Using R for Statistical Analysis
HMS796
Overview
To enrol in this unit, you must be accepted into a course from the provider.
Read before you start
Level of study: What does Postgraduate mean?
Postgraduate
EFTSL: What does EFTSL mean?
0.125
Delivery Method: What does delivery method mean?
Web Dependent
Prerequisites: What are the prerequisites?
Duration:
13 weeks
Government loans available:
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Domestic student fee:
$1,950.00 (AUD)
International student fee:
$2,175.00 (AUD)
Description
The aims of this unit are to provide knowledge and skills sufficient to allow students to understand basic syntax and programming in the statistical language R, and to learn how to apply methods of data analysis using R software. Methods include advanced graphical representations, simulation and probability models, classical hypothesis testing, R modelling syntax, maximum likelihood estimation and Bayesian analysis using Markov Chain Monte Carlo simulation methods.
Enrolment Restriction
In order to enrol in this unit, you must be accepted into one of the following courses:
If you wish to seek approval to enrol in this unit without being accepted in a course, please contact OUA regarding the process.
Prerequisites
Recommended prerequisites
You are recommended to have completed the following unit(s) or have equivalent knowledge before starting this unit:
- HMS770 — Statistical Practice 1
- HMS771 — Statistical Practice 2
- HMS780 — Multivariate Statistics
Special Requirements
- Additional materials
- Other special requirement — You will require the R software package which is available freely on the web.
Assessment
- Assignment 1 (20%)
- Assignment 2 (20%)
- Invigilated Exam (50%)
- Quizzes (10%)
Learning Outcomes
At the completion of this unit students will be able to:
- understand R data types and the R language
- use R software to display, describe and summarise data
- use R to control the finer details of graphical representations
- develop knowledge of simulation functions and understand their purpose
- calculate confidence intervals and perform classical hypothesis tests
- fit a variety of linear models
- understand the difference between Bayesian estimation and maximum likelihood estimation
- apply Bayesian methodology using Markov Chain Monte Carlo simulation.
Topics
This unit addresses the following topics.
| Number | Topic |
|---|---|
| 1 | R data types and syntax |
| 2 | Describing and summarising data with R |
| 3 | Probability distributions and simulation |
| 4 | Classical hypothesis testing and confidence intervals |
| 5 | The linear modelling framework: extensions of multiple regression |
| 6 | Maximum likelihood estimation |
| 7 | Bayesian estimation using Markov Chain Monte Carlo simulation |
Study Resources
This unit is delivered using the following methods and materials:
Instructional Methods
- Discussion Forum/Discussion Board
- Online Quizzes/Tests
- Online assignment submission
- Podcasting/Lecture capture
- Standard Media
- Web links
Textbooks are subject to change within the academic year. Students are advised to purchase their books no earlier than one to two months before the start of a unit.
Click on the titles of the listed books below to find out more:
Required textbooks
Recommended textbooks
Relevant Courses
This unit is a core requirement in the following courses:
This unit may be eligible for credit towards other courses:
- Many undergraduate courses on offer through OUA include 'open elective' where any OUA unit can be credited to the course. You need to check the Award Requirements on the course page for the number of allowed open electives and any level limitations.
- In other cases, the content of this unit might be relevant to a course on offer through OUA or elsewhere. In order to receive credit for this unit in the course you will need to supply the provider institution with a copy of the Unit Profile in the approved format, which you can download here. Note that the Unit Profile is set at the start of the year, and if textbooks change this may not match the Unibooks textbook list.
