Course Title: Analysis of Categorical Data
Part A: Course Overview
Course Title: Analysis of Categorical Data
Credit Points: 12.00
Terms
Course Code |
Campus |
Career |
School |
Learning Mode |
Teaching Period(s) |
MATH2300 |
City Campus |
Undergraduate |
145H Mathematical & Geospatial Sciences |
Face-to-Face |
Sem 2 2016 |
MATH2300 |
City Campus |
Undergraduate |
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
Pre-requisites -
MATH2200: Introduction to Probability and Statistics
MATH2201: Basic Statistical Methodologies
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 BH119 Bachelor of Analytics (Honours):
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:
- Model categorical data;
- Visualize and interpret categorical data;
- Extract and structure categorical data;
- Perform statistical inferences on categorical data;
- 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. A computer package will be used to visualize categorical data. The assignments and labs will test your understanding of class materials. Assignments will provide you with opportunities to practice your problem solving skills, test your understanding, and exchange ideas with others. 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.