Course Title: Statistical Computing

Part A: Course Overview

Course Title: Statistical Computing

Credit Points: 12.00


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 2017

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

Course Coordinator: Assoc Prof Cliff Da Costa

Course Coordinator Phone: +61 3 9925 6114

Course Coordinator Email: cliff.dacosta@rmit.edu.au

Course Coordinator Location: 201.03.24 (Bundoora West Campus)

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:

• attendance at lectures where syllabus material will be presented and explained. Topics will be illustrated with demonstrations via java applets, 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-lecture 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.
 

Lecture: 2 hours/week, Labs: 1 hour/week; Independent  study: 5 hours/week
Total: 96 hours per semester.


Overview of Learning Resources

You will be able to access course information and learning materials through Blackboard. 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:Practical and Review Exercises

Weighting = 25%

This assessment supports CLOs 1 - 6

 

Assessment Task 2: Mid-Semester Multiple-Choice Test

Weighting = 25%

This assessment supports CLOs 1 - 6

 

Assessment Task 3: Final Exam

Weighting = 50%

This assessment supports CLOs 1 - 6

 

To obtain a PASS in the course, you must attempt all assessments and obtain an overall weighted average score of at least 50%.