Course Title: Analysis of Categorical Data

Part A: Course Overview

Course Title: Analysis of Categorical Data

Credit Points: 12.00


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

Course Coordinator: Dr Vural Aksakalli

Course Coordinator Phone: +61 3 9925 2277

Course Coordinator Email: vural.aksakalli@rmit.edu.au

Course Coordinator Location: 8.9.84


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.