Course Title: Statistical Computing
Part A: Course Overview
Course Title: Statistical Computing
Credit Points: 12.00
Important Information:
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) |
MATH1275 |
Bundoora Campus |
Undergraduate |
145H Mathematical & Geospatial Sciences |
Face-to-Face |
Sem 1 2006, Sem 1 2007, Sem 1 2008, Sem 1 2009, Sem 1 2010, Sem 1 2011, Sem 1 2012, Sem 1 2013, Sem 1 2014, Sem 1 2015, Sem 1 2016 |
MATH1275 |
Bundoora Campus |
Undergraduate |
171H School of Science |
Face-to-Face |
Sem 1 2018, Sem 1 2019, Sem 1 2020, Sem 1 2021 |
MATH1276 |
City Campus |
Undergraduate |
145H Mathematical & Geospatial Sciences |
Face-to-Face |
Sem 1 2006, Sem 1 2007, Sem 1 2008, Sem 1 2009, Sem 1 2010, Sem 1 2011, Sem 1 2012, Sem 1 2013, Sem 1 2014, Sem 1 2015, Sem 1 2016 |
MATH1276 |
City Campus |
Undergraduate |
171H School of Science |
Face-to-Face |
Sem 1 2017, Sem 1 2018, Sem 1 2019, Sem 1 2020, Sem 1 2021, Sem 1 2022 |
Course Coordinator: Sevvandi Kandanaarachchi
Course Coordinator Phone: +61 3 9925 2880
Course Coordinator Email: sevvandi.kandanaarachchi@rmit.edu.au
Course Coordinator Location: 15.4.13
Course Coordinator Availability: Contact via email for appointment
Pre-requisite Courses and Assumed Knowledge and Capabilities
A pass in any VCE Year 12 mathematics course is desirable
Course Description
This course provides an introduction to the role of Statistics in the Data Analysis Process. The emphasis is on learning statistical methods for processing data to yield information that leads to informed decision-making in work and research contexts. The statistical package SPSS is used in the analysis of data.
Topics include the following: Types of variables; Sampling: probability and non-probability samples; Graphical methods for describing data; Numerical methods for describing data; Summarising bivariate data; Probability and Probability distributions; Normal distribution and its properties and hypothesis testing with the normal distribution.
Objectives/Learning Outcomes/Capability Development
On completion of the course, you will be able to:
1. Apply a range of sampling techniques and be conversant with their strengths and weaknesses in data collection.
2. Discuss the role of statistical methods in analysing data.
3. Use SPSS to set up data files and use menu commands to analyse data.
4. Construct appropriate graphical displays of data using SPSS.
5. Numerically summarise data via SPSS using descriptive statistics.
6. Describe the properties of the normal distribution and perform hypotheses testing.
You will gain or improve capabilities in:
1.0 Critical Analysis and Problem Solving
1.1 Ability to apply scientific principles and methods to describe and analyse research data
1.2 Ability to know what questions to ask, who to ask and how to ask them.
2.0 Teamwork & Leadership
2.1 Ability to work in collaboration with others on data analysis tasks
3.0 Communication and Presentation
3.1 Ability to communicate in a range of forms (written, electronic, graphic, oral) and to tailor the style and means of communication to the circumstances of the situation and capabilities and sensitivities of the psychological disciplines.
3.2 Ability to constructively give and receive feedback
4.0 Self management
4.1 Ability to take personal responsibility for decisions and actions while being aware of limits of knowledge and skill and when to seek help.
Overview of Learning Activities
The learning activities included in this course are:
- Lectorials where syllabus material will be discussed and explained. Topics will be illustrated with demonstrations via online tools, statistical packages, simulations and worked examples;
- Completion of tutorial/practice questions and data analysis computer laboratory sessions which are designed to give further practice in the application of theory and procedures and to give feedback on your progress and understanding;
- In-lecturorial review questions on topics completed so as to enable you to gauge progress in your learning;
guided private study through the provision of lecture summaries that indicate follow-up reading and practice problems to attempt on the material taught. - Working with team members on a specific project broadening your knowledge of statistical analysis in the real world.
Overview of Learning Resources
You will be able to access course information and learning materials through Canvas. Additional learning materials will be provided in lectures via appropriate handouts. You will also use computer software within the School’s computer laboratories.
A library guide is available at http://rmit.libguides.com/mathstats
Overview of Assessment
This course has no hurdle requirements.
Assessment Tasks
Assessment Task 1: Introductory Practical Assessments
Weighting 30%
This assessment supports CLOs 1-6
Assessment Task 2: Intermediate Practical Assessments
Weighting = 30%
This assessment supports CLOs 1-6
Assessment Task 3: Collaborative Learning Component
Weighting = 40%
This assessment supports CLOs 1 -6