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UNSW SCIENCE
School of Maths and
Statistics
Course outline
MATH2089
Numerical Methods and Statistics
Term 2, 2022
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Staff
Position
Name
Email
Room
Lecturer-in-charge
(Numerical Methods)
Prof Frances Kuo
f.kuo@unsw.edu.au
RC-3061
Lecturer-in-charge
(Statistics)
Dr Tom Stindl
t.stindl@unsw.edu.au
RC-4073
Please refer to your Timetable on MyUNSW for your Lecture Tut, Lab enrolment days and times.
Timetable weblink: http://timetable.unsw.edu.au/2022/MATH2089.html#S2S
Administrative Contacts
Please visit the School of Mathematics and Statistics website for a range of information on School
Policies, Forms and Help for Students.
For information on Courses, please go to “Current Students” and either Undergraduate and/or
Postgraduate”, Course Homepage” for information on all course offerings,
The “Student Notice Board” can be located by going to the “Current Students” page; Notices
are posted regularly for your information here. Please familiarise yourself with the information
found in these locations. The School web page is: https://www.maths.unsw.edu.au
If you cannot find the answer to your queries on the web you are welcome to contact the Student
Services Office directly.
By email Undergraduate ug.mathsstats@unsw.edu.au
By phone: 9385 7011 or 9385 7053
Should we need to contact you, we will use your official UNSW email address of in the first
instance. It is your responsibility to regularly check your university email account. Please
state your student number in all emails.
Course Information
Assumed knowledge / Pre-Requisite: MATH1231 or MATH1241 or MATH1251 or DPST1014
Exclusions: BEES2041, CVEN2002, CVEN2702, MATH2099, MATH2301, MATH2801, MATH2859,
MATH2901, ECON3209
We are aware some course exclusions on the Handbook may be different to the School website. We
are in the process of updating this information. Meanwhile, students should be following the
Handbook course information with the School website information as a supplement.
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Course Description
This course gives an introduction to numerical methods and statistics essential in a wide range of
engineering disciplines.
Numerical methods: Computing with real numbers. Numerical differentiation, integration,
interpolation and curve fitting (regression analysis). Solution of linear and nonlinear algebraic
equations. Matrix operations and applications to solution of systems of linear equations, elimination
and tri-diagonal matrix algorithms. Introduction to numerical solution of ordinary and partial
differential equations.
Statistics: Exploratory data analysis. Probability and distribution theory including the Binomial,
Poisson and Normal distributions. Large sample theory including the Central Limit Theorem.
Elements of statistical inference including estimation, confidence intervals and hypothesis testing.
One sample and two-sample t-tests. Simple linear regression and analysis of variance.
In each component, applications will be drawn from a variety of engineering disciplines. Matlab will
be used extensively as a practical tool for both numerical and statistical computations and to
illustrate theoretical concepts.
Assessment and Deadlines
Assessment
Weighting %
Numerical Methods
Online quizzes (8)
10
1.25% each for 8 modules
Online tests (2)
10
Final Exam
30
50
Statistics
Online lectures & quizzes (10)
10
1% each for 10 modules
Midterm Test
10
Final Exam
30
50
100
To pass this course, you must achieve at least 50% in your final combined mark for the course.
Additionally, you must achieve at least 40% in Numerical Methods and 40% in Statistics, that is, a
minimum of 20/50 in each component.
Late Submission of Assessment Tasks
No late submissions will be accepted. (Where "late" in this context means after any extensions
granted for Special Consideration or Equitable Learning Provisions.)
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Course Schedule
The course will include material taken from some of the following topics. This is should only
serve as a guide as it is not an extensive list of the material to be covered and the timings are
approximate. The course content is ultimately defined by the material covered in lectures.
Numerical Method
Weeks
Topic
Reading (if applicable)
1
Numerical Computing
Refer to Moodle page
2
Linear Systems
Refer to Moodle page
3
Linear Systems
Refer to Moodle page
4
Least Squares and Polynomial Interpolation
Refer to Moodle page
5
Nonlinear Equations
Refer to Moodle page
7
Numerical Differentiation and Integration
Refer to Moodle page
8
Ordinary Differential Equations
Refer to Moodle page
9
Partial Differential Equations
Refer to Moodle page
10
Partial Differential Equations and Review
Refer to Moodle page
Statistics
Weeks
Topic
Reading (if applicable)
1
Probability (revision); Descriptive statistics
Lecture notes
2
Random variables
Lecture notes
3
Special random variables
Lecture notes
4
Sampling distributions and the Central Limit
Theorem
Lecture notes
5
Confidence intervals for means and proportions
Lecture notes
7
Hypothesis testing
Lecture notes
8
Inference concerning differences in means
Lecture notes
9
Regression analysis
Lecture notes
10
Analysis of variance
Lecture notes
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Textbooks
Numerical Methods
Recommended textbook:
S. S. Rao, Applied Numerical Methods for Engineers and Scientists, Prentice Hall, Upper Saddle
River, N.J., 2002. This book is available for purchase in the UNSW bookshop and is also in the
UNSW library High Use Collection.
Further resources on MatLab:
•
J. H. Mathews and K. D. Fink, Numerical methods using MATLAB, Upper
Saddle River, N.J: Pearson, 2004.
•
C. Moler, Numerical Computing with Matlab, SIAM,
2004, http://www.mathworks.com/moler/
•
Gilat, MATLAB: an introduction with applications, New York; Chichester: Wiley, 2005.
Statistics
Recommended Text:
J. Devore and N. Farnum, Applied Statistics for Engineers and Scientists, 2nd Edition,
2005 Duxbury Press, Thomson Publishers (or 3rd edition of this book).
Additional Reading:
Any text with ”Statistics” and “Engineers” in its title. A quite comprehensive reference is:
D. Montgomery and G. Runger, Applied Statistics and Probability for Engineers,
5th Edition, 2011, Wiley (or a previous edition of this book)
Course Learning Outcomes (CLO)
CLO1 Identify risks associated with floating point computations
CLO2 Demonstrate a basic knowledge of the techniques for accurate and efficient solution of
models based on linear and nonlinear systems of equations, ordinary differential equations and
partial differential equations
CLO3 Apply these techniques to practical problems in Engineering
CLO4 Use Matlab for the implementation and application of numerical methods and the
visualization of results
CLO5 Apply various graphical and data analysis methods for summarizing and understanding
data
CLO6 Apply various statistical models and methods for drawing conclusions and
making decisions under uncertainty in engineering contexts
CLO7 Apply Matlab for graphical and statistical analysis
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Moodle
Log in to Moodle to find announcements, general information, notes, lecture slide, classroom tutorial
and assessments etc.
https://moodle.telt.unsw.edu.au
School and UNSW Policies
The School of Mathematics and Statistics has adopted a number of policies relating to enrolment,
attendance, assessment, plagiarism, cheating, special consideration etc. These are in addition to the
Policies of The University of New South Wales. Individual courses may also adopt other policies in
addition to or replacing some of the School ones. These will be clearly notified in the Course Initial
Handout and on the Course Home Pages on the Maths Stats web site.
Students in courses run by the School of Mathematics and Statistics should be aware of the School and
Course policies by reading the appropriate pages on the Maths Stats web site starting at:
https://www.maths.unsw.edu.au/currentstudents/assessment-policies
The School of Mathematics and Statistics will assume that all its students have read and understood
the School policies on the above pages and any individual course policies on the Course Initial
Handout and Course Home Page. Lack of knowledge about a policy will not be an excuse for failing to
follow the procedure in it.
Academic Integrity and Plagiarism
UNSW has an ongoing commitment to fostering a culture of learning informed by academic integrity.
All UNSW staff and students have a responsibility to adhere to this principle of academic integrity.
Plagiarism undermines academic integrity and is not tolerated at UNSW. Plagiarism at UNSW is
defined as using the words or ideas of others and passing them off as your own.
The UNSW Student Code provides a framework for the standard of conduct expected of UNSW
students with respect to their academic integrity and behaviour. It outlines the primary obligations of
students and directs staff and students to the Code and related procedures.
In addition, it is important that students understand that it is not permissible to buy essay/writing
services from third parties as the use of such services constitutes plagiarism because it involves using
the words or ideas of others and passing them off as your own. Nor is it permissible to sell copies of
lecture or tutorial notes as students do not own the rights to this intellectual property.
If a student breaches the Student Code with respect to academic integrity, the University may take
disciplinary action under the Student Misconduct Procedure.
The UNSW Student Code and the Student Misconduct Procedure can be found at:
https://student.unsw.edu.au/plagiarism
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An online Module “Working with Academic Integrity” (https://student.unsw.edu.au/aim) is a six-lesson
interactive self-paced Moodle module exploring and explaining all of these terms and placing them
into your learning context. It will be the best one-hour investment you’ve ever made.
Plagiarism
Plagiarism is presenting another person's work or ideas as your own. Plagiarism is a serious breach
of ethics at UNSW and is not taken lightly. So how do you avoid it? A one-minute video for an
overview of how you can avoid plagiarism can be found https://student.unsw.edu.au/plagiarism.
Additional Support
ELISE (Enabling Library and Information Skills for Everyone)
ELISE is designed to introduce new students to studying at UNSW.
Completing the ELISE tutorial and quiz will enable you to:
▪ analyse topics, plan responses and organise research for academic writing and other
assessment tasks
▪ effectively and efficiently find appropriate information sources and evaluate relevance to your
needs
▪ use and manage information effectively to accomplish a specific purpose
▪ better manage your time
▪ understand your rights and responsibilities as a student at UNSW
▪ be aware of plagiarism, copyright, UNSW Student Code of Conduct and Acceptable Use of
UNSW ICT Resources Policy
▪ be aware of the standards of behaviour expected of everyone in the UNSW community
▪ locate services and information about UNSW and UNSW Library
Some of these areas will be familiar to you, others will be new. Gaining a solid understanding of all
the related aspects of ELISE will help you make the most of your studies at UNSW.
The ELISE training webpages:
https://subjectguides.library.unsw.edu.au/elise/aboutelise
Equitable Learning Services (ELS)
If you suffer from a chronic or ongoing illness that has, or is likely to, put you at a serious
disadvantage, then you should contact the Equitable Learning Services (previously known as
SEADU) who provide confidential support and advice.
They assist students:
• living with disabilities
• with long- or short-term health concerns and/or mental health issues
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• who are primary carers
• from low SES backgrounds
• of diverse genders, sexes and sexualities
• from refugee and refugee-like backgrounds
• from rural and remote backgrounds
• who are the first in their family to undertake a bachelor-level degree.
Their web site is: https://student.unsw.edu.au/els/services
Equitable Learning Services (ELS) may determine that your condition requires special arrangements
for assessment tasks. Once the School has been notified of these, we will make every effort to meet
the arrangements specified by ELS.
Additionally, if you have suffered significant misadventure that affects your ability to complete the
course, please contact your Lecturer-in-charge in the first instance.
Academic Skills Support and the Learning Centre
The Learning Centre offers academic support programs to all students at UNSW Australia. We assist
students to develop approaches to learning that will enable them to succeed in their academic study.
For further information on these programs please go to:
http://www.lc.unsw.edu.au/services-programs
Applications for Special Consideration for Missed Assessment
Please adhere to the Special Consideration Policy and Procedures provided on the web page below
when applying for special consideration.
https://student.unsw.edu.au/special-consideration
Please note that the application is not considered by the Course Authority, it is considered by a
centralised team of staff at the Nucleus Student Hub.
The School will contact you (via student email account) after special consideration has been granted
to reschedule your missed assessment, for a lab test or paper-based test only.
For applications for special consideration for assignment extensions, please note that the new
submission date and/or outcome will be communicated through the special consideration web site
only, no communication will be received from the School.
For Dates on Final Term Exams and Supplementary Exams please check the “Key Dates for Exams”
ahead of time to avoid booking holidays or work obligations.
https://student.unsw.edu.au/exam-dates
If you believe your application for Special Consideration has not been processed, you should email
[email protected].au immediately for advice.
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Course Evaluation and Development (MyExperience)
Student feedback is very important to continual course improvement. This is demonstrated within the
School of Mathematics and Statistics by the implementation of the UNSW online student survey
myExperience, which allows students to evaluate their learning experiences in an anonymous way.
myExperience survey reports are produced for each survey. They are released to staff after all
student assessment results are finalised and released to students. Course convenor will use the
feedback to make ongoing improvements to the course.