Units

---

Sample subject outline - Semester 2 2013

Note: Subject outlines often change before the semester begins. Below is a sample outline.

Rationale

There is an ever-increasing demand for engineers to understand and accurately model real world situations in order to better control or use resources and lower the costs. This has prompted engineers to use methods of decision making, such as statistical modelling and optimization, to design and produce products and systems both economically and efficiently. Statistical and optimization techniques are being used in a wide spectrum of industries, including electrical, transport, automotive, construction, aerospace, and manufacturing industries. This unit develops advanced statistical and optimization methods.

Aims

This unit aims to introduce you to the fundamental concepts of how to conduct research in a non-experimental setting (observational). While the fundamental concepts of various statistical methods will be taught, the prime focus will be on the correct application and interpretation of a variety of statistical methods to data.

As well as statistical methods, you are introduced to techniques, applications and optimisation methods. All topics will be discussed referring to applications in a wide spectrum of industries such as electrical, transport, automotive and manufacturing industries.

Objectives

On completion of this unit you will be able to demonstrate:

1. Formulation and solving of complex real world engineering problems as mathematical problems.
2. Application and interpretation of variety of statistical methods to data.
3. Selection and application of optimisation methods to real world engineering problems.

Content

Section 1: Statistics
Topic 1: Elements in The Process of Stochastic Research (supplemental review-not in text)
Topic 2: Statistics Fundamentals and Review (Appendix A)
Topic 3: Linear regression analysis (Chapters 3 and 4): Continuous dependent variable models
Topic 4: Simultaneous Equations Models (Chapter 5): Systems of continuous dependent variable models
Topic 5: Count Data Models (Chapter 10): Models of dependent variables that follow a count process
Topic 6: Time series analysis

Section 2: Optimisation
Topic 1: Introduction to optimization
Topic 2: Classical Optimization Techniques (Single Value, Multi-variable, Convex)
Topic 3: Linear Programming (Simplex)
Topic 4: Nonlinear Programming (Elimination, Unconstrained, Constrained, Direct, indirect)
Topic 5: Geometric and Dynamic Programming

Approaches to Teaching and Learning

Teaching Mode: 4 hours per week
Lectures: 2 hrs per week
Tutorial: 1 hrs per week
Laboratories: 1 hour per week

This unit will be delivered in the form of combined lecture and tutorial classes. Teaching approaches will include formal lectures, problem solving activities, computer simulations and technical discussions. Combined lectures and tutorials will be in four hour sessions per week. Theory will be applied in practice through exposure to industry applications and settings.

You will also gain experience at independent learning from various reference sources and will be encouraged to work and learn cooperatively with others through interacting with peers in tutorials and project work. The project also will allow you to apply the theoretical material to a real world engineering project. Tutorial sessions and laboratory sessions are designed to integrate theory from lectures with practice. Tutorial tasks and assessments are problem based giving you an opportunity to assess your knowledge using worked solutions.

Assessment

Assessment will include practical problem solving and project work as well as a final exam.Formative feedback will be provided through worked examples during tutorials and during class. Summative feedback related to both problem-based projects will be provided in the form of written comments on returned reports. Written and verbal feedback will be provided for laboratory work and assessment results; completed CRA sheets will be available for final exam.

Assessment name: Project (applied)
Description: Statistics and optimisation application project
Submit a project report as a group.
Relates to objectives: 1, 2
Weight: 30%
Internal or external: Internal
Group or individual: Group
Due date: Week 7

Assessment name: Problem Solving Task
Description: Statistical and optimisation problem solving
Submit a report based on statistical and optimisation solutions.
Relates to objectives: 1, 3
Weight: 30%
Internal or external: Internal
Group or individual: Individual
Due date: Week 13

Assessment name: Examination (written)
Description: Final Exam
You will answer questions and problems related to key concepts in statistics and optimisation covered in this unit during the semester. The exam will be aimed at evaluating conceptual knowledge and application, as well as problem solving ability, for the topics presented throughout the semester.
Relates to objectives: 1, 2, 3
Weight: 40%
Internal or external: Internal
Group or individual: Individual
Due date: End of Semester

Academic Honesty

QUT is committed to maintaining high academic standards to protect the value of its qualifications. To assist you in assuring the academic integrity of your assessment you are encouraged to make use of the support materials and services available to help you consider and check your assessment items. Important information about the university's approach to academic integrity of assessment is on your unit Blackboard site.

A breach of academic integrity is regarded as Student Misconduct and can lead to the imposition of penalties.

Resource materials

1. Washington, S., M. Karlaftis, and F. Mannering. Statistical & Econometric Methods for Transportation Data Analysis. Chapman and Hall/CRC. Boca Raton, FL. (2003).

2. Rao, S. S., Engineering optimization : theory and practice, Hoboken, N.J. : John Wiley & Sons, c2009.

Risk assessment statement

None.

Disclaimer - Offer of some units is subject to viability, and information in these Unit Outlines is subject to change prior to commencement of semester.

Last modified: 07-Jun-2012