PROBABILITY MODELS

MST2PM

Not currently offered

Credit points: 15

Subject outline

The analysis of scientific, engineering and economic data makes extensive use of probability models. This subject describes the most basic of these models and their properties. Specific topics covered in this subject include a wide range of discrete and continuous univariate distributions; joint distributions; conditional expectation; mean and variance of linear combinations of random variables; Chebyshev's inequality; moment generating functions; the law of large numbers; the Central Limit Theorem. To help students with little or no previous knowledge of calculus, the equivalent of one lecture per week consists of relevant mathematical topics to support the other content.

SchoolSchool of Humanities & Social Sciences

Credit points15

Subject Co-ordinatorKatherine Seaton

Available to Study Abroad StudentsYes

Subject year levelYear Level 2 - UG

Exchange StudentsYes

Subject particulars

Subject rules

Prerequisites (MAT1CDE and MAT1NLA) or MAT1CLA or STA1SS or STA1LS or STA1PSY

Co-requisitesN/A

Incompatible subjects STA2MD AND STA2MDA

Equivalent subjectsN/A

Special conditionsN/A

Graduate capabilities & intended learning outcomes

01. Model and solve problems when randomness is involved

Activities:
Modelled in lectures, and practised in tutorials
Related graduate capabilities and elements:
Inquiry/ Research (Inquiry/ Research)
Creative Problem-solving (Creative Problem-solving)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)
Critical Thinking (Critical Thinking)
Discipline-specific GCs (Discipline-specific GCs)

02. Compute/derive mathematical calculations to investigate numerical properties of probability models

Activities:
Modelled in lectures, and practised in tutorials
Related graduate capabilities and elements:
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)
Discipline-specific GCs (Discipline-specific GCs)
Creative Problem-solving (Creative Problem-solving)
Critical Thinking (Critical Thinking)
Inquiry/ Research (Inquiry/ Research)

03. Derive some basic probability results in selected areas of application

Activities:
Modelled in lectures, and practised in tutorials
Related graduate capabilities and elements:
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)
Critical Thinking (Critical Thinking)
Discipline-specific GCs (Discipline-specific GCs)

04. Defend or question the validity of different probability models

Activities:
Modelled in lectures, and practised in tutorials
Related graduate capabilities and elements:
Creative Problem-solving (Creative Problem-solving)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)
Critical Thinking (Critical Thinking)
Discipline-specific GCs (Discipline-specific GCs)
Writing (Writing)

05. Present clear, well structured explanations of numerical results including the appropriate use of statistical and mathematical vocabulary

Activities:
Modelled in lectures, and practised in tutorials and on formative assignments, with particular emphasis on the work students complete at home with time to proof-read
Related graduate capabilities and elements:
Writing (Writing)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)

Subject options

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