MATHS 2107 - Statistics & Numerical Methods II
North Terrace Campus - Semester 2 - 2024
General Course Information
Course Code MATHS 2107 Course Statistics & Numerical Methods II Coordinating Unit Mathematical Sciences Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week. Available for Study Abroad and Exchange Y Prerequisites MATHS 1012 and (ENG 1002 or ENG 1003 or COMP SCI 1012 or COMP SCI 1101 or COMP SCI 1102 or COMP SCI 1201 or MECH ENG 1100 or MECH ENG 1102 or MECH ENG 1103 or MECH ENG 1104 or MECH ENG 1105 or C&ENVENG 1012) Incompatible ECON 1008, MATHS 2104, STATS 1000, STATS 1004, STATS 1005, STATS 1504 Restrictions Available to Bachelor of Engineering students only. Course Description Statistics and data analysis are an essential part of a modern engineer's toolkit. So are numerical methods for solving a variety of mathematical problems that arise in engineering practice. The course provides an introduction to probability and statistics; inference for population means, multiple population means and categorical variables; and linear regression. The course also covers interpolation methods, numerical integration, linear systems and factorisations, iterative solutions of linear and nonlinear systems, and numerical methods in ordinary differential equations. Applications in engineering are emphasised throughout.
Course Coordinator: Mr Max Glonek
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesStudents who successfully complete the course will be able to:
- Demonstrate understanding of the probability and statistical foundations of data analysis.
- Demonstrate understanding of the importance of assumption checking for valid statistical analysis, and be able to perform assumption checking.
- Demonstrate understanding of common numerical methods and how they are used to obtain approximate solutions to otherwise intractable mathematical problems.
- Derive numerical methods for various mathematical operations and tasks, such as interpolation, differentiation, integration, the solution of linear and nonlinear equations, and the solution of differential equations.
- Analyse and evaluate the accuracy of common numerical methods.
- Apply standard statistical and numerical methods using Matlab.
- Interpret results from the application of standard statistical and numerical methods.
- Write efficient well-documented Matlab code and present statistical and numerical results in an informative way.
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s)
Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
Attribute 2: Creative and critical thinking, and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.
Attribute 3: Teamwork and communication skills
Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.
Attribute 4: Professionalism and leadership readiness
Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.
Attribute 7: Digital capabilities
Graduates are well prepared for living, learning and working in a digital society.
Required ResourcesCourse notes will be available in electronic form on MyUni.
The textbook for the Numerical Methods component of the course is Scientific Computing with MATLAB and Octave (fourth edition) by Quarteroni, Saleri and Gervasio, Springer, 2014. This is available in electronic form from the library.
Online LearningAll course materials (except the textbook) will be made available on MyUni.
Learning & Teaching Activities
Learning & Teaching ModesShort video recordings and online quizzes introduce course material.
Practicals develop skills in applying statistical and numerical methods in Matlab.
Tutorials consolidate understanding of course material and help develop problem-solving skills.
Assignments give you the opportunity to practise these skills and get feedback on your work.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Workload hours Study of notes/textbook/videos 72 Tutorials 6 18 Practicals 6 12 Quizzes 12 Assignments 6 42 TOTAL 156
Learning Activities SummaryStatistics (weeks 1-6)
- Probability background
- Statistical background
- Inference for population means
- Inference for multiple population means
- Inference for categorical variables
- Linear regression
- Numerical integration and differentiation
- Numerical linear algebra
- Iterative solution of linear and nonlinear systems
- Numerical solution of ordinary differential equations
Practicals are held fortnightly, commencing week 1.
Tutorials are held fortnightly, commencing week 2.
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
More details will be announced later.
Assessment Task Type Weighting Learning Outcomes Quizzes Formative and Summative 5 % All except 8 Practicals Formative and Summative 5 % All Assignments Formative and summative 20 % All Tests (2) Summative 20 % All Exam Summative 50 % All
Assessment Related RequirementsTo pass the course the student must attain:
- an aggregate score of 50%, and
- at least 40% on the final examination.
Assessment DetailWritten assignments are due every fortnight. The first written assignment will be released in Week 1 and due in Week 3.
Weeklly Mobius (online) quizzes are due by the end of the week.
Fortinghtly Computer Practicals are due by the end of the week in which they are scheduled.
There is a Statistics Test in Week 7 and a Numerical Methods Test in Week 12.
SubmissionAssignments must be submitted on MyUni.
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending
Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
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