# Course Title: Mathematics and Statistics for Manufacturing Engineers

## Part A: Course Overview

Course Title: Mathematics and Statistics for Manufacturing Engineers

Credit Points: 12.00

## Terms

### Teaching Period(s)

MATH2185

City Campus

Undergraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

Sem 1 2009,
Sem 1 2010,
Sem 1 2011,
Sem 1 2012,
Sem 1 2013,
Sem 1 2014,
Sem 1 2015,
Sem 1 2016

MATH2185

City Campus

Undergraduate

171H School of Science

Face-to-Face

Sem 1 2017,
Sem 1 2018,
Sem 1 2019

Course Coordinator: Dr Yan Ding

Course Coordinator Phone: +61 3 9925 3217

Course Coordinator Email: yan.ding@rmit.edu.au

Course Coordinator Location: 8--9--19

Pre-requisite Courses and Assumed Knowledge and Capabilities

Required Prior Study

MATH2117 Engineering Mathematics.
MATH2118 Further Engineering Mathematics.
Or equivalent first year university mathematics courses

Assumed Knowledge

• Ability to apply the techniques of integral and differential calculus to formulate and solve problems involving change and approximation, including problems with more than one variable.
• Ability to recognize the properties of the common mathematical functions (polynomials, exponentials and hyperbolic functions, logarithms, inverse trigonometric and inverse hyperbolic functions) and their combinations commonly found in engineering applications.
• Ability to recognize the properties of vectors and curves in space; apply the techniques of vector analysis to problems involving three-dimensional geometry and motion.
• Ability to recognize the properties of complex numbers; apply complex numbers to the solution of algebraic equations.
• Ability to formulate and solve differential equations.
• Ability to recognize the properties of matrices; apply the techniques of matrix analysis to problems involving three-dimensional geometry and transformations in three-dimensional space; calculate determinants of matrices; find eigenvalues and eigenvectors.
• Ability to recognize the basic properties of infinite series and apply power series to problems involving approximation of functions.
• Ability to create a Taylor series approximation to a function of one variable and determine its radius of convergence.

Course Description

Mathematics for Engineers is a single semester course consisting of two main components: Mathematical Transforms and Statistics. The course content has been selected, in consultation with the discipline of Aeronautical, Mechanical and Manufacturing Engineering to provide the necessary mathematical and statistical training that will assist and expand your learning experience within your field of study. There are six key topics:  Laplace transforms; Fourier series;  Discrete Fourier transform;  Descriptive statistics;  Inferential statistics; and  Regression and Correlation.

Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes for

• BH068 - Bachelor of Engineering (Advanced Manufacturing and Mechatronics) (Honours)
• BH086 - Bachelor of Science (Advanced Manufacturing and Mechatronics)(Honours) / Bachelor of Business (International Business)

Knowledge and Skill Base:

1.2 Conceptual understanding of mathematics, numerical analysis, statistics, computer and information sciences which underpin the engineering discipline

On successful completion of this course, you should be able to:

1. Manipulate Laplace transforms and their inverses, solve appropriate initial value problems’ using Laplace transforms.
2. Understand and apply Fourier series expansion to construct periodic continuation of functions.
3. Apply discrete Fourier transform to determine system performance, make use of software package Matlab to utilize the in-built F. F. T algorithm
4. Construct graphical displays of science/engineering data and interpret the role of such displays in data analysis.
5. Apply basic statistical inference techniques, including confidence intervals, hypothesis testing and analysis of variance, to science/engineering problems.
6. Employ appropriate regression models to determine statistical relationships.

Overview of Learning Activities

This course is presented using a mixture of classroom instruction; in-class problem-based exercises; WebLearn quizzes and Weblearn tests; and problem-based computer laboratory assignments.

Primarily you will be learning in face-to-face lectures. An online course site will be used to disseminate course materials; to provide you access to online Weblearn quizzes for independent learning and to online Weblearn tests for self-assessment; and to submit problem-based computer laboratory assignments.

The key concepts and their applications will be explained and illustrated with many examples in lectures. Problem-based weekly exercises/computer labs will build your capacity to solve problems and to think analytically and critically. The computer laboratory sessions are designed to assist students to use Microsoft Excel for performing the basic statistical analysis of data collected from a broad range of engineering fields.

Mathematical and statistical theories and applications will also be reinforced through the online WebLearn quizzes and Weblearn tests. Weblearn quizzes are designed to provide instant feedback and can be attempted as many times as you like until proficiency in the learning objectives is achieved before you attempt the corresponding Weblearn tests.

Assignments will enhance your understanding of the statistical concepts and help you to develop your capacity to analyse data. Typical exam style questions are provided to assist in your preparation for the final examination.

Overview of Learning Resources

A prescribed textbook will be nominated.

The course Canvas site links to the Google site where you will find:
1. Teaching schedule and suggested reading
2. Assessment guide and assessment schedule.
3. Guide to WebLearn tests.
4. Lecture slides.
5. Computer lab guides for using Excel in statistics
6. Assignment papers
7. Recommended references.
8. Tables and formula sheets.
9. Weekly exercises and answers.
10. Practice exam questions.
You will have access to the software package MATLAB, Microsoft Excel and Minitab through myDesktop.

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

Overview of Assessment

☒This course has no hurdle requirements.

Early Assessment Task: Two Weblearn Tests (Weeks 2-5),
Weighting: 6.5%+6.5%=13%
This assessment task supports CLO 1

Assessment Task 2: Four Weblearn Tests
Weighting: 6.5% + 6.5% + 4% + 4% = 21%
This assessment task supports CLOs 1-6

Assessment Task 3: Four Statistics assignments
Weighting: 4% + 4% + 4% + 4% = 16%
This assessment task supports CLOs 4-6

Assessment Task 4: Final Exam (2-hour open book)
Weighting: 50%
This assessment task supports CLOs: 1-6