STATISTICAL INFERENCE

STA4SI

2015

Credit points: 15

Subject outline

Statistical inference is used to describe procedures that draw conclusions from datasets arising from systems affected by random variation.This subject comprises components in estimation and testing hypotheses. Topics in the first component include method of moments and maximum likelihood, reduction by sufficiency and invariance, unbiasedness, consistency, efficiency and robustness. The second component examines size and power of tests, Neyman-Pearson lemma, optimality of tests, the likelihood ratio test and relationship to confidence interval estimation. This subject is co-taught with STA3SI. For STA4SI there is greater emphasis on research and inquiry with an expactation that students independently formulate proofs for extension questions related to the subject material. STA4SI is co-taught with STA3SI.

SchoolSchool Engineering&Mathematical Sciences

Credit points15

Subject Co-ordinatorAndriy Olenko

Available to Study Abroad StudentsYes

Subject year levelYear Level 4 - UG/Hons/1st Yr PG

Exchange StudentsYes

Subject particulars

Subject rules

Prerequisites STA2MD or STM2PM

Co-requisitesN/A

Incompatible subjects STA3SI

Equivalent subjectsN/A

Special conditionsN/A

Readings

Resource TypeTitleResource RequirementAuthor and YearPublisher
ReadingsIntroduction to Probability and Mathematical StatisticsRecommendedBain LJ, Engelhardt M 20002ND ED, DUXBURY.

Graduate capabilities & intended learning outcomes

01. Research, model and solve problems when randomness is involved

Activities:
10 assignments and weekly problem classes involve various modelling and problem solving questions. One question on each of five assignments provides little guidance and therefore requires students to research possible solutions.
Related graduate capabilities and elements:
Writing (Writing)
Discipline-specific GCs (Discipline-specific GCs)

02. Present clear, well structured proofs of important theoretical statistical model results. This includes detailed referencing to inportant statistical principles.

Activities:
Weekly problem classes involve theoretical derivations of results introduced in lectures. One question on each of five assignments provides little guidance and therefore requires students to research possible solutions to prove statistical results.
Related graduate capabilities and elements:
Creative Problem-solving (Creative Problem-solving)
Discipline-specific GCs (Discipline-specific GCs)
Writing (Writing)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)

03. Compute/derive mathematical calculations to investigate numerical properties of statistical models

Activities:
12 problem classes where students need to do this to solve complex problems. Modelled as worked examples in Lectures
Related graduate capabilities and elements:
Discipline-specific GCs (Discipline-specific GCs)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)
Creative Problem-solving (Creative Problem-solving)
Critical Thinking (Critical Thinking)

04. Present clear, well structured explanations of numerical results. This includes appropriate use of statistical and mathematical vocabulary

Activities:
10 assignments includes a 10% mark for each assignment relating to students written expression and clarity.
Related graduate capabilities and elements:
Inquiry/ Research (Inquiry/ Research)
Critical Thinking (Critical Thinking)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)
Discipline-specific GCs (Discipline-specific GCs)
Writing (Writing)

05. Independently formulate proofs for key theoretical results presented in the lectures.

Activities:
10 assignments include 20% of marks relating to research and enquiry where questions may only be answered with correct referencing to resources.
Related graduate capabilities and elements:
Critical Thinking (Critical Thinking)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)
Writing (Writing)
Creative Problem-solving (Creative Problem-solving)
Inquiry/ Research (Inquiry/ Research)
Discipline-specific GCs (Discipline-specific GCs)

Subject options

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Start date between: and    Key dates

Melbourne, 2015, Semester 1, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorAndriy Olenko

Class requirements

Practical Week: 10 - 22
One 1.0 hours practical per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Lecture Week: 10 - 22
Three 1.0 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Assessments

Assessment elementComments% ILO*
10 Assignments (approx. 250-300 words each)30 01, 02, 03, 04
3-hour short answer Final Examination70 01, 02, 03, 04, 05