Part A: Course Overview

Course Title: Analysis of Categorical Data

Credit Points: 12.00

Important Information:

Please note that this course may have compulsory in-person attendance requirements for some teaching activities. 

To participate in any RMIT course in-person activities or assessment, you will need to comply with RMIT vaccination requirements which are applicable during the duration of the course. This RMIT requirement includes being vaccinated against COVID-19 or holding a valid medical exemption. 

Please read this RMIT Enrolment Procedure as it has important information regarding COVID vaccination and your study at RMIT:  https://policies.rmit.edu.au/document/view.php?id=209. 

Please read the Student website for additional requirements of in-person attendance:  https://www.rmit.edu.au/covid/coming-to-campus 

Please check your Canvas course shell closer to when the course starts to see if this course requires mandatory in-person attendance. The delivery method of the course might have to change quickly in response to changes in the local state/national directive regarding in-person course attendance. 


Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH1298

City Campus

Postgraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

 Sem 2 2012,
Sem 2 2014,
Sem 2 2016

MATH1298

City Campus

Postgraduate

171H School of Science

Face-to-Face

 Sem 2 2018,
Sem 2 2020

Course Coordinator: Dr. David Akman

Course Coordinator Phone: NA

Course Coordinator Email: david.akman@rmit.edu.au


Pre-requisite Courses and Assumed Knowledge and Capabilities

MATH1324: Introduction to Statistics

MATH2267: Essential Mathematics for Analytics


Course Description

This course focuses on analysing categorical response data in scientific fields. It provides you with an overview of methods used in analysing categorical data also known as data on the nominal scale. Categorical distributions and their properties and applications will be covered. This is followed by binary, nominal and ordinal logistic regression and Poisson regression. Visualization of categorical data using a computer package is part of the course.


Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes for MC004 Master of Statistics and Operations Research and MC242 Master of Analytics:

Personal and professional awareness

  • The ability to contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions
  • The ability to reflect on experience and improve your own future practice
  • The ability to apply the principles of lifelong learning to any new challenges.

Knowledge and technical competence

  • The ability to use the appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.

Problem-solving

  • The ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
  • An understanding of the balance between the complexity and accuracy  of the mathematical and  statistical models used, and the timeliness of the delivery of the solution.

Information literacy

  • The ability to locate and use data and information and evaluate its quality with respect to its authority and relevance.



On successful completion of the course you should be able to:

  1. Model categorical data;
  2. Visualize and interpret categorical data;
  3. Extract and structure categorical data;
  4. Perform statistical inferences on categorical data;
  5. Analyse categorical data using statistical software


Overview of Learning Activities

Key concepts of categorical data analysis will be extensively covered in this course. These will be explained and elucidated in class and lab. We will use a computer package to visualize categorical data. The assignments and labs will test your understanding of class materials. Assignments will provide you with an opportunity to practise your problem solving skills, test your understanding and exchange ideas with your peers. You will also have the opportunity to discuss your progress with teaching staff.


Overview of Learning Resources

You will have access to computer laboratories utilising SAS and R software available in the School. This course is taught through a mixture of classroom instruction, computer laboratory exercises and assignments.

You will have access to extensive course materials made available via the online RMIT Learning Hub (myRMIT), including digitised readings, lecture notes and a detailed study program, external internet links and access to RMIT Library online and hardcopy resources

Library Subject Guide for Mathematics & Statistics http://rmit.libguides.com/mathstats


Overview of Assessment

Assessment Tasks

Assessment Task 1: Assignments
Weighting: 20%
This assessment task supports CLOs 1, 2, 3, 4, and 5. 

Assessment Task 2: Class test
Weighting: 30%
This assessment task supports CLOs 1, 2, 3, 4, and 5.

Assessment Task 3: Final exam
Weighting: 50%
This assessment task supports CLOs 1, 2, 3, 4, and 5.