Note: This is an archived Handbook entry from 2016.
|Dates & Locations:|| |
This subject is not offered in 2016.
|Time Commitment:||Contact Hours: Two week intensive teaching period: Mon, Wed, Fri each week. 36 hours, comprising 4 hours of lectures, 2 hours of computer laboratory each day. This subject includes a pre-teaching period, beginning 1 month before commencement of classes. Students will be expected to review preliminary course material and be working on the first assignment during this period. |
Total Time Commitment:
Estimated Total Time Commitment: 144 hours, including self-directed study and research
|Recommended Background Knowledge:|| |
Previous exposure to statistics through an introductory statistics subject or familiarity with elementary statistics.
|Non Allowed Subjects:||None|
|Core Participation Requirements:||
For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.
It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability
To make reliable, defensible decisions in complex and uncertain settings, we need information, an honest appraisal of its quality, and a decision framework within which the information can be used. In many settings, obtaining high-quality information is best underpinned by systematic data collection and analysis. Data must be collected efficiently, stored and managed properly, and analyzed appropriately. Sometimes, data from various sources must be pieced together to develop a clear picture of a complex situation, and decisions can depend on how various uncertain outcomes are valued relative to one another. Reliable use of data analysis in a decision setting requires familiarity with basic statistical ideas and techniques, together with the principles of decision making. Results must often be communicated to non-specialists who will make decisions based on the presentation. Furthermore, we may need to make decisions based on the analyses and interpretations of others. This subject examines the whole process of data collection, analysis and decision making appropriate for realistic, complex and uncertain settings.
Students completing the subject will gain a comprehensive understanding of the statistical measurement and modeling process from experimental design and data collection to synthesis, decision support, and report presentation. In passing they will become familiar with several standard statistical techniques, but this is not the primary aim of the subject. By being aware of the entire statistical process and the resources required to make good decisions, they will be better equipped to manage projects in realistic decision environments. Students will also become familiar with a major statistical computing package.
|Breadth Options:|| |
This subject is not available as a breadth subject.
|Fees Information:||Subject EFTSL, Level, Discipline & Census Date|
At the completion of this subject, students should gain the following generic skills:
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