MATHS 2107 - Statistics & Numerical Methods II

North Terrace Campus - Semester 2 - 2021

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.

  • General Course Information
    Course Details
    Course Code MATHS 2107
    Course Statistics & Numerical Methods II
    Coordinating Unit School of 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.
    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 Staff

    Course Coordinator: Dr Trent Mattner

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    Students who successfully complete the course will be able to:
    1. Demonstrate understanding of the probability and statistical foundations of data analysis.
    2. Demonstrate understanding of the importance of assumption checking for valid statistical analysis, and be able to perform assumption checking.
    3. Demonstrate understanding of common numerical methods and how they are used to obtain approximate solutions to otherwise intractable mathematical problems.
    4. 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.
    5. Analyse and evaluate the accuracy of common numerical methods.
    6. Apply standard statistical and numerical methods using Matlab.
    7. Interpret results from the application of standard statistical and numerical methods.
    8. 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)
    1. Probability background
    2. Statistical background
    3. Inference for population means
    4. Inference for multiple population means
    5. Inference for categorical variables
    6. Linear regression
    Numerical Methods (weeks 7-12)
    1. Interpolation
    2. Numerical integration and differentiation
    3. Numerical linear algebra
    4. Iterative solution of linear and nonlinear systems
    5. Numerical solution of ordinary differential equations
    Practicals

    Practicals are held fortnightly, commencing week 1.

    Tutorials

    Tutorials are held fortnightly, commencing week 2.

  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    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
    More details will be announced later.
    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 Access Adelaide.

  • 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.

  • Student Support
  • Policies & Guidelines
  • 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’s disciplinary procedures.

The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.