STATISTICAL INFERENCE

STM3SI

2020

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.

School: Engineering and Mathematical Sciences (Pre 2022)

Credit points: 15

Subject Co-ordinator: Andriy Olenko

Available to Study Abroad/Exchange Students: Yes

Subject year level: Year Level 3 - UG

Available as Elective: No

Learning Activities: N/A

Capstone subject: No

Subject particulars

Subject rules

Prerequisites: STM2PM OR STA2MD

Co-requisites: N/A

Incompatible subjects: STM4SI OR STA3SI OR STA4SI

Equivalent subjects: N/A

Quota Management Strategy: N/A

Quota-conditions or rules: N/A

Special conditions: N/A

Minimum credit point requirement: N/A

Assumed knowledge: N/A

Learning resources

Introduction to Probability and Mathematical Statistics

Resource Type: Book

Resource Requirement: Recommended

Author: Bain, LJ and Engelhardt, M

Year: 2000

Edition/Volume: 2ND EDN

Publisher: DUXBURY

ISBN: N/A

Chapter/article title: N/A

Chapter/issue: N/A

URL: N/A

Other description: N/A

Source location: N/A

Career Ready

Career-focused: No

Work-based learning: No

Self sourced or Uni sourced: N/A

Entire subject or partial subject: N/A

Total hours/days required: N/A

Location of WBL activity (region): N/A

WBL addtional requirements: N/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

Melbourne (Bundoora), 2020, Semester 1, Day

Overview

Online enrolment: Yes

Maximum enrolment size: N/A

Subject Instance Co-ordinator: Andriy Olenko

Class requirements

LectureWeek: 10 - 22
Three 1.00 hour 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 hour 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/ANo30SILO1, SILO2, SILO3, SILO4

3-hour short answer Final Examination (approx. 3000 words)

N/AN/AN/ANo70SILO1, SILO2, SILO3, SILO4