# Course Title: Numerical Methods/Statistics for Engineers

## Part A: Course Overview

Course Title: Numerical Methods/Statistics for Engineers

Credit Points: 12.00

## Terms

### Teaching Period(s)

MATH2114

City Campus

145H Mathematical & Geospatial Sciences

Face-to-Face

Sem 2 2006,
Sem 2 2007,
Sem 2 2008,
Sem 2 2009,
Sem 2 2010,
Sem 2 2011,
Sem 2 2012,
Sem 2 2013,
Sem 2 2014,
Sem 2 2015,
Sem 2 2016

MATH2114

City Campus

171H School of Science

Face-to-Face

Sem 2 2017,
Sem 2 2018,
Sem 2 2019,
Sem 2 2020,
Sem 2 2021

Course Coordinator: Dr. Sevvandi Kandanaarachchi

Course Coordinator Phone: N/A

Course Coordinator Email: sevvandi.kandanaarachchi@rmit.edu.au

Course Coordinator Availability: By appointment, by email

Pre-requisite Courses and Assumed Knowledge and Capabilities

Required Prior Study

MATH2113 Differential Equations for Engineers.
Or equivalent first year university mathematics courses.

Assumed Knowledge

To successfully complete this course, you are expected to have capabilities consistent with the completion of VCE Mathematical Methods at Year 12 level. That is, you are expected to be able to correctly perform basic algebraic and arithmetic operations; solve quadratic and other algebraic equations; solve simultaneous linear equations; recognise and apply the concepts of function and inverse of a function; recognise the properties of common elementary functions (e.g. polynomials and trigonometric functions); sketch the common elementary functions; solve mathematical problems involving functions; find the derivative of elementary functions from first principles and combinations of elementary functions using the product, quotient and chain rules; find the anti-derivative (integral) of elementary functions; use integral calculus to determine the area under a curve.

Course Description

Numerical Methods/Statistics for Engineers is a single semester course consisting of two main components: Numerical Methods and Statistics. The course content has been selected, in consultation with the discipline of Chemical and Civil Engineering, to provide the necessary mathematical training that will assist and expand your learning experience.

The topics covered in the numerical methods component include solutions of nonlinear equations, ordinary differential equations and systems of differential equations, simultaneous linear equations, eigenvalues and eigenvectors and linear least-squares.

Topics covered in the statistical component include descriptive statistics, inferential statistics, linear regression and correlation.

Objectives/Learning Outcomes/Capability Development

This course contributes to Program Learning Outcomes for

• BH077 - Bachelor of Engineering (Civil and Infrastructure) (Honours)
• BH079 - Bachelor of Engineering (Chemical Engineering) (Honours)
• BH085 - Bachelor of Engineering (Chemical Engineering) (Honours) / Bachelor of Business (Management)
• BH088 - Bachelor of Engineering (Civil and Infrastructure) (Honours) / Bachelor of Business (Management)
• BH098 - Bachelor of Science (Applied Chemistry) / Bachelor of Engineering (Chemical Engineering) (Honours)
• BH099 Bachelor of Science (Food Technology & Nutrition) / Bachelor of Engineering (Chemical Engineering) (Honours)
• BH087 - Bachelor of Engineering (Chemical Engineering) / (Honours)/Bachelor of Science (Biotechnology)

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. Solve large systems of simultaneous linear equations. Find eigenvalues and eigenvectors of a matrix. Use the least-squares method to obtain a function for data analysis.
2. Find solutions of non-linear equations using bisection method, Newton’s methods and secant method and implement using a computer.
3. Estimate the solutions of systems of first order ordinary differential equations or higher order ordinary differential equations using various numerical methods and implement using a computer.
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 recorded lectures, interactive Q&A sessions, and problem-based practicals. An online course site will be used to disseminate course materials, to provide access to online On-line quizzes and online On-line tests for self-assessment and to submit the problem-based computer laboratory assignments.

Key concepts and their applications will be explained and illustrated with many examples. The problem-based weekly practical sessions/computer labs will build your capacity to solve problems and to think analytically and critically. The mathematical and statistical theories and applications will also be reinforced through assessment. The computer laboratory sessions are designed to assist students in applying statistical methods using software packages, such as Microsoft Excel or Minitab for performing the basic statistical analysis of data collected from a broad range of sciences and engineering fields. Assignments will enhance your understanding of the statistical concepts and help you to develop your ability to apply your knowledge to analyse data.

Overview of Learning Resources

The course Canvas site links to the course notebook where you will find:

1. Teaching schedule and suggested reading (including prescribed textbook)
2. Assessment guide and assessment schedule.
3. Lecture slides.
4. Computer lab guides for using Excel in statistics
5. Assignment papers
6. Recommended references.
7. Tables and formula sheets.

You will have access to the software packages Microsoft Excel and Minitab through myDesktop.

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

Overview of Assessment

Early Assessment Task: (Weeks 2-5), Interim discipline-based on-line assessments

Weighting: 14%

This assessment task supports CLOs 1 & 2

Assessment Task 2: Interim discipline-based on-line assessments

Weighting: 32%

This assessment task supports CLOs 2, 3, 4, 5 & 6