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STAT802 Advanced Topics in Analytics
创建日期：2024-04-11 15:27:13
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标签： STAT802
Advanced Topics in Analytics

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STAT802 Advanced Topics in Analytics

Assignment 2
Total Marks: 100 23 April 2021
Paper Description: Advanced Topics in Analytics
Paper Code: STAT802
Total Marks: 100
Date: 23 April 2021
Due date: 26 May 2021 at 10am
INSTRUCTIONS:
1. Only documents in portable format (pdf) will be accepted. You can use, e.g., LATEX, Word,
knitr or Sweave to create your report, as well as RStudio as editor of the source files.
2. Submit the pdf file via Blackboard.
3. Formats other than pdf will be ignored and the author will be asked to re-submit the
assignment. Resubmissions will be subject to the late policy outlined in the study guide
(i.e. 5% per day up to 5 days).
4. The R and SAS codes required to complete this assignment, which includes code to support
your conclusions & answers, must be embedded in the document in the corresponding answer
as text (not image), unless otherwise specified. This code will be marked. Unsolicited
SAS and R scripts submitted separately will not be marked.
5. It is not necessary to copy and paste the question text, just make reference to each question,
e.g., Answer Question 1 (a), Answer Question 1 (b), ... , etc.
6. Read carefully – Answer all the questions as requested. Any material or information
unrelated to the correct answer may result in a significant reduction of marks for that question.
7. Do not forget to fill in and sign the cover sheet which must be the very first page in the pdf.
Use software such as Adobe Acrobat Pro on the Uni computers to include the file at the start
of your document. Do not submit the cover sheet separately.
8. If you need an extension or if your performance has been impacted by some extenuating
circumstances, then you must complete the special consideration form on Blackboard.
9. The comprehension of the questions is part of the assignment.
Grade table:
Question: 1 2 Total
Points: 30 70 100
Score:
23 April 2021 STAT802— Advanced Topics in Analytics Page 1 of 5
Student ID: STAT802— Assignment 2 Semester 1, 2021
QUESTIONS:
1. The data come from the Western Collaborative Group Study, which was carried out in California
in 1960-61 and studied 3,154 middle-aged men to investigate the relationship between behaviour
pattern and the risk of coronary heart disease. These particular data were obtained from
the 40 heaviest men in the study (all weighing at least 225 pounds) and record cholesterol
measurements (mg per 100 ml), and behaviour type on a twofold categorization. In general
terms, type A behaviour is characterized by urgency, aggression and ambition, while type B
behaviour is relaxed, non-competitive and less hurried. The question of interested is whether,
in heavy middle-aged men, cholesterol level is related to behaviour type.
Total for Question 1: 30 marks
Type A behaviour: cholesterol levels
233 291 312 250 246 197 268 224 239 239
254 276 234 181 248 252 202 218 212 325
Type B behaviour: cholesterol levels
344 185 263 246 224 212 188 250 148 169
226 175 242 252 153 183 137 202 194 213
Source: Selvin, S. (1991) Statistical analysis of epidemiological data, New York: Oxford
University Press, Table 2.1
Use the resampling methods studied in classes and perform the analyses in SAS to answer
the following questions:
(a) Does the Type A behaviour group differ from the Type B group in terms of their average
cholesterol level? Justify your answer. Include the corresponding hypotheses and histogram,
indicating in it clearly the statistic used for the test. [Marks: 2 SAS code + 5 rest] [7 marks]
(b) If there is a difference on average on the cholesterols levels of Type A and B groups in the
analysis performed previously, calculate a 95% bootstrap confidence interval. Interpret
your results. [Marks: 2 SAS code + 5 rest] [7 marks]
(c) What is your advice as analytics specialist? Justify your answer. Write a brief report with
your findings. [4 marks]
(d) The patients of group A have voluntarily participated in a therapy in order to change
positively their behaviour. Some previous studies were used to convince them. The therapy
is new and it has not been tested before. Their initial and final cholesterol levels, i.e., before
and after the therapy, were registered and are displayed in the table below:
Patient 1 2 3 4 5 6 7 8 9 10
Before 233 291 312 250 246 197 268 224 239 239
After 247 283 282 185 254 172 243 221 234 240
Patient 11 12 13 14 15 16 17 18 19 20
Before 254 276 234 181 248 252 202 218 212 325
After 259 279 215 129 237 234 178 165 188 310
You as a data analyst have to assess the therapy. What would you conclude? Write a brief
report with your findings. This should answer the following questions: was the therapy
effective for all the patients in the study?; is the therapy effective?; if so, quantify the
23 April 2021 STAT802— Advanced Topics in Analytics Page 2 of 5
Student ID: STAT802— Assignment 2 Semester 1, 2021
difference between the groups. Justify all your conclusions. [Marks: 3 SAS code + 9
rest] [12 marks]
2. By the early years of the 18th century, astronomers had fairly well determined the relative
dimensions of the solar system, the relative distances between planetary orbits and between
the planets and the sun. However, they lacked precise information on the absolute dimensions
of the solar system, and were eager to determine even one such distance in miles, in particular
the mean distance from the earth to the sun, from which all others could be found. Actually,
the quantity that 18th century astronomers chose to pursue was the parallax of the sun, the
angle subtended by the earth’s radius, as if viewed and measured from the surface of the sun.
From this angle and available knowledge of the physical dimensions of the earth, the mean
distance from earth to sun (or astronomical unit) could be easily determined.
The astronomer Edmund Halley (1656-1742) is generally credited with having been the first to
suggest that the parallax of the sun could be determined by observing a “transit of Venus,” the
apparent passage of the planet across the face of the sun, as viewed from earth. If observers
were dispatched to all corners of the globe from which this transit would be visible, and they
carefully recorded their positions and the elapsed time of the transit (on the order of 5.5 hours),
then each pair of observers would furnish one determination of the parallax of the sun.
Unfortunately for the implementation of Halley’s plan, transits of Venus are quite rare, owing
to the 3◦36’ inclinations between the orbits of Venus and Earth. The first recorded transit
of Venus occurred on 1639 and was observed only in England; the next transits were due in
1761 and 1769, with later transits due in 1874, 1882, 2004 and 2012. By 1761, interest in the
forthcoming transit was high, and observations were made at the Cape of Good Hope, and in
Calcutta, Rome, Stockholm and most other European observatories. A good account of the
transits of 1761 and 1769 can be found in Woolf (1959); and excellent discussion of the data
generated by these transits is given by Newcomb (1891).
James Short was a maker of optical instruments, particularly telescopes. He was born in
Edinburgh, Scotland and entered the University of Edinburgh in 1726, where he attended
lectures by the mathematician Colin Maclaurin. Short is perhaps best remembered for his
observations of the transit of Venus on June 6, 1761, one of the two most important astronomical
events of the mid-eighteenth century (the other one was the transit of Venus in 1769).
Short recorded 53 observations and analysed them in a “robust” manner. He took the mean of
all n = 53 determinations (8.61), then rejected all results differing from 8.61 by more than 1.00
and obtained the mean of the remainder (8.55), then rejected all results differing from 8.61 by
more than 0.50 and obtained the mean of the remainder (8.57); finally he took the mean of
8.61, 8.55, 8.57 to obtain the sun’s parallax as 8.58. He applied similar analyses to other data
sets of the same phenomenon.
The parallax.txt file contains James Short’s measurements of the parallax of the sun (in
seconds of a degree), based on the 1761 transit of Venus. The data were originally published
in Philosophical Transactions of the Royal Society of London. The table with the observations
and descriptive information here are adapted from an article by Stephen Stigler in the Annals
of Statistics. The number of cases is 53 and the true value is 8.798. The parallax of the sun is
the angle subtended by the earth, as seen from the surface of the sun.
The data is available on http://www.randomservices.org/
References:
23 April 2021 STAT802— Advanced Topics in Analytics Page 3 of 5
Student ID: STAT802— Assignment 2 Semester 1, 2021
Newcomb, S. (1891). Discussion of observations of the transits of Venus in 1761 and 1769.
Astronomical Papers 2 259-405, U.S. Nautical Almanac Office.
Stigler, S.M. (1977). Do Robust Estimators Work with Real Data? The Annals of Statistics,
5, 1055-1098.
Woolf, H. (1959). The transits of Venus. Princeton Univ. Press.
Suppose that your are James Short’s data analyst at that time and you are asked to analyse
his data in a Bayesian way.
Assume that Xi|µ, σ ∼ N(µ, σ2) for i = 1, . . . , n, with µ∼N(µ0, σ20) and σ2∼ InvGamma(α, β).
It can be shown that
µ|σ,X1, . . . , Xn ∼ N

nx¯
σ2
+
µ0
σ20
n
σ2
+
1
σ20
,
(
n
σ2
+
1
σ20
)−1
σ2|µ,X1, . . . , Xn ∼ InvGamma
(
n
2
+ α,
1
2
n∑
i=1
(xi − µ)2 + β
)
.
Note that small α (shape) and β (scale) values have a small impact on the conditional distribution
of σ2. Total for Question 2: 70 marks
(a) Identify and define the parameters of the model. [5 marks]
(b) Specify the prior distributions. Justify your answer. [10 marks]
(c) Implement a Gibbs sampling algorithm. Comment on the MCMC specifications and the
effective sample sizes (ESS). Generate trace plots of the posterior of the parameters and
comment about them. [Marks: 8 code + 12 brief report] [20 marks]
(d) Generate a 95% credible interval for µ. Interpret your results and propose a point estimate
for this parameter. Justify your answer. [10 marks]
(e) We now know that the true value of the parallax of the sun is 8.798. What do you conclude
about Short’s and Bayesian estimates? Justify your answer. Hint: consider the relative
error. [10 marks]
(f) Calculate the 95% confidence interval for µ and interpret it in the context of this problem.
Compare it to the credible interval calculated previously and discuss their difference. [10 marks]
(g) Write down the JAGS model. [5 marks]
23 April 2021 STAT802— Advanced Topics in Analytics Page 4 of 5
Student ID: STAT802— Assignment 2 Semester 1, 2021
23 April 2021 STAT802— Advanced Topics in Analytics Page 5 of 5
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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 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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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COMP2013 IB9X60 INVP001 EMATM0051 COMP220 BLAW1002 E20 TCP8001 ECON61001 EART40005 ECONM1022 MATH 365 MATH 367 ELEC6218 COMP 5812M EDUC 5061M ELEC ENG 1101 equation MATH6174 ST334 C4 EC9570 A33648 EBUS603 ECON47815 stata WFMS0001 PEM102 INF6060 ionically ELECTRICAL MATH 375 MMW 12 MKTG860 ECON60401 ALY6010 SOST20131 Introduction COM3524 MME2 GGR305 JNB 538 EECS101 COMM5030 IEOR E4601 2B CSC336 EN2315 LAW 432 CSC263 MATH2001 OLET1143 CSCB63 SIT772 COMM1170 STAT 441 MS930 INFT3050 GLBH0030 Stat 120B MAST10007 MGEC61 Machine GGR 252H1S ECON7030 Financial Marketing MGMT30019 ECON5120 SDSC 8009 CSC165H1S DSCI 551 3081W ACM41000 ECA 5304 MATH 1064 ECSE444 DATA 604 COMP7105 MIE1615 ECON4004 ENV200H1S EC481 MKT 306 EC1B1 MATH08051 ALY6140 CS260C CSE30 MSIN0226 CS4103 CSC3064 AM16 COMP11212 CSE4101 PSTAT 174 Math 380W CNT 5106C STAT 310 FIN2028 CS5014 STAT 425 COMP 4102A ELEC2140 MANF9544 QRM 9 QRM 8 ECON 457 COMPSCI 4091 MATH 523 DNSC-4211 PEAS 6000 MSIN0180 COMP2119 A2A HT 2022 IB93F0 MAS316/414 ACCT2522 MSIN0181 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 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