# ANALYSES OF LINEAR MODELS

STA4LM

2017

Credit points: 15

## Subject outline

Modern research often involves the analysis of data for more than one variable and in this regard, linear models are the most widely used class of models. They relate a reponse variable to one or more explanatory variables enabling researchers to answer important research questions as well as make future predictions. The methods are used in many areas including biological science, economics, engineering, medical science and psychological science. Topics include simple and multiple linear regression, response and explanatory variable transformations, ANOVA and ANCOVA, as well as more modern methodologies such as generalized linear models and linear mixed effects models. This subject has a stong emphasis on preparing students for future careers in statistics. This subject is co-offered with STA3LM although assumes a deeper level of understanding of the subject content.

SchoolSchool Engineering&Mathematical Sciences

Credit points15

Subject Co-ordinatorAgus Salim

Subject year levelYear Level 4 - UG/Hons/1st Yr PG

Exchange StudentsYes

## Subject particulars

### Subject rules

Prerequisites STA2ABS or STA2AMS or STA2MD or STM2PM or approval of honours or masters by coursework coordinator

Co-requisitesN/A

Incompatible subjects STA3LM

Equivalent subjectsN/A

Special conditionsN/A

Resource TypeTitleResource RequirementAuthor and YearPublisher
ReadingsIntroduction to Linear Regression AnalysisRecommendedMontgomery, DC, Peck, EA and Vining, G 2006WILEY, 4TH EDITION.

## Graduate capabilities & intended learning outcomes

01. Present clear, well structured proofs of important fundamental linear model results. This includes appropriate use of statistical and mathematical vocabulary and notation.

Activities:
Weekly problem classes involve theoretical derivations of results introduced in lectures. 2 assignments consist of up to 50% assessed theoretical derivations.
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)
Discipline-specific GCs (Discipline-specific GCs)
Creative Problem-solving (Creative Problem-solving)
Writing (Writing)

02. Describe and use key analytical linear modelling tools including a justification of appropriate usage based on known model/data conditions.

Activities:
Appropriate usage of methodologies is discussed and modelled via example in lectures. Weekly computer lab classes involve interpreting, including a justification of, computer output relating to analyses of real data sets. 2 assignments consist of up to 50% assessed usage and justification of methodolgies.
Critical Thinking (Critical Thinking)
Discipline-specific GCs (Discipline-specific GCs)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)
Teamwork (Teamwork)
Inquiry/ Research (Inquiry/ Research)
Writing (Writing)

03. Implement and document various strategies to identify and account for model inadequacies.

Activities:
Lectures in week 3 onwards introduce students to ways in which model conditions can be critiqued and to the manner in which inadequacies can be corrected. Computer lab classes from week 3 onwards and assignments 2 and 3 require students to defend the use of methodolgies by implenting model checking as well as requiring the correction of inadequacies when necessary.
Critical Thinking (Critical Thinking)
Creative Problem-solving (Creative Problem-solving)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)
Ethical Awareness (Ethical Awareness)
Teamwork (Teamwork)

04. Present clear written and oral commuications of statistical results in a manner which can be understood by a scientist who fully understands the variables in the associated data set, but who has only a basic understanding of statistics.

Activities:
Simple written summaries based on real data analyses are modelled weekly in lectures. Weekly computer lab classes in part involve students writing simple evidence based conclusions. 3 assignments also partly require students to prepare such simple conclusions. 25% of the total assessment is allocated to a consulting role-roleplay which requires students to work in teams to produce a statistical analysis based on real data. Thie role-play culminates in each student individually verbally communicate results of the analysis to the lecturer where the lecturer acts as a scientist with little statistics background.
Writing (Writing)
Ethical Awareness (Ethical Awareness)
Critical Thinking (Critical Thinking)
Creative Problem-solving (Creative Problem-solving)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)
Teamwork (Teamwork)
Inquiry/ Research (Inquiry/ Research)
Discipline-specific GCs (Discipline-specific GCs)
Speaking (Speaking)

05. Work efficiently and effectively as a member of a team to produce a statistcal analysis based on real data.

Activities:
25% of the total assessment is allocated to a consulting role-roleplay which requires students to work in teams to produce a statistical analysis based on real data.
Inquiry/ Research (Inquiry/ Research)
Speaking (Speaking)
Quantitative Literacy/ Numeracy (Quantitative Literacy/ Numeracy)
Discipline-specific GCs (Discipline-specific GCs)
Creative Problem-solving (Creative Problem-solving)
Teamwork (Teamwork)
Ethical Awareness (Ethical Awareness)

06. Justify the choice of an appropriate error term correlation structure for linear mixed effects model analyses.

Activities:
Questions specific to STA4LM students in the week 12 computer lab and practice class will involve the using the internet to explore various correlations structure. The student is expected decribe the most commonly used of these. The final exam will also require justification of correlation structure.
Critical Thinking (Critical Thinking)
Discipline-specific GCs (Discipline-specific GCs)
Inquiry/ Research (Inquiry/ Research)

## Subject options

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Start date between: and    Key dates

## Melbourne, 2017, Semester 2, Day

### Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorAgus Salim

### Class requirements

Computer Laboratory Week: 32 - 43
One 1.0 hours computer laboratory per week on weekdays during the day from week 32 to week 43 and delivered via face-to-face.

Lecture Week: 31 - 43
Two 1.0 hours lecture per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.

Practical Week: 32 - 43
One 1.0 hours practical per week on weekdays during the day from week 32 to week 43 and delivered via face-to-face.