stm3si statistical inference




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


Incompatible subjects STA4SI, STA3SI, STM4SI

Equivalent subjectsN/A

Special conditionsN/A

Learning resources


Resource TypeTitleResource RequirementAuthor and YearPublisher
ReadingsIntroduction to Probability and Mathematical StatisticsRecommendedBain, LJ and Engelhardt, M 20002ND EDN, DUXBURY.
ReadingsOnline learning materials (readings and examples)Prescribed2016La Trobe university, LMS

Graduate capabilities & intended learning outcomes

01. Model and solve problems when randomness is involved

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.

Weekly problem classes involve theoretical derivations of results introduced in lectures.

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

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

8 assignments includes a 10% mark for each assignment relating to student's written expression and clarity.

Subject options

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Start date between: and    Key dates

Melbourne, 2019, Semester 1, Day


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.


Assessment elementComments%ILO*
8 Assignments (approx.180 words each)3001, 02, 03, 04
3-hour short answer Final Examination (approx. 3000 words)7001, 02, 03, 04