stm4si statistical inference
STATISTICAL INFERENCE
STM4SI
2017
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 STM3SI. For STM4SI there is greater emphasis on research and inquiry with an expactation that students independently formulate proofs for extension questions related to the subject material.
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, STA4SI, STM3SI
Equivalent subjectsN/A
Special conditionsN/A
Learning resources
Readings
| Resource Type | Title | Resource Requirement | Author and Year | Publisher |
|---|---|---|---|---|
| Readings | Introduction to Probability and Mathematical Statistics | Recommended | Bain LJ, Engelhardt M 2000 | 2ND ED, DUXBURY. |
| Readings | Online learning materials (readings and examples) | Prescribed | 2016 | La Trobe University |
Graduate capabilities & intended learning outcomes
01. Research, model and solve problems when randomness is involved
- Activities:
- 8 assignments and weekly problem classes involve various modelling and problem solving questions. One question on each of four 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 four 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:
- 8 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:
- 8 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
Select to view your study options…
Melbourne, 2017, Semester 1, Day
Overview
Online enrolmentYes
Maximum enrolment sizeN/A
Enrolment information
Subject Instance Co-ordinatorAndriy Olenko
Class requirements
LectureWeek: 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.
PracticalWeek: 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.
Assessments
| Assessment element | Comments | % | ILO* |
|---|---|---|---|
| 8 Assignments (approx. 200-250 words each) | 30 | 01, 02, 03, 04 | |
| 3-hour short answer Final Examination (approx. 3000 words) | 70 | 01, 02, 03, 04, 05 |