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MATH1015 Biostatistics

创建日期：2024-05-29 14:49:06

所属分类：数学mathematics

标签： MATH1015

Biostatistics

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MATH1015
Biostatistics
Lecturers:
Time Allowed: Reading - 10 minutes; Writing - 1.5 hours
Exam Conditions: This is a closed-book examination — no material permitted. Writing
is not permitted at all during reading time.

This examination has two sections: Multiple Choice and Extended Answer.
The Multiple Choice Section is worth 50% of the total examination.
There are 20 questions. The questions are of equal value.
All questions may be attempted.
Answers to the Multiple Choice questions must be entered on
the Multiple Choice Answer Sheet before the end of the examination.
The Extended Answer Section is worth 50% of the total examination.
There are 3 questions. The questions are of equal value.
All questions may be attempted. Working must be shown.
THE QUESTION PAPER MUST NOT BE REMOVED FROM THE
EXAMINATION ROOM.
Marker’s use
only
Page 1 of 20
Sample Exam Questions A 2018 Page 2 of 20
Multiple Choice Section
In each question, choose at most one option.
Your answers must be entered on the Multiple Choice Answer Sheet.
1. In a medical trial, the placebo effect occurs when
(a) the subject is scared of the trial
(b) the subject responds to the idea of the treatment
(c) the subject is already sick
(d) the subject knows the investigator
(e) there is a historical treatment group
2. In a study on smoking and lung cancer, a possible confounder is NOT
(a) a gene for smoking (b) brand of cigarettes (c) diet
(d) alcohol (e) exercise
3. Simpson’s Paradox occurs when
(a) relationships between percentages in subgroups are reversed when the subgroups are
combined.
(b) relationships between percentages in subgroups are the same when the subgroups
are combined.
(c) relationships between percentages in subgroups are the same when the subgroups
are separated.
(d) relationships between percentages in subgroups disappear when the subgroups are
separated.
(e) a clear trend in individual groups of data is revealed when the groups are pooled
together.
4. Suppose a set of bivariate data has a correlation coefficient of 0.90. Which statement is
true?
(a) 90% of the points are highly correlated.
(b) 90% of the points fall on a line.
(c) The linear regression line has a slope of 0.9.
(d) 90% of the points can be predicted by a linear regression line.
(e) The data may have a strong linear trend.
5. In a dataset of size 6, the mean is 7 and standard deviation is 4. We add 3 to each
observation in the data. The new mean and standard deviation are respectively
(a) 7 and 4 (b) 10 and 4 (c) 10 and 7 (d) 7 and 7 (e) 10 and 13
Sample Exam Questions A 2018 Page 3 of 20
6. A box has a mean 7 and standard deviation 2. Which distribution can best approximate
the distribution for the mean of 100 draws?
(a) Normal with mean 7 and standard deviation 2.
(b) Normal with mean 7 and standard deviation 0.2.
(c) Normal with mean 700 and standard deviation 20.
(d) T with degrees of freedom 99, mean 7 and standard deviation 2.
(e) T with degrees of freedom 100, mean 700 and standard deviation 20.
7. For a quantitative data set, the mean and median are the same. Which statement is
true?
(a) The histogram is skewed (b) The scatterplot shows a linear trend
(c) The barplot is balanced (d) The boxplot is symmetric
(e) not enough information
8. For a quantitative data set, the interquartile range (iqr) is 1. Which statement is true?
(a) The data is skewed. (b) There is no outlier.
(c) The standard deviation is 1. (d) The boxplot has length 1.
(e) The box in the boxplot has length 1.
9. If Z ∼ N(0, 1) then P (−3 ≤ Z ≤ 3) is closest to
(a) 0.25 (b) 0.5 (c) 0.68 (d) 0.95 (e) 0.997
10. Which is the output for the following R command?
pnorm(0)
(a) 0 (b) 0.5 (c) 0.7 (d) 0.9 (e) 1
11. Using the linear regression line, what expression would predict y when x = 2.
##
## Call:
## lm(formula = y ~ x)
##
## Coefficients:
## (Intercept) x
## 1.8403 0.8655
(a) 1.8403− 0.8655× 2 (b) 1.8403 + 0.8655× 2 (c) 0.8655 + 1.8403× 2
(d) 0.8655− 1.8403× 2 (e) Not enough information
Sample Exam Questions A 2018 Page 4 of 20
12. A box contains the numbers 0,2,3,4,6 and 25 draws are made with replacement. The
expected sum of draws and the standard error are respectively
library(multicon)
box=c(0,2,3,4,6)
mean(box)
## [1] 3
popsd(box)
## [1] 2
(a) 3/25 and 2/
√
25 (b) 3×
√
25 and 2/
√
25 (c) 3×
√
25 and 2×
√
25
(d) 3× 25 and 2×
√
25 (e) 3× 25 and 2× 25
13. A box contains the numbers 0,2,3,4,6 and 100 draws are made with replacement. Which
R codes calculate the probability that the mean of draws lies within 2.9 to 3.1?
(a) pnorm((3.1-3)/(2*10))-pnorm((2.9-3)/(2*10))
(b) pnorm((3.1-3)/(2/100))-pnorm((2.9-3)/(2/100))
(c) pnorm((3.1-3)/(2/10))-pnorm((2.9-3)/(2/10))
(d) pnorm((2.9-3)/(2/10))-pnorm((3.1-3)/(2/10))
(e) pt((3.1-3)/(2/100),100)-pt((2.9-3)/(2/100),100)
14. A box contains the numbers 0,2,3,4,6, each repeated 200 times and 20 draws are made
without replacement, which of the following statements about standard error for the
sum of draws is TRUE?
(a) It becomes one fourth if the sample size is half.
(b) It drops to 0 if the sample size is 200.
(c) It remains unchanged compared to the with replacement case.
(d) It enlarges by a factor of
1000− 20
1000− 1 compared to the with replacement case.
(e) It shrinks by a factor of
√
1000− 20
1000− 1 compared to the with replacement case.
15. A box contains nine “0” and one “1” and 16 draws are made with replacement. Which
distribution will best approximate the distribution for the average of draws?
box=c(0,0,0,0,0,0,0,0,0,1)
library(multicon)
mean(box)
## [1] 0.1
Sample Exam Questions A 2018 Page 5 of 20
popsd(box)
## [1] 0.3
(a) Normal with mean 0.025 and standard deviation 0.3/4.
(b) Normal with mean 0.1 and standard deviation 0.3/16.
(c) T with degrees of freedom 15, mean 1.6 and standard deviation 0.3*4.
(d) Unknown skew distribution with mean 0.1 and standard deviation 0.3/4.
(e) Unknown skew distribution with mean 0.4 and standard 0.3*4
16. A box contains nine “0” and one “1” and 16 draws are made with replacement. Using
the simulation result below, what is the probability that the sum of draws is at least 6?
box=c(0,0,0,0,0,0,0,0,0,1)
sim=replicate(10000,sum(sample(box,16,rep=T)))
table(sim)
## sim
## 0 1 2 3 4 5 6 7
## 1840 3382 2667 1416 533 134 26 2
(a) 0.0002 (b) 0.0026 (c) 0.0028 (d) 0.1840 (e) 0.9998
17. A marketing company is surveying consumers’ preference for Coke over Pepsi. If p =
P (Customer prefers Coke to Pepsi), the null and alternative hypotheses are respectively
(a) H0 : p = 0.5 and H1 : p 6= 0.5 (b) H0 : p = 0.5 and H1 : p > 0.5
(c) H0 : p 6= 0.5 and H1 : p = 0.5 (d) H0 : p = 0.5 and H1 : p < 0.5
(e) H0 : p < 0.5 and H1 : p > 0.5
18. A marketing company is surveying consumers’ preference for Coke over Pepsi. Which
box could NOT model the null hypothesis?
(a) 0,1 (b) 0,1,1 (c) 0,0,1,1 (d) 0,0,0,1,1,1 (e) 0,0,0,0,1,1,1,1
19. A marketing company is surveying consumers’ preference for Coke over Pepsi. Out of
100 consumers surveyed, 60 prefer Coke to Pepsi. Which formula gives the test statistic?
box=c(0,1)
mean(box)
## [1] 0.5
Sample Exam Questions A 2018 Page 6 of 20
popsd(box)
## [1] 0.5
(a)
60− 50
10
(b)
50− 0.6
10
(c)
0.6− 0.5
0.05
(d)
0.6− 0.5
0.5
(e)
0.5− 0.6
5
20. A test was conducted to test the hypotheses H0 : µA = µB vs H1 : µA 6= µB where µA
and µB represent the population mean for group A and B respectively.
##
## Welch Two Sample t-test
##
## data: yield by variety
## t = -4.9994, df = 19.441, p-value = 7.458e-05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.9293569 -0.3814274
## sample estimates:
## mean in group A mean in group B
## 4.052941 4.708333
Based on the results above, which of the following statements is FALSE?
(a) The test statistic is -4.9994.
(b) The p-value is close to zero.
(c) Equality of variance assumption is made.
(d) The data are against H0.
(e) The 95% confidence interval of µA − µB excludes 0.
End of Multiple Choice Section
Make sure that your answers are entered on the Multiple Choice Answer Sheet
The Extended Answer Section begins on the next page
Sample Exam Questions A 2018 Page 7 of 20
Extended Answer Section
Answer these questions in the spaces provided.
1. We are interested in monitoring the air quality index (AQI) in the month of July 2015
between two regions: Sydney’s central-east (CE) and Sydney’s north-west (NW). Due
to the fact that data readings from different pollutants have different underlying units
of measure, the AQI is a derived value based on multiple data readings that enables
easier comparison across regions and time. In general an AQI score above 100 and
below 150 indicates a ‘poor’ air quality level and that people in the sensitive group (e.g.
people with asthma, older adults and children) should consider either cutting back or
rescheduling strenuous outdoor activities. The general public are usually not affected
by the air quality within this AQI range.
Source: http://www.environment.nsw.gov.au/AQMS/search.htm
head(data)
CEAQI NWAQI
[1,] 99 92
[2,] 32 44
[3,] 70 82
[4,] 74 96
[5,] 95 100
...
[29,] 41 59
[30,] 48 57
[31,] 34 58
CE=data$CEAQI
NW=data$NWAQI
summary(CE)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 30.00 35.50 41.00 50.77 60.50 108.00
summary(NW)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 33.00 38.50 54.00 56.52 67.00 100.00
n = nrow(data)
table(CE >= 100)
## FALSE TRUE
## 30 1
Sample Exam Questions A 2018 Page 8 of 20
table(NW >= 100)
## FALSE TRUE
## 30 1
l lCE
N
W
40 60 80 100
l
l
l
l
l
l
l
l
l
l
l
lll l l
l
l
l
l
l
ll
l
ll
ll l
l
40 60 80 100
40
50
60
70
80
90
10
0
CE
N
W
Look over the R output and then answer the following questions.
(a) Begin by examining the air quality index (AQI) of Sydney’s central-east (CE) region.
(i) What is the mean AQI score for Sydney’s CE region during July?
(ii) Comment on the shape of the boxplot for Sydney’s CE region during July.
(iii) Would it be better to report the mean or median as the measure of centre for
Sydney’s CE region? Explain.
(iv) How many days in July was the air quality considered ‘poor’ in Sydney’s CE
region?
Sample Exam Questions A 2018 Page 9 of 20
(b) The scatter plot graphically shows the air quality index (AQI) relationship between
the two Sydney regions in the month of July.
(i) From the scatter plot, suggest a value for the correlation coefficient between
the two Sydney regions. Explain what it represents.
(ii) Suppose on the 28th of July, the air quality monitoring instrument was not
working in Sydney’s NW region, but the AQI value in Sydney’s CE region
was recorded as 40. Using the R output below, give an expression without
evaluation to estimate the AQI value in Sydney’s NW region.

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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 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BCPM0008 ECN 620 QBUS6830 ECSE-415 ECON5102 MGMT20005 MAT 653 ECO 480 COMP 5/600 CSSE 5600 ME 571 7CCMMS61 CS 550 COMP2865 MATH3075 STATS 506 QTM 220 ISYS2120 FIT5106 STAT 153 MKTG3112 ECON2102 ELE2018 COMP3031 MSIN0010 SCC461 MATH237 CS246 CSC3065 CSE 101 ICTWEB411 COMP9313 ECOS 2025 FN620 MATH11111 MATH 3018 KE1068 FINA 6112 C++ MISM 6202 MSIN0105 CSC8631 MECO6935 COMP4121 ACCT2011 DPBS1150 EEL 3701C F19PL OPM 661 CSE 310 CS918 BIOL30001 ACCT3563 BANK3600 MET CS622 MATH367 ACCT5907 Continuing COMP5048 COMP61021 CS3334 GSOE9210 CSCI-570 COMP 5322 HW 2 ALY-6020 IEOR 4706 DATA 200 EBUS504 CGE12412 COMP4108 7SSMM800 ECMM171P MATE50002 CS225 RS 6003 BH2274 ELEC3111 MAT6669 ECON-UA 227 PHIL 1012 7SSMM603 TB028A P6 ECS708 CSC 427 CS6476 CSE 250A A1A2 ITS61504 EECS 376 model HW6 UV0010 ECON 6074 MG4F7 CMP-7030Y INFO3008 BFIP013 ECON 5068 MATH6002 CS3483 PSTAT 131 C31FM CSOR W4246 AAEC 5307 DATA ELEC362 COMSM0047 ECN6006 MA20224 BST351 MATH 323 MATH323 AA2021 COMP3811 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 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