# Course Title: Probability and Statistics

## Part A: Course Overview

Course Title: Probability and Statistics

Credit Points: 12.00

### Teaching Period(s)

MATH2199

City Campus

145H Mathematical & Geospatial Sciences

Face-to-Face

Sem 1 2010,
Sem 1 2011,
Sem 1 2012,
Sem 1 2013,
Sem 1 2014,
Sem 1 2015

Course Coordinator: A/Prof. Sergei Schreider

Course Coordinator Phone: +61 3 9925 3223

Course Coordinator Email: Sergei.Schreider@rmit.edu.au

Course Coordinator Location: 8.9.33

Pre-requisite Courses and Assumed Knowledge and Capabilities

MATH2200 Statistics 1 and MATH2201 Statistics 2 or their equivalent.

Course Description

This course provides a solid undergraduate foundation in both probability theory and mathematical statistics and at the same time provides an indication of the relevance and importance of the theory in solving practical problems in the real world. Extensive use will be made of the industry-standard SAS statistical software package. Topics include: probability distributions, exploratory data analysis via various descriptive statistics, inferential statistical methods such as the various forms of the t-test, use of confidence intervals, sample size and power calculations, effect size estimation.

Objectives/Learning Outcomes/Capability Development

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

• Apply probability theory to set up tree diagrams
• Apply probability theory via Bayes’ Rule
• Describe the properties of discrete and continuous distribution functions
• Use method of moments and moment generating functions
• Assess the consistency, efficiency and unbiasedness of estimators
• Apply method of maximum likelihood estimation
• Apply the Central Limit Theorem
• Use statistical tests in testing hypotheses on data

This course contributes to the development of the following Program Learning Outcomes:

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 / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.

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 or an appropriate website dedicated to the course. Additional learning materials will be provided in lectures via appropriate handouts. Lists of relevant texts, resources in the library and freely accessible Internet sites will be provided. You will also use computer software within the School’s computer laboratories.

Overview of Assessment

The assessment for this course includes assignments, lab-based assessment and a final exam. Feedback on labs and tests will be provided to you during the semester.