Department of Mathematics and Statistics STAT2402: Analysis of Observations
Important: This assignment is assessed, and carries weight 20% towards your final mark
for the STAT2402 unit. Your work for this assignment must be submitted by 12noon on Friday,
20 September 2019.
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.
Semester 2, 2019, page 2 Set 1 Due date: Friday, 2019-09-20, 12:00noon
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