Course Title: Quantitative Data Analysis and Decision Making

Part A: Course Overview

Course Title: Quantitative Data Analysis and Decision Making

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


625H Economics, Finance and Marketing


Sem 1 2006,
Sem 2 2006,
Sem 1 2007,
Sem 2 2007


City Campus


620H Business IT and Logistics


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


City Campus


630H Management


Sem 1 2008,
Sem 2 2008,
Sem 1 2009,
Sem 2 2009

Course Coordinator: Dr Ian Storey

Course Coordinator Phone: +61 3 9925 5954

Course Coordinator Email:

Course Coordinator Location: 80.09.35

Pre-requisite Courses and Assumed Knowledge and Capabilities


Course Description

This course is designed to develop your ability to quantitatively analyze data patterns, statistical trends, and quantitative business indicators, with the emphasis on helping you to be able to interpret and understand quantitative results for more informed decision-making. Its aim is to introduce you to a broad range of statistical concepts and associated quantitative techniques and research methods with a view to helping you appreciate the merits and limitations of these techniques as well as the data and technical requirements involved with their use. It is not the course’s primary intention to teach you to remember the various concepts, techniques and formulas that it covers, but rather to let you gain practice in their use and in deciding for what type(s) of logistics and supply chain management problems/ issues/ opportunities they are likely to be the most applicable and informative from a decision making perspective.

Objectives/Learning Outcomes/Capability Development


On successful completion of this course you will be able to:

  1. Demonstrate the basic quantitative concepts underlying data analysis and quantitative decision-making
  2. Distinguish between the various elements, observations, values and variables as applicable to quantitative analysis and decision-making
  3. Apply the underlying concepts of Inferential Statistical Analyses and hypotheses testing, analysis of variance, time series forecasting and regression analysis in the manufacturing and service sectors.
  4. Apply the tools and underlying principles of data analysis in logistics and supply chain profession.
  5. Investigate various methods to make more informed and data-driven business decisions.

Overview of Learning Activities

To assist you in achieving the above learning outcomes, you are provided with weekly two hours lectures and one tutorial sessions for the twelve weeks of the semester. Each lecture addresses one of the learning outcomes and is supported with extensive lecture slides that you can down load from the OMGT2186 Blackboard that is accessible from myRMIT. You will have the chance to work through assigned tutorial activities and will be guided in this by your tutor. You are advised to actively participate actively and constructively in both the lectures and the tutorials to deepen your learning.
You will also learn through both personal reading and note-taking from the prescribed text and other relevant reference material. Another important learning activity is the work you do and the insights you gain from working on a group-performed and submitted assignment.

Overview of Learning Resources

RMIT University will provide you with resources and tools for learning in this course through our online systems and computer laboratories.
You have access to extensive course materials on myRMIT Studies, including digitised readings, lecture notes and a detailed study program, external internet links and access to RMIT Library online and hardcopy resources. If you require assistance with the RMIT library facilities contact the Business Liaison Librarian for your school. Contact details for Business Liaison Librarians are located online on the RMIT Library website.

Overview of Assessment

The assessment tasks, their weighting and the course learning outcomes to which they are aligned are as follows:

Assessment Task 1: 10%
Linked CLOs: 1, 2, 3, 4, 5

Assessment Task 2: 40%
Linked CLOs: 1, 2, 3, 4, 5

Assessment Task 3: 50%
Linked CLOs: 1, 2, 3, 4, 5

Feedback will be provided throughout the semester in class and/or in online forums through individual and group feedback on practical exercises and by individual consultation.