Course Title: Aviation Mathematics

Part A: Course Overview

Course Title: Aviation Mathematics

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2314

City Campus

Undergraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

Sem 1 2016

MATH2314

City Campus

Undergraduate

171H School of Science

Face-to-Face

Sem 1 2017,
Sem 1 2018,
Sem 1 2019

Flexible Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2299

Voc Training Ctre of Hong Kong

Undergraduate

171H School of Science

Face-to-Face

OFFSep2018 (VA1)

Course Coordinator: Dr Xu Zhang

Course Coordinator Phone: +61 3 9925 3161

Course Coordinator Email: xu.zhang@rmit.edu.au

Course Coordinator Location: 8.9.77

Course Coordinator Availability: By appointment, by email


Pre-requisite Courses and Assumed Knowledge and Capabilities

None.


Course Description

This course provides you with mathematical statistical skills appropriate to the aviation industry. This includes statistical techniques, specifically descriptive statistics, inferential statistics, regression and time series analysis. The course also includes fundamental problem solving skills, such as problem analysis, generalisation, cross-checking, goal modification and interpretation of solutions. All applications will involve the use of Microsoft Excel to perform any of the calculations associated with these Statistical techniques. Examples will be drawn predominantly from the Aviation Industry.


Objectives/Learning Outcomes/Capability Development

This course develops and assesses the following program learning outcomes for the Bachelor of Applied Science (Aviation):

Problem-solving and design

  • Apply problem solving, design and decision-making methodologies to develop components, systems and/ or processes to meet specified requirements, including innovative approaches to synthesise alternative solutions, concepts and procedures, while demonstrating information skills and research methods.

Abstraction and modelling

  • Apply abstraction, mathematics and discipline fundamentals to analysis, design and operation, using appropriate computer software, laboratory equipment and other devices, ensuring model applicability, accuracy and limitations.

Coordination and communication

  • Communicate and coordinate proficiently by listening, speaking, reading and writing English for professional practice, working as an effective member or leader of diverse teams


On completion of this course you should be able to:

  1. Present and describe data using various numerical and graphical procedures.
  2. Utilize statistical inference about sample information to arrive at probable conclusions.
  3. Undertake estimation and hypothesis testing.
  4. Use regression and correlation analysis to reveal relationships between variables and to produce forecasts of the future values of strategic variables.
  5. Apply general problem solving and modeling skills including problem analysis, generalization, cross-checking, goal modification and interpretation of solutions
  6. Demonstrate mastery of advanced functions in Excel relevant to solving mathematical problems and communicate mathematical findings and results in an unbiased manner to a non-technical audience such as decision makers, stakeholders and the general public.


Overview of Learning Activities

  • Lectures where underlying theory will be presented.
  • Regular computer laboratory class that will reinforce the material covered in lectures and in your independent study.
  • Assignments to practice the usage of relevant software packages.


Overview of Learning Resources

A list of recommended texts will be provided.

You will have access to extensive course materials made available online through myRMIT, including digitized readings, lecture notes and a detailed study program, external internet links and access to RMIT Library online and hardcopy resources.

A Library Guide is available at http://rmit.libguides.com/mathstats


Overview of Assessment

This course has no hurdle requirements.

 

Assessment Tasks:

Assessment Task 1: Assignment 1

Weighting 5%

This assessment task supports CLO 1     

 

Assessment Task 2: Mid semester test

Weighting 20%

This assessment task supports CLOs 1 - 3

 

Assessment Task 3: Assignment 2

Weighting 25%

This assessment task supports CLOs  4 - 6

 

Assessment Task 4: Final Exam  

Weighting 50% 

This assessment task supports CLOs 1 – 5