MATHS 2107 - Statistics & Numerical Methods II
North Terrace Campus - Semester 2 - 2020
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General Course Information
Course Details
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 5 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. Assessment Ongoing assessment, examination. Course Staff
Course Coordinator: Dr Trent Mattner
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
Students 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) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
All Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
4,5,6,7 Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
7,8 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
6,8 -
Learning Resources
Required Resources
Course 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 Learning
All course materials (except the textbook) will be made available on MyUni. -
Learning & Teaching Activities
Learning & Teaching Modes
Short 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.Workload
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 Summary
Statistics (weeks 1-6)- Probability background
- Statistical background
- Inference for population means
- Inference for multiple population means
- Inference for categorical variables
- Linear regression
- Interpolation
- 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
Tutorials are held fortnightly, commencing week 2.
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Assessment
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.
Assessment Summary
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 30 % All Tests (2) Summative 30 % All Exam Summative 30 % All Assessment Detail
No information currently available.
Submission
No information currently available.
Course Grading
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 .
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Student Feedback
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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Fraud Awareness
Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.
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