Studying online

There are now 2 possible online modes for units:

Units with modes Online timetabled and Online flexible are available for any student to self-enrol and study online.

Click on an offering mode for more details.

Unit Overview

Description

The explosion in power of computers over recent times is changing the face of statistical science. Once upon a time, intuitively attractive statistical procedures had to be consigned to the waste bin if they led to unfathomable mathematical complications, or required masses of intricate calculations for their practical implementation. Now such difficulties can often be circumvented by using computer simulation and number crunching. This has led to the development and widespread use of many new statistical tools including the bootstrap, Markov Chain Monte Carlo methods and non-parametric kernel smoothing methods. Computer intensive statistical methods are not only in general use by statisticians, but are also applied by quantitative researchers in the life sciences, medicine and biological science, social sciences and business.

This unit gives a broad coverage of computer intensive methods with numerous applied examples, together with the underlying general concepts and basic theory. Particular emphasis is placed on the use of these methods in real statistical applications. Data sets are analysed using the statistical package R. Topics are selected from the following: simulation and Monte Carlo, bootstrap methods, Markov Chain Monte Carlo (MCMC) methods and Bayesian inference, non-parametric kernel smoothing methods, and statistical/machine learning.

Credit
6 points
Offering
(see Timetable)
AvailabilityLocationMode
Semester 2UWA (Perth)Face to face
Details for undergraduate courses
  • Level 4 elective
  • Honours option in Mathematics and Statistics [Bachelor of Science (Honours)]
Outcomes

Students are able to (1) appropriately apply computationally intensive statistical techniques in simulation studies and to real-world problems; (2) extend their knowledge of computational techniques in general, but statistical computing techniques in particular, and adapt known solutions to different situations; and (3) present results in a logical and coherent fashion and communicate effectively with others.

Assessment

Indicative assessments in this unit are as follows: (1) assignments and (2) a final examination. Further information is available in the unit outline.



Student may be offered supplementary assessment in this unit if they meet the eligibility criteria.

Unit Coordinator(s)
Dr Michael Bertolacci
Unit rules
Prerequisites
STAT3062 Statistical Science
or STAT4064 Applied Predictive Modelling
Advisable prior study
STAT2402 Analysis of Observations (ID 389) or STAT2062 Fundamentals of Probability with Applications (ID 5019)
Contact hours
3-hours per week
  • The availability of units in Semester 1, 2, etc. was correct at the time of publication but may be subject to change.
  • All students are responsible for identifying when they need assistance to improve their academic learning, research, English language and numeracy skills; seeking out the services and resources available to help them; and applying what they learn. Students are encouraged to register for free online support through GETSmart; to help themselves to the extensive range of resources on UWA's STUDYSmarter website; and to participate in WRITESmart and (ma+hs)Smart drop-ins and workshops.
  • Visit the Essential Textbooks website to see if any textbooks are required for this Unit. The website is updated regularly so content may change. Students are recommended to purchase Essential Textbooks, but a limited number of copies of all Essential Textbooks are held in the Library in print, and as an ebook where possible. Recommended readings for the unit can be accessed in Unit Readings directly through the Learning Management System (LMS).
  • Contact hours provide an indication of the type and extent of in-class activities this unit may contain. The total amount of student work (including contact hours, assessment time, and self-study) will approximate 150 hours per 6 credit points.