Course Title: Mathematics and Statistics

Part A: Course Overview

Course Title: Mathematics and Statistics

Credit Points: 12.00


Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2123

City Campus

Undergraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

Sem 1 2006,
Sem 2 2006,
Sem 1 2007,
Sem 2 2007,
Sem 1 2008,
Sem 2 2008,
Sem 1 2009,
Sem 2 2009,
Sem 1 2010,
Sem 2 2010,
Sem 1 2011,
Sem 2 2011,
Sem 1 2012,
Sem 2 2012,
Sem 1 2013,
Sem 2 2013,
Sem 1 2014,
Sem 2 2014,
Sem 1 2015,
Sem 2 2015,
Sem 1 2016,
Sem 2 2016

MATH2123

City Campus

Undergraduate

171H School of Science

Face-to-Face

Sem 1 2017,
Sem 2 2017

Course Coordinator: Assoc Professor Sergei Schreider

Course Coordinator Phone: +61 3 9925 3223

Course Coordinator Email: sergei.schreider@rmit.edu.au

Course Coordinator Location: 8.9.33


Pre-requisite Courses and Assumed Knowledge and Capabilities

VCE Further Mathematics (or equivalent)


Course Description

MATH 2123 - Mathematics and Statistics for Environmental Science will introduce you to a number of mathematical and statistical procedures often used in environmental science. The course aims to provide the theoretical foundations that any environmental scientist will require to meet community and regulatory requirements. The learning of statistics will integrate theory and applications using a problem-based approach aided by the use of the Minitab statistical package. The course will focus on developing your abilities in critical analysis and decision making as well as teamwork and reflection.


Objectives/Learning Outcomes/Capability Development

Knowledge Capability
1.1 Awareness of related statistical methods to formulate and solve scientific problems

Technical Capability
2.1 Formulate a real world problem into a mathematical or a statistical problem
2.2 Awareness and ability of using appropriate statistics computer software packages.

Critical Analysis and Problem Solving
3.1 Solve a related statistical problem and interpret the solution
3.2 Collection, analysis and interpretation of data.
3.3 Treat data in appropriate ways.

Communication
4.1 Ability to communicate in writing and orally
4.2 Understanding working in partnership with others of different disciplinary orientations and professional aspirations, introducing cross-disciplinary work.


On successful completion of this course you will be able to

  1. perform appropriate graphical displays of data and understand the role of such displays in data analysis;
  2. calculate probabilities of binomial and normal distributions;
  3. construct point and interval estimation for mean and proportion;
  4. conduct hypothesis tests for mean and proportion, and compare two-sample means and proportions;
  5. carry out linear regression analyses;
  6. use statistical package to carry out descriptive and simple inferential statistical analyses.


Overview of Learning Activities

You will be expected to attend lectures, computer laboratories and tutorial sessions. The lecture, laboratory and tutorial classes will focus on problem-based activities encountered in environmental science settings. These environmental relevant problems will be used to assist you to construct appropriate graphical displays of data, understand the nature of random variables through direct calculation and computer simulation, perform quality assessment tasks using software packages and understand the calculations involved in such tasks and to be aware of assumptions that are necessary for the validation of results.

The instructor will use demonstrations to provide you with an insight into the mental processes one goes through when mapping real life problems to abstract mathematical and statistical procedures and then to pen-and-paper calculations or the use of Minitab. The instructor will demonstrate the problem identification-solution process by “thinking aloud”. From this, you will be able to compare your own problem solving approaches to these problems. The instructor will use a mixture of such strategies as well as other means to highlight the main solution techniques and to show how the solution relates to the original model or data.

Each class will provide opportunities for you to receive feedback while attempting to determine solutions using pen-and-paper and suitable statistical packages. This is critical, as these skills will impact on the other mathematical knowledge and skills developed throughout the program. This could be achieved during lecture, practical sessions in a laboratory style problem solving class and tutorial classes.

You will spend much of the time working on problems as well as  class discussions highlighting the similarities and differences involved in various solution strategies. You would be encouraged to find support from peers and the instructor when appropriate. Students experiencing difficulty in understanding lecture material may seek free help from course coordinator, tutors at the tutorial sessions or the Study and Learning Centre.


Overview of Learning Resources

You will be able to access course information, learning materials and online Weblearn tests through the Learning Hub. You will also use computer laboratory equipment and computer software at university computer pools, or through RMIT mydesktop.


Overview of Assessment

Note:

This course has no hurdle requirements

 

Assessment tasks

 

Assessment Task 1: Practical Tests

Weighting 25%

This assessment task supports CLOs 1, 2, 3 ,4 & 5

Assessment Task 2: Lab Tests

Weighting 25%

This assessment task supports CLOs 6

Assessment 3: Examination

Weighting 50% 

This assessment supports CLOs 1, 2, 3, 4&5.