprised that no other controls
were included in the regression reported in Q5b). In particular, they note that the
correlation of 0.61 between the number of people in the booking party and Expend
PAGE 8 OF 8
is positive as expected and quite strong. Explain to Head Office why you think this
is not a problem in interpreting the estimates of 1 in Q5b) as causal.
[max 100 words] (4 marks)
e) After reviewing the evidence, Head Office decided to implement TV for all
customers and advertise the facilities through the television at all three resorts. After
implementing this change, the Resort B manager monitored their Facebook reviews
and noticed that the negative reactions to the television advertising outweighed the
positive posts. What do you think is the most likely explanation for why the
experimental evidence obtained for Resort B is different from the reactions on
Facebook?

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ATMS 201 COMP3425 MATH7861 SOLA2060 MA 325 7CCEMCTH ECON 432 INFT2051 SOLA 2060 ECOS2002 Stats141XP BIOA02H3 S2022 MA4601 K5 COMP5329 EG 284 IB3A70 WECO1020 COMP3027 Energy ACS61011 MAST20026 STAT 533 MATA31 ENVS257 BEM2031 EC 486 COMP0172 CS3214 EECE5644 IB2B20 COSC2536 COSC1147 COMP6214 5CCM242A COMP0130 AMME2000 MTMG50: EG238 ACF6006 QBUS2810 COSC2391 ECON7560 MATH377 STA305 CS 486 COMP1117B EL648 ELEC3909 ECON 241 COMP 4901W BUSI4428 ENEN20002 ENGN4524 ELEC3115 CSC 413 PSYC 100B ECON1002 MATH256 ANSC20005 MTHS2008 MATH40082 COSC 2406 FIT3174 STA 238 CMT202 Differential Equations MTHM017 GARCH 101 MATH2069 CSC336S ACSC12 ETF2121 CEME 2001 DB B474F ARHT1001 EECS 3311 3220 STATS 786 CS 484 COMP20008 MATH4091 ECE 455 CSCI 204 BUS B200F BA 875 BU5562 COSC 1147 X11 GEOG 101 IMM250F CSE 584A MATH38172 LAB 4 ELEC5882M FINS5523 EE 430 COMP 465 6CCS3PRE 7CCSMNSE FINANCE ACMB02H3 ENGG2112 ELEC5616 SCM B371 CHEM3120 CS22 ECMT2130 MAST30011 ADSPBF706 CENG0013 ACCOUNTING BUSS1040 CHEM 181 MAT136H1 GY 6183 COMP3170 AGRI90089 PHYSICS 1002 ECON10003 IEOR E4004 ACS61012 MATH 40550 MKTG2113 TELE4653 ENSC1004 ECE 232E MK726 MTRX2700 SHEET 3 CSCA08 COMP30027 Math 2XX3 statistic MMAN3200 BISM7213 ECMM109 ECON 2022 FNCE 20005 GR5067 CSCB09 ME 3017 CS 5100 PHYS 2903 COMS 4771 COMP222 COMP 1046 CSC148H1 ISYS2038 FINS2615 OT5206 COMP2048 A6 MECH5770M MA826/20 CMPT 726 STA457 INFS6071 INFS588 integral ECON 216 IRDR 0017 MA323 PA 15213 PSYC2001 ENV222 Statistics ACCT5930 ACCT12 LAND2121 ME 5405 COMM1180 TELE3113 A7 ECE 568 ACCT2019 INFS1200 ENGG1340 CS 1400 ASST3 FINM7409 SWEN30006 INFO2222 FIN20150 SOSS1000 CSSE7231 FINS5513 COMS3200 CECN 702 ECON4060H MN3515 HW2 PSYA02H3 ECON 2112 COMP90051 CS 1652 MA30059 AF1605 MKTG7501 ECO-6003B COM4521 CTM ECON3350 COMP 3804 ELEC9762 BISM 7255 ST22 MATH20014 MNE3122 COMP343 MATH5165 ELE7029 ENGR30002 SEEM3500 ECON7350 RISK5002 FINM7405 COMP90059 MOS 3320B STAT 431 BISM7221 MATH3014 MTH783P EBA 35302 ACS133 BUS2206 IE5600 ECOS 3997 EDST2003 FIN 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