Course Title: Statistical Analysis 1

Part A: Course Overview

Course Title: Statistical Analysis 1

Credit Points: 12.00


Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH1279

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

MATH1279

Bundoora Campus

Undergraduate

171H School of Science

Face-to-Face

Sem 1 2017

MATH1280

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

MATH1280

City Campus

Undergraduate

171H School of Science

Face-to-Face

Sem 1 2017

Course Coordinator: Associate Professor Cliff Da Costa

Course Coordinator Phone: +61 3 9925 6114

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

Course Coordinator Location: 201.03.24

Course Coordinator Availability: Email me to make an appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

Students doing a psychology major are required to have done MATH1275 and MATH1277 (or MATH1276 and MATH1278 ) or their equivalents prior to enrolling in this course. To successfully complete this course, you should have knowledge of basic probability concepts, descriptive statistical analysis and inferential statistical analysis involving the use of the t-test and ANOVA. You should also be familiar with simple and multiple linear regression at the level taught in MATH1277/MATH1278 . The ability to use a statistical package such as MINITAB and/or SPSS to perform data entry, basic descriptive and inferential statistical analysis is a requirement.


Course Description

The purpose of this course is to introduce you to the application of statistical methods in research. The intent is to enable you to read and understand current research literature in your respective fields of specialisation especially with regard to the use of statistical methods. Among the statistical methods that you will be exposed to are the following: t-tests, one-way ANOVA  and their non parametric  equivalents, two-way ANOVA, MANOVA, reepeated measures ANOVA.. The testing of assumptions underlying the use of statistical tests will be emphasised. Extensive use will be made of the SPSS statistical packages to illustrate applications.


Objectives/Learning Outcomes/Capability Development

On completion of the course, you should be able to:

  1. Perform exploratory analysis of data collected within your field of specialisation.
  2. Determine effect size estimates on existing research and estimating sample sizes for new research.
  3. Apply advanced inferential statistical methods via hypothesis testing and confidence interval estimation.
  4. Test for statistical assumptions underlying the use of advanced inferential statistical methods on the data.
  5. Proficiently use statistical packages such as SPSS in analysing data using advanced statistical methods.
  6. Critically examine the research literature with regard to the use of appropriate statistical methods.
  7. Plan a research study in your own field of specialisation by thinking ahead of the kinds of data analyses you intend doing in order to answer specific research questions.



Overview of Learning Activities

The learning activities included in this course are:

• attendance at lectures where syllabus material will be presented and explained, and the 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;


Overview of Learning Resources

You will be able to access course information and learning materials through the Learning Hub (also known as online@RMIT) or an appropriate website dedicated to the course. Additional learning materials will be provided in lectures via appropriate handouts. You will also use computer software within the School’s computer laboratories.


Overview of Assessment

Assessment will be composed of the following:

  1. Data Analysis Project 1 weighted 40% of final mark
  2. Data Analysis Project 2 weighted 40% of final mark
  3. Completion and submission of Demo of Learning Exercises weighted 20% of final mark

In order to be eligible to achieve a pass (or higher grade) in the course, you must fulfill the following criterion:

A mark of 50% or higher is required on EACH of the two project assessment tasks.

Should you fail to achieve 50% and ONLY if your score is between 40% and 50%, in any one of the two project assignments, you will be given the opportunity to re-submit.  However, the maximum mark you can achieve on a re-submission is 50%