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COMM1190: DATA, INSIGHTS AND DECISIONS
创建日期：2024-05-17 17:02:03
标签： COMM1190
DATA, INSIGHTS AND DECISIONS

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COMM1190: DATA, INSIGHTS AND DECISIONS
FINAL EXAMINATION
1. Writing Time: 3 Hours.
2. Reading and Submission Allowance Time: 1 Hour.
3. This is an Online Open-Book Exam, your responses must be your original
work. You must attempt this Final Exam by yourself without any help from
others. Thus, you have NOT worked, collaborated or colluded with any other
persons in formulating your responses. The work that you are submitting for
your Final Exam is your OWN work.
4. Release date/time (via Moodle): Saturday, 3rd December 1:00pm (Australian
Eastern Time Zone)
5. Submission date/time (Via Turnitin): Saturday, 3rd December 5:00pm
(Australian Eastern Time Zone)
6. Failure to upload the exam by the submission time will result in a penalty of
15% of the available marks per hour of lateness.
7. This Examination Paper has 9 pages, including the cover page.
8. Total number of Questions: 3 Questions.
9. Answer all 3 Questions.
10. Total marks available: 100 marks. This examination is worth 50% of the total
marks for the course.
11. Questions are not of equal value. Marks available for question sub-parts are
shown on this examination paper.
12. Answers to questions are to be written in the exam answer sheet template
provided. Please ensure that you provide all details required on the cover
sheet of your Final Exam answer sheet.
13. Failure to submit exam answers with the correct exam answer sheet will result
in 10% penalty of your overall exam marks.
PAGE 2 OF 9
This Final Exam is an open book/open web, and further information is
available “Here”.
• You are permitted to refer to your course notes, any materials provided by
the course convenor or lecturer, books, journal articles, or tutorial
materials.
• It is sufficient to use in-text citations that include the following information:
the name of the author or authors; the year of publication; the page
number (where the information/idea can be located on a particular page
when directly quoted), For example, (McConville, 2011, p.188).
• You are required to cite your sources and attribute direct quotes
appropriately when using external sources (other than your course
materials).
• When citing Internet sources, please use the following format:
website/page title and date.
• If you provide in-text citations, you MUST provide a Reference List. The
Reference list will NOT BE counted towards your word limit.
16. Students are advised to read the Final Exam paper thoroughly before
commencing.
17. The Lecturer-in-Charge (LiC) / Exam Referee will be available online (via
Moodle) after the Open-book Exam paper is released for a period of one hour.

PAGE 3 OF 9
QUESTION 1 40 MARKS
This question consists of three parts – Part A, B and C. Answer all parts.
The following dataset relates quality of wine (scores between 0 and 10) to the
following input variables:
1 - fixed acidity
2 - volatile acidity
3 - citric acid
4 - residual sugar
5 - chlorides
6 - free sulfur dioxide
7 - total sulfur dioxide
8 - density
9 - pH
10 - sulphates
11 – alcohol
PART A. (16 Marks)
You are provided with the following output:
PAGE 4 OF 9
Please answer the following questions:
1. Which model has been fitted? Write down the mathematical equation for this model.
(2 marks)
2. Which variables are significant at 5% significance level in explaining the dependent
variable? Which test is used to test the significance of variables: formulate the null,
the alternative and the test statistic, and explain how the conclusion to reject or not
to reject is drawn? (4 marks)
3. Provide interpretation of the coefficients associated with variables citric acid and
alcohol. (2 marks)
4. Comment on the overall model fit referring to 2 statistics in the output and explain
the meaning of each statistic. (4 marks)
5. Explain why we need adjusted R-squared and cannot just rely on R-squared.
(2 marks)
6. If you decide to make a prediction for the quality of wine, would you keep the
proposed model or adjust it in some way? Explain your proposal. If a new model
should be used, write down the regression equation and provide an explanation
behind the choice of this model. (2 marks)
PART B. (10 Marks)
To simplify wine classification, we assign the following scores to poor, okay, and good
wines: Poor wines are those with scores of 3 or 4; okay wines are those with scores of
5 or 6; good wines are those with scores of 7 or 8.
Two models were fitted to the data:
Output 1:
PAGE 5 OF 9
Output 2:
Please answer the following questions:
1. Write down mathematic expressions for both models and briefly explain the right-
hand side and left-hand side of the equation. (5 marks)
2. Comment on the significance of variables in Model 1 and Model 2. (2 marks)
3. Which model performs better and why? Which criteria have you used to determine
the quality of fit? Explain. (3 marks)
PAGE 6 OF 9
PART C. (14 Marks)

Two different classification trees were fitted to the data, where we only aim to identify
good wine (i.e. those wines scoring 7 or 8) and the following output was produced:
Tree 1:
PAGE 7 OF 9
Tree 2:
1. What do the decimal numbers and percentage numbers in each node represent?
Provide a brief explanation (you can use the first split of a tree branch for your
explanation). (4 marks)
2. What are the most influential variables characterising the quality of wine?
(1 mark)
3. Consider the following confusion matrix:

PAGE 8 OF 9
Comment on the quality of the prediction and compare the results from the trees.
Please calculate all possible accuracy rates to facilitate your explanation. (7 marks)
4. Provide two facts to demonstrate the consistency of results from the classification
tree above with the findings reported in Parts A and B. (2 marks)

QUESTION 2 25 MARKS
Consider the following mini-case:
SOSText (fictionalised name) is a non-profit that offers support via text messages for
people who are going through mental health crises. The support is primarily driven by
a network of volunteers servicing communities in major cities. For years, the non-profit
had been collecting a database of messages exchanged and used the data to triage
the incoming calls for help and to create training modules to help train its volunteers to
better manage difficult conversations with people in great distress. In a 2020 report,
the non-profit (which first launched in 2013) stated that “by implementing data science
tools and machine learning from day one, we have created the largest mental health
dataset in the world.” A report section titled “Data Philosophy” added that they share
data to support more innovative research, policy, and community organising. Unlike
other large-scale datasets on mental health and crisis, the organisation claimed its
data had incredible volume, velocity, and variety. However, the positive impact of
SOSText remained constrained as financial reasons limited their ability to scale their
offerings to more people in different contexts. To overcome this issue, one of the
founders of SOSText decided to launch a for-profit spin-off called SmartAssist.ai. This
new service planned to use SOSText data (which it said was anonymised) to gain
insights that would be incorporated into building new training modules for people
recruited by the new spin-off. SOSText and SmartAssist.ai created a data-sharing
agreement that allowed controlled access to data solely to build models for training
that would improve mental health more broadly to reach a broader and more diverse
population of people in distress. SmartAssist.ai would also share a portion of the profits
from that software with SOSText to help sustain the operations of the volunteer-driven
organisation. SmartAssist.ai also wants to build predictive tools to help assistors
identify people who might be at risk of harming themselves to engage in the proactive
intervention.
Based on the case above, answer the following questions:
a) Identify and discuss TWO (2) ethical concerns presented in the case. Analyse
the concerns through the lens of the ethical theories covered in the course. What
aspects of the ethical landscape do they highlight? (15 marks)
b) Identify and discuss at least TWO (2) data ethics dimensions discussed in the
course relevant to the above case. How might the risks associated with these
dimensions be mitigated? (10 marks)
PAGE 9 OF 9
QUESTION 3 35 MARKS
Consider the following:
You are part of a policy team in the Australian government tasked with examining how
to reduce the gender-pay-gap between men and women. One policy that has been
suggested is to improve the representation of women on board of Australian public
companies.
The minister is particularly interested in overseas experience and the team discovers
that in 2003 Norway passed a law mandating 40 percent representation of each gender
on the board of public limited liability companies.
Your team is tasked with figuring out whether a board quota is an effective policy for
reducing the gender pay gap. Answer all of the following:
a) One member of your team suggests looking at the gender-pay-gap in Norway
in 2002 and 2004 to determine the causal effect of the policy. Is this a good
strategy? Why or why not. (10 marks)
b) Another team member suggests comparing teenage driving accidents in
Norway and the United States (which did not enact such a policy) in 2008. Is
this a good strategy? Why or why not. (10 marks)
c) A third team member suggests calculating changes in the gender-pay-gap in
Norway between 2002 and 2004 and then comparing those to the changes in
the gender-pay-gap in another country between 2002 and 2004. Is this a good
strategy? Why or why not. (15 marks)
案例分类
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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 EARN 5 FE 825 IERG 5130 STAT 8178 EE161 ADS2 G54FUZ CS326 113B ECN 226 QBUS6860 ELEC3202 COMP9334 MGT11B MET CS 777 STA238 00099F Python SMM306 CMT206 EC116 SP MAST90084 CS480/680 BCPM0091 CMPT 300 ECON2209 FY20 STAT 432 CVEN9625 MATH1712 CMP9771M CMP3750M CIS 325 AI506 MAT344H1S MGT7180 PSTAT 160B CSYS5010 1D STAT 453/558 ME152B STA304H1F CSCI-128 ME428 CO3002 FINE 7640 COMP3234 ECSE 223 EE497 – 3D MATH5905 ECS648U ENG5009 MATH10242 EC665 A CPI221 ISOM5220 MACE40402 MGT B456F COMP9315 AFM 113 STAT 778 CS 564 MA231 CS 230 CS 124 COMP5511 CSC317 ITEC1010 ELEC207 INFT 3019 7CCMFM06 EE 161 5G ECLT5810 MATH 7349 CS526 O2 COMP90054 ELE00063M ACCT5034 ARC1015 CSCI 40 CSCI E40 FIN 524B CS 462 389COM CMT309 MFIN703 COMP4250 STA304 STA304H1S STA 1003 FIN 448 BUFN 732 CS520 CVEN3701 ECMT6006 PHAS0100 CSIT314 MDIA 5028 CSCI 2134 MAT 451 CMPT 459 STAT 457 INFO20003 COMP4141 IELE8066 F540 ECM9 CSE 6321 FIN 430 MATH3911 BFC5916 COMP1011 EEE 6209 STATS 240 CW3 COMP206 COMP1003 4331W ISE 530 CMPT 295 MATH 239 QF5203 INFR08010 IT114105 FIT2093 ECON3124 ECON1195 MATH 337 STAT 3006 COMP202 COMP3130 MATH3609 COMP 250 CS6140 IEEE ELE00041I FIN40090 CIV 5899 MATH3066 IGNMENT MATH 2009 STAT3405 HIST 101 MATH 3281 CEGE0042 ICT532 LAB 7 LAB 5 MCO455 CSIT214 Q4 IFB104 A1 ECO220Y5Y DA5020 CMPSC473 FIN9007 DSCI553 MSCI534 COMP3308 CSE-112 CMPT 310 EE104 F 2C P20 ALY2010 BUSM060 STA258 ECO00030M R BIOSTAT 274 COS6012-B FIT3155 CMPUT 340 EC421 INFS4205 ECOM135 NATS 1740 COMP809 MBE 6005 STA465 CSSE2310 CIVL2700 DATA4700 CMPSC 473 ISE 529 CS5044 MAST30020 COMP2207 ENGG1001 COMP0046 FIT3165 STAT GR5241 MECH0007 vATHK1001 ANALYTIC ATHK1001 MATH352001 HW3 S1 BMAN 24662 COMP 636 Stat-461 STAT 302 STAT 2132 STAR534 ENG4137 CMP3752M CS3103 BISM3222 STA 135 ME155A IIMT3601 ECON3371 ECON 3371 HW4 FINA 5840 DT211C CTEC3902 MAT005 MATH2148 S261F Econ 332 MATH3734 MIS41270 7CCMFM13 SYST 664 CIS434 CENG10014 MT4111 MT4527 ECMM424 CMT219 Math 136 MATH 3120 COMP10002 MAST20009 MATH0058 BEEM117 ISYE 6420 MGMTMFE 405 25579 1ECE 273 M122 COMP4434 1ELEC ELEC5620M STAT0010 BFF2401 MXB361 MATH3811 CS330 CMT307 FIT1033 CptS 583 UP 431 MATH6005 AF5343 CS2435 ECOM30002 ES50061 CS 2506 ECMT6002 CODE 00099F MT4539 ESSAY ICM317 MATH1231 MFIT5010 BMA547 MATH334 CIVL2201 MATH39032 T-04 CIVE97119 EMESTER CAB302 CAB301 59PM STAT802 ISYS3412 RM3 COSC 2123 AcF 311 MATH2022 1FINAL 4CCS1PHC ICM 142 ENVS3023 Math 4023 I1 ACSE8 FIN B385F CS-275 STA 4109-01 A31021 EE140 MT1 EE124 ECO-6004B Inf2-IADS CS126 IS2184 UL18 MATH5092 ECOM009 COMP 6211 ELEC202 MKT3019 IAB230 FIT5046 584-S21 COMP3210 CO250 A29401 MATH 136 MGT6174 ECMT6007 DATA2001 MAT 443 MATH10222 PHDE0071 ELE00123M COSC1295 MAT00045H MATH32032 1004GRC MAS223 COMP10001 DATA3404 CIVL2110 STAT2008 FNCE10002 EEE6431 COM6515 COMP9120 NBS8185 ECTE942 ECON 405 MATH314301 STAT 2011 COMPSCI 350 COMP20007 CMPSC-132 ECON5002 STA 35 MTH6108 FIT5147 MTH6150 100C FINA 5260 DESC9115 ECON4998 NBS8257 GMM ECON 6070 BSA2 ECE599 STATS 102B STA120 INFS601 BFC3241 MATH20602 COMP532 MAS275 COMP5318 COMP4002 FIT5086 MAST20004 FIT3152 FIT5129 CIS2380 CS521 COMP 1005 EECMS CITS5501 ECE 462 ECON 6002 ENG 4051 CSC 648 CMT654 MECHENG 713 PHYS1002 AMME 2000 FP0060 DDES3200 MECHENG 743 BINF90002 FIT2081 ISYS90081 ELECTENG 722 BUS 360 ECOS3010 BUS2810 ETF2100 ENG2048 STAT5002 COMP2017 CEME 2003 MATS3004 COMP9319 PHYS1001 COMP5349 AMME2261 COMP-381 FIT3173 COSC2757 ENG4025 QBUS2820 COMP9414 MATH2021 EOSC118 MFIN6690 PMATH 321 CSE-141L MATH575A COMPSCI5002 COMP1511 management comp2123 FINS3616 COMP 3331 COMS30034 DTSC 71 AcF 702 AcF702 DTSC71 MAT021 ELE8083 ACFI213 EG-127J 127J FIT5163 FIT2001 ELE00113M CMT310 CS 245 ENGL 1101 MTH6115 EE497 CS 325 Math 124A MTH5130 MBA502 MGT 5380 ARCH 1261 F033800 20LLP109 ELE4009 FINANCIAL COMP3331 Computer ECE4179 20T3 COMP9311 COMP9311 UNSW MFIN6210 ACCT 5930 EE590 ENG2029 ECO100 SIT315 ACCT3610 COMP3600 STK2100 FIT2004 COMP30020 ACTL1101 Stat 101B BANA 200 COSC2473 ENGG1400 AI COMP90043 MMF 1914H PHYS 127 CS584 CIVL2010 ECMT2150 CS 333 CP1406 KIT308 Economics 701 ECON3740 ECE 5759 BFC3340 FINC2012 COMP4500 MATH3871 FIT3080 18-819C Economic ECO202Y5Y PHY100 FINC-780 Econ 100B EC602 IE582 EE450 COMP9517 WRDS 150B ECON 570 LECTURE 2 ECA5101 ECE 4420 ADAD9311 FIT3031 CS 544 MF 703 CS 6140 KIT501 ENGSCI 344 CSC171 IE 332 FINM8007 21S2 COSC 1114 FE545 E471 SIT102 CSC108 MAT301 IAE 101 COP3503 STAT2004 EC 303 CS1026 STAT1203 MATH3171 ISOM5160 IFN657 FIT2014 ACCT2102 DSCI550 STATS 330 ELEC421 COMP3670 QBUS6840 MEES:698B MAST30027 MIET1077 Economics IFT 6758 FIT5122 BUSN5101 CptS 111 HRMT5502 Math2018 PP 312 INFS3200 CZ3005 CSSE1001 SENG 2011 MKTG 553Q BUFN 640 COMP507 ETF5952 DATA 311 ECE4043 BANK3011 finance M3H623544 HW1 INFS 7410 AI6103 STAT3888 INFS3208 ACCT7103 ECOS3013 PSYCH 20B F79MA STAT3006 ENGG952 2X MGSC 7000 ECE4132 MSF 526 MATH 3033 CDS 511 EEEE4120 EEET2465 ECA5103 MATH2070 ELEC3104 BU51019 EEE8147 ENGR20003 COSC 1107 ETF3200 FNCE 435 MSFI 435 ISyE6420 GENG4405 HW ETC5523 IE 7315 MOEC0021 STA 106 ENGG 1300A ENGG 1300 OLET1139 STAT 3503 STAT 206 FEM11149 ELEN 4720 COMP20003 SOLA1070 CS574 C11CS LINB29H3 MAST90083 ECON7200 STAT 610 LINB29 DSM-5 COMP3161 EEEN40010 MATH 4431 K353 STA 3386 IERG4300 COMS W4167 ENG5298 AMME 2302 CS3910 COMPSCI 320 STATS 369 GR5242 COSC 328 CSCI435 MGTS1301 INFS2200 COMP3900 CIS 375 Module AE03 COMP3121 CS260 HAM503 MGMT5050 INT3075 COSC 285 GEOL0029 GEGE2X01 STAT1201 MATH1061 MMF1941H STAT 5060 CS 520 COMP 4320 MSIN0104 000T FINS3648 ECMT2160 ECON4003 Phys 129L CSE 142 CS 3130 MGEB01 EECS 340 Finance (20443/20444) ECMM149 COMP5310 EEEN60180 CSC 111 AT2 CSCI 4430 STAB57 MA/CSC 427 MATH322 D100 CSCI544 CITS3004 CI6227 CSE 565 APCO 1P01 852L1 ELEC S411F COMP3207 STATS330 ECON7530 FIT5149 CSI4104 CMPT 371 COMP201 SEHS4696 FI4003 MATH6182 CG1 WS MATH401 MGT 205 QTM 110 COMP 0137 MACE12201 FIT5210 ECE 544 BISM7206 HUDM 6055 ECON3203 STATS 210 MSCI 570 MATH6173 COMPSCI4039 Accounting COM3503 AOE2024 ECON213 PSYC10004 Control Data 100/200A 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