# STATISTICAL INFERENCE

STM4SI

2018

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

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

Resource TypeTitleResource RequirementAuthor and YearPublisher
ReadingsIntroduction to Probability and Mathematical StatisticsRecommendedBain LJ, Engelhardt M 20002ND ED, DUXBURY.

## 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.
Writing
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.
Creative Problem-solving
Discipline-specific GCs
Writing
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
Discipline-specific GCs
Quantitative Literacy/ Numeracy
Creative Problem-solving
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.
Inquiry/ Research
Critical Thinking
Quantitative Literacy/ Numeracy
Discipline-specific GCs
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.
Critical Thinking
Quantitative Literacy/ Numeracy
Writing
Creative Problem-solving
Inquiry/ Research
Discipline-specific GCs

## Subject options

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Start date between: and    Key dates

## Melbourne, 2018, Semester 1, Day

### Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorAndriy Olenko

### Class requirements

Lecture Week: 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.

Practical Week: 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.