Skip to Content

Future Students - Courses

click here for a printable version of this page  

Using R for Statistical Analysis

Unit Code: HMS796




Duration

Contact Hours

Campus

Prerequisite

Corequisite

One semester / teaching period

36

Hawthorn, Online

HMS780 Multivariate Statistics

Credit Points: 12.5 Credit Points

Related Course/s:

Back to Top

Aims & Objectives:

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.

 
Learning Objectives
After successfully completing this unit, you should be able to:
* Understand R data types and the R language.
* Use R software to display, describe and summarize data.
* Use R to control finer details of graphical representations.
* Develop knowledge of simulation functions and understand their purpose.
* Calculate confidence intervals and perform classical hypothesis tests.
* Understand R modeling syntax and basic R programming.
* Fit a variety of different linear models.
* Understand the differences between Bayesian estimation and maximum likelihood estimation.
* Apply Bayesian methodology using Markov Chain Monte Carlo simulation.

Back to Top

Teaching Methods:

Twelve lectures (3 hours) and independent study

Back to Top

Assessment:

Online quizzes (10%) 
Two Assignments (40%)

Examination (50%)

Back to Top

Generic Skills Outcomes:

This unit is designed to produce graduates who:
 
• Possess the following generic skills; 
  *  Data analysis skills
  *  Problem solving skills
  *  Able to tackle unfamiliar problems
  *  Able to work independently
 
• Are capable in their chosen professional, vocational or study areas;
  *  Students will be proficient in the statistical software package R.
  *  Students will be well rounded competent graduates with adequate quantitative skills in areas such as exploratory data
      analysis, estimation and statistical inference, simulation, parametric modelling.
  *  Students will have good communication and presentation skills developed using assignment work.
 
• Are adaptable and manage change
  *  Students will have a flexible approach to problem solving with an ability to listen and understand the advice and opinions of domain experts. These skills are honed through the use of real life problems and real data related to science and technology in lectures and assignments.

Back to Top

Content:

Describing and summarizing data                
Probability distributions and simulation                
R data types and syntax                
Classical hypothesis testing and confidence intervals                
R modeling syntax and object oriented programming                
The linear modeling framework: extensions of multiple regression                
Maximum likelihood estimation                
Bayesian estimation using Markov Chain Monte Carlo simulation

Back to Top

Textbooks:

Verzani, J. Using R for Introductory Statistics. Chapman & Hall.

Back to Top

References:

Zuur, A.F., Ieno, E. N. & Meesters E.H.W.G. (2009) A Beginner’s Guide to R. Springer.

Dalgaard, P. (1988)  Introductory Statistics with R. Springer.

The Comprehensive R Archive Network (CRAN) http://cran.r-project.org/

The R Project for Statistical Computing  http://www.r-project.org/

Back to Top