stm3si statistical inference
STATISTICAL INFERENCE
STM3SI
2019
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. STM3SI is co-taught with STM4SI.
SchoolSchool Engineering&Mathematical Sciences
Credit points15
Subject Co-ordinatorAndriy Olenko
Available to Study Abroad StudentsYes
Subject year levelYear Level 3 - UG
Exchange StudentsYes
Subject particulars
Subject rules
Prerequisites STA2MD or STM2PM
Co-requisitesN/A
Incompatible subjects STA4SI, STA3SI, STM4SI
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 and Engelhardt, M 2000 | 2ND EDN, DUXBURY. |
| Readings | Online learning materials (readings and examples) | Prescribed | 2016 | La Trobe university, LMS |
Graduate capabilities & intended learning outcomes
01. Model and solve problems when randomness is involved
- Activities:
- 8 assignments and weekly problem classes involve various modelling and problem solving questions.
02. Present clear, well structured proofs of important theoretical statistical model results.
- Activities:
- Weekly problem classes involve theoretical derivations of results introduced in lectures.
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
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 student's written expression and clarity.
Subject options
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Melbourne, 2019, Semester 1, Day
Overview
Online enrolmentYes
Maximum enrolment sizeN/A
Enrolment information
Subject Instance Co-ordinatorAndriy Olenko
Class requirements
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.
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.
Assessments
| Assessment element | Comments | % | ILO* |
|---|---|---|---|
| 8 Assignments (approx.180 words each) | 30 | 01, 02, 03, 04 | |
| 3-hour short answer Final Examination (approx. 3000 words) | 70 | 01, 02, 03, 04 |