sta4lm analyses of linear models
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
Available to Study Abroad StudentsYes
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
Learning resources
Readings
| Resource Type | Title | Resource Requirement | Author and Year | Publisher |
|---|---|---|---|---|
| Readings | Introduction to Linear Regression Analysis | Recommended | Montgomery, DC, Peck, EA and Vining, G 2006 | WILEY, 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.
- Related graduate capabilities and elements:
- 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.
- Related graduate capabilities and elements:
- 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.
- Related graduate capabilities and elements:
- 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.
- Related graduate capabilities and elements:
- 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.
- Related graduate capabilities and elements:
- 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.
- Related graduate capabilities and elements:
- Critical Thinking(Critical Thinking)
- Discipline-specific GCs(Discipline-specific GCs)
- Inquiry/ Research(Inquiry/ Research)
Subject options
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Melbourne, 2017, Semester 2, Day
Overview
Online enrolmentYes
Maximum enrolment sizeN/A
Enrolment information
Subject Instance Co-ordinatorAgus Salim
Class requirements
Computer LaboratoryWeek: 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.
LectureWeek: 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.
PracticalWeek: 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.
Assessments
| Assessment element | Comments | % | ILO* |
|---|---|---|---|
| 2 Assignments (approx. 800 words each) | 20 | 01, 02, 03, 04, 06 | |
| 2-hour short answer Final Examination | 50 | 01, 02, 03, 04, 06 | |
| 5-minute oral presentation briefly describing an applied research paper. | 5 | 04 | |
| 5-minute oral presentation of a current job ad | Hurdle Speaking Preparation/Practice. | ||
| Consulting role-play | 25 | 02, 03, 04, 05 |