Course Title: Statistics

Part A: Course Overview

Course Title: Statistics

Credit Points: 12.00


Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH1277

Bundoora Campus

Undergraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

Sem 2 2006,
Sem 2 2007,
Sem 2 2008,
Sem 2 2009,
Sem 2 2010,
Sem 2 2011,
Sem 2 2012,
Sem 2 2013,
Sem 2 2014,
Sem 2 2015,
Sem 2 2016

MATH1277

Bundoora Campus

Undergraduate

171H School of Science

Face-to-Face

Sem 2 2017

MATH1278

City Campus

Undergraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

Sem 2 2006,
Sem 2 2007,
Sem 2 2008,
Sem 2 2009,
Sem 2 2010,
Sem 2 2011,
Sem 2 2012,
Sem 2 2013,
Sem 2 2014,
Sem 2 2015,
Sem 2 2016

MATH1278

City Campus

Undergraduate

171H School of Science

Face-to-Face

Sem 2 2017

Course Coordinator: Dr Sergei Schreider (city) Dr Stella Stylianou (Bundoora)

Course Coordinator Phone: +61 3 9925 3223 / +61 3 9925 6227

Course Coordinator Email: sergei.schreider@rmit.edu.au /stella.stylianou@rmit.edu.au


Pre-requisite Courses and Assumed Knowledge and Capabilities

MATH1275/MATH1276 or an equivalent basic introductory Statistics course


Course Description

This course extends the probability and statistics material covered in MATH1275/1276 with more advanced statistical techniques for Psychology students. In the laboratory sessions extensive use will be made of appropriate statistical packages such as MINITAB or SPSS for data analysis.
Topics areas include:
Sampling distributions, central limit theorem, hypothesis testing and confidence interval calculations. One sample t test. Two sample hypothesis test (z and t-tests). Confidence intervals for difference between population means. Inference for proportions and difference between proportions. One way analysis of variance. Linear regression (simple and multiple), inference for regression and assumptions underlying statistical tests. Outliers in data and their influence in descriptive and inferential statistical methods. Effect size estimation and statistical power


Objectives/Learning Outcomes/Capability Development

On successful completion of this course, you will be able to:

1. Apply  the SPSS statistical software package to perform statistical tests
2. Interpret SPSS output on statistical tests
3. Write APA style reports on the outcomes of these statistical tests.
4. Relate Type1, Type 2 errors, effect size and  sample size to the power of statistical tests.


You will gain or improve your 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 and topics 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 provide 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

The assessment for this course comprises the following:
Early Assessment Task
Assignment 1 (Supports CLOs 1-3)
Weighting = 5%

Assessment Task 2
SPSS Data Analysis (Supports CLOs 1-4)
Weighting = 20%

Assessment Task 3
SPSS Data Analysis (Supports CLOs 1-4)
Weighting = 15%

Assessment Task 4
Questions from Lecturers and Practicals (Supports CLOs 1-4)
Weighting = 10%

Final Exam
(Supports CLOs 1-4)
Weighting = 50%