STATISTICAL INFERENCE

STM3SI

2021

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.

SchoolEngineering and Mathematical Sciences

Credit points15

Subject Co-ordinatorAndriy Olenko

Available to Study Abroad/Exchange StudentsYes

Subject year levelYear Level 3 - UG

Available as ElectiveNo

Learning ActivitiesN/A

Capstone subjectNo

Subject particulars

Subject rules

PrerequisitesSTA2MD OR STM2PM

Co-requisitesN/A

Incompatible subjectsSTA3SI OR STA4SI OR STM4SI

Equivalent subjectsN/A

Quota Management StrategyN/A

Quota-conditions or rulesN/A

Special conditionsN/A

Minimum credit point requirementN/A

Assumed knowledgeN/A

Readings

Introduction to Probability and Mathematical Statistics

Resource TypeRecommended

Resource RequirementN/A

AuthorBain, LJ and Engelhardt, M

Year2000

Edition/Volume2ND EDN

PublisherDUXBURY

ISBNN/A

Chapter/article titleN/A

Chapter/issueN/A

URLN/A

Other descriptionN/A

Source locationN/A

Career Ready

Career-focusedNo

Work-based learningNo

Self sourced or Uni sourcedN/A

Entire subject or partial subjectN/A

Total hours/days requiredN/A

Location of WBL activity (region)N/A

WBL addtional requirementsN/A

Graduate capabilities & intended learning outcomes

Graduate Capabilities

Intended Learning Outcomes

01. Model and solve problems when randomness is involved
02. Present clear, well structured proofs of important theoretical statistical model results.
03. Compute/derive mathematical calculations to investigate numerical properties of statistical models
04. Present clear, well structured explanations of numerical results. This includes appropriate use of statistical and mathematical vocabulary

Subject options

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Start date between: and    Key dates

Melbourne (Bundoora), 2021, Semester 1, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorAndriy Olenko

Class requirements

LectureWeek: 10 - 22
Three 1.00 h lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

PracticalWeek: 10 - 22
One 1.00 h practical per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Assessments

Assessment elementCommentsCategoryContributionHurdle% ILO*
8 Assignments (approx.180 words each)N/AN/AN/ANo30 SILO1, SILO2, SILO3, SILO4
3-hour short answer Final Examination (approx. 3000 words)N/AN/AN/ANo70 SILO1, SILO2, SILO3, SILO4