## MATHS 2201 - Engineering Mathematics IIA

### North Terrace Campus - Semester 1 - 2018

Mathematical models are used to understand, predict and optimise engineering systems. Many of these systems are deterministic and are modelled using differential equations. Others are random in nature and are analysed using probability theory and statistics. This course provides an introduction to differential equations and their solutions and to probability and statistics, and relates the theory to physical systems and simple real world applications. Topics covered are: Ordinary differential equations, including first and second order equations and series solutions; Fourier series; partial differential equations, including the heat equation, the wave equation, Laplace's equation and separation of variables; probability and statistical methods, including sampling and probability, descriptive statistics, random variables and probability distributions, mean and variance, linear combinations of random variables, statistical inference for means and proportions and linear regression.

• General Course Information
##### Course Details
Course Code MATHS 2201 Engineering Mathematics IIA School of Mathematical Sciences Semester 1 Undergraduate North Terrace Campus 3 Up to 3.5 hours per week Y MATHS 1012 MATHS 2102 Available to Bachelor of Engineering students only Mathematical models are used to understand, predict and optimise engineering systems. Many of these systems are deterministic and are modelled using differential equations. Others are random in nature and are analysed using probability theory and statistics. This course provides an introduction to differential equations and their solutions and to probability and statistics, and relates the theory to physical systems and simple real world applications. Topics covered are: Ordinary differential equations, including first and second order equations and series solutions; Fourier series; partial differential equations, including the heat equation, the wave equation, Laplace's equation and separation of variables; probability and statistical methods, including sampling and probability, descriptive statistics, random variables and probability distributions, mean and variance, linear combinations of random variables, statistical inference for means and proportions and linear regression.
##### 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
1. Apply statistical analysis of a variety of experimental and observational studies.
2. Solve statistical problems using computational tools.
3. Derive mathematical models of physical systems.
4. Solve differential equations using appropriate methods.
5. Present mathematical solutions in a concise and informative manner.

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)
1,2,3,4
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
1,2,3,4
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
5
• technology savvy
• professional and, where relevant, fully accredited
• forward thinking and well informed
• tested and validated by work based experiences
2
Self-awareness and emotional intelligence
• a capacity for self-reflection and a willingness to engage in self-appraisal
• open to objective and constructive feedback from supervisors and peers
• able to negotiate difficult social situations, diffuse conflict and engage positively in purposeful debate
5
• Learning Resources
None.
##### Recommended Resources
Advanced Engineering Mathematics (10th edition) by Erwin Kreyszig.
##### Online Learning
This course uses MyUni exclusively to provide electronic resources, such as lecture notes, assignment papers, sample solutions, discussion boards, etc. It is recommended that students make appropriate use of these resources. Link to MyUni login page: https://myuni.adelaide.edu.au/webapps/login/
• Learning & Teaching Activities
##### Learning & Teaching Modes
This course relies on lectures as the primary delivery mechanism for the material. Tutorials supplement the lectures by providing exercises and example problems to enhance the understanding obtained through lectures. A sequence of written assignments provides assessment opportunities for students to gauge their progress and understanding.

The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

 Activity Quantity Workload Hours Lectures 32 80 Tutorials 11 28 Assignments 11 48 Total 156
##### Learning Activities Summary
 Week 1 Statistics Basic probability Week 2 Statistics Random variables Week 3 Statistics Confidence intervals - Hypothesis tests Week 4 Statistics Linear regression Week 5 ODEs First-order ordinary differential equations Week 6 ODEs Higher-order linear homogeneous constant coefficient ODEs Week 7 ODEs Higher-order linear nonhomogeneous constant-coefficient ODEs Week 8 ODEs Systems of first-order ODEs Week 9 ODEs Power series Week 10 ODEs Fourier series Week 11 PDEs Partial differential equations Week 12 PDEs Heat, wave and Laplace's equations
Tutorials begin in Week 2 and cover the material of the previous week.
• 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 Due Weighting Learning Outcomes Examination Summative Examination Period 70 % All Assignments Formative and Summative Weeks 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 30 % All
##### Assessment Related Requirements
To pass the course the student must attain:
1. an aggregate score of 50%, and
2. an exam score of at least 45%.
##### Assessment Detail
 Task Set Due Weighting Assignment 1 Week 2 Week 3 2.7 % Assignment 2 Week 3 Week 4 2.7 % Assignment 3 Week 4 Week 5 2.7 % Assignment 4 Week 5 Week 6 2.7 % Assignment 5 Week 6 Week 7 2.7 % Assignment 6 Week 7 Week 8 2.7 % Assignment 7 Week 8 Week 9 2.7 % Assignment 8 Week 9 Week 10 2.7 % Assignment 9 Week 10 Week 11 2.7 % Assignment 10 Week 11 Week 12 2.7 % Assignment 11 Week 12 Week 13 2.7 %
##### Submission
Assignments must be submitted according to the policies and procedures published on the Engineering Mathematics IIA MyUni site.

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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