## MATHS 2107 - Statistics & Numerical Methods II

### North Terrace Campus - Semester 2 - 2023

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 Statistics & Numerical Methods II Mathematical Sciences Semester 2 Undergraduate North Terrace Campus 3 Up to 5 hours per week. Y 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) ECON 1008, MATHS 2104, STATS 1000, STATS 1004, STATS 1005, STATS 1504 Available to Bachelor of Engineering students only. 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.

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.

All

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.

4,5,6,7

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.

7,8

Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.

6,8

Attribute 7: Digital capabilities

Graduates are well prepared for living, learning and working in a digital society.

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.

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
 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
More details will be announced later.
##### Assessment Related Requirements
To pass the course the student must attain:
1. an aggregate score of 50%, and
2. at least 40% on the final examination.
##### Assessment Detail
Written 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.
##### Submission
Assignments 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)
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 studentâ€™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.

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