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STAT2402: Analysis of Observations

创建日期：2024-07-30 17:33:00

所属分类：统计学statistics

标签： STAT2402

Analysis of Observations

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STAT2402: Analysis of Observations

Important: This assignment is assessed, and carries weight 20% towards your final mark

for the STAT2402 unit.

Attach a properly completed assignment cover sheet (available in the reception area of the

Department of Mathematics and Statistics or on LMS) to the front of your solutions. You

may hand in your work during the computer lab of STAT2402 on 18 September or via the

assignment box for STAT2402 in the Mathematics Computing Laboratory (on the right from

the entry door). You may instead email soft copies of your solutions to my UWA email.

Unless special considerations were granted, any student failing to submit work by the deadline

will receive a penalty for late submission (10% per day late, 0 marks after 7 days). Please

ensure that you write your name and student number on your work.

Plagiarism: You are encouraged to discuss assignments with other students and to solve

problems together. However, the work that you submit must be your sole effort (i.e. not copied

from anyone else). If you are found guilty of plagiarism you may be penalised.

You are reminded of the Faculty of Engineering, Computing and Mathematics’ ‘Policy on

Plagiarism’:

http://www.ecm.uwa.edu.au/students/archived/exams/dishonesty

The two assignment questions involve the analysis of some data. The data sets mentioned

are available from LMS. If you have difficulty accessing the data sets you should contact the

lecturer immediately.

You should hand in a mini-report for each question. The mini-report for each exercise

should be no longer than a single side of A4 paper (excluding any relevant Figures, Tables, R

code or computer output, which can be attached as an appendix). You will be marked down

for exceeding this page limit. The aim of the mini-report is to convey the aims, methodology

and results of your data analysis in a concise, readable fashion. It is strongly recommended

that you structure your report into sections, along the following lines.

• Introduction: Introduce the data and the aims of the analysis.

• Methodology: Describe the statistical methods that you use (technical details not required).

• Results: Describe the results of your analysis.

• Discussion: Draw conclusions adn interpretations (based on your results) as appropriate.

Discuss any interesting issues arising from your analysis.

Marks for each mini-report will be awarded (with a relative weighting of 2:3) for

• Exposition: your mini-reports should be well organised. You should aim to write in a

concise, yet readable, manner.

• Statistical content: marks will be awarded for the correct use of appropriate statistical

techniques, and for the correct interpretation of results from these techniques.

Task 1. Poh and Khan (2019, A support learning programme for first year mathematics,

IJMEST (to appear)) investigated the effect of attending Support Learning Tutorial (SLT)

programme for a low level Mathematics unit and the performance of students. The data is in

the file Poh1.txt. The variables in the data are self explanatory. Note that Group indicates if

the student attend the SLT sessions (SLT) or not (NonSLT). Count is the number of students

in the corresponding level (Pass or Fail) of Result.

Read the data into R Studio and obtain some summary tables.

Poh <- read.table("Poh1.txt", header=T, sep = "\t")

Poh$Year<-factor(Poh$Year)

Semester 2, 2019, page 1 Set 1 Due date: Friday, 2019-09-20, 12:00noon

Department of Mathematics and Statistics STAT2402: Analysis of Observations

Table = xtabs(Count ~ Group + Result + Semester + Repeat, data=Poh)

ftable(Table)

Table1 = xtabs(Count ~ Group + Result + Repeat + Cohort, data=Poh)

B<-ftable(Table1)B

library(data.table)

as.data.table(B)

Table2=xtabs(Count ~ Group + Repeat, data=Poh)

Table2

You may conduct a chi squared test and Fisher’s exact test on the two-way table of Results

and Group to further explore the data.

Before a logistic regression analysis can be fitted, the data needs to be expanded to individual

records. (See the R code in lecture 4.) Further, a second data set Poh2.txt contains

demographic information on the sutdents. Read this data and merge your expanded records

with this.

Aims of the analysis: Investigate whether there is any evidence that the SLT programme

affects student peformance. You should also consider the effects of the other variables in the

data set. Further, you should suggest weaknesess in the study and recommend ways to improve

it.

Task 2. The data set “ex2011” in the R package “Sleuth3” contains the launch temperatures

(degrees Fahrenheit) and an indicator of O-ring failures for 24 space shuttle launches prior to

the space shuttle Challenger disaster of January 28, 1986.

On January 27, 1986, the night before the launch, an engineer recommended to the National

Aeronautics and Space Administration (NASA) that the space shuttle not be launched

in the cold weather. The forecasted temperature for the Challenger launch was 31 degrees

Fahrenheit—the coldest launch ever. After an intense 3-hour telephone conference, officials

decided to proceed with the launch.

Aims of the analysis: Investigate whether there is any evidence that the probability of

having O-ring failures is related to (some polynomial of) the temperature at launch time. If

so, provide an interpretation for the regression parameters in your model. Also, provide an

estimate for the probability of having O-rings failures if the launch temperature is 31 degrees

Fahrenheit. Carefully discuss any possible problem with your estimate.

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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 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