ANALYSES OF LINEAR MODELS

STA3LM

2021

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. Linear models relate a response variable to one or more explanatory variables enabling researchers to answer important research questions and make predictions about how variables will respond. These methods are used in many areas including biological science, economics, engineering, medical science and psychological science. Topics covered in this subject 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 strong emphasis on preparing students for future careers in statistics. This subject addresses La Trobe's Innovation and Entrepreneurship Essential which entails developing the ability to tackle problems creatively, generating new ideas, taking calculated risks and creating change to achieve ambitions now and in the future.

SchoolEngineering and Mathematical Sciences

Credit points15

Subject Co-ordinatorAmanda Shaker

Available to Study Abroad/Exchange StudentsYes

Subject year levelYear Level 3 - UG

Available as ElectiveNo

Learning ActivitiesN/A

Capstone subjectYes

Subject particulars

Subject rules

PrerequisitesSTA2MD OR STA2AMS OR STA2ABS OR STM2PM OR STA2ASM

Co-requisitesN/A

Incompatible subjectsSTA4LM

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 Linear Regression Analysis

Resource TypeRecommended

Resource RequirementN/A

AuthorMontgomery, DC, Peck, EA and Vining, G

Year2006

Edition/Volume4TH EDITION

PublisherWILEY

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. Present clear, well-structured proofs of important fundamental linear model results that include appropriate use of statistical and mathematical vocabulary and notation.
02. Describe and use key analytical linear modelling tools including justification of appropriate usage based on known model/data conditions.
03. Implement and document various strategies to identify and account for model inadequacies.
04. Present written and oral communications of statistical results clearly in a manner that can be understood by experts and lay audience.
05. Work efficiently and effectively as a member of a team to produce a statistical analysis based on real data.

Subject options

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Start date between: and    Key dates

Melbourne (Bundoora), 2021, Semester 2, Day

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorAmanda Shaker

Class requirements

Computer Laboratory Week: 32 - 43
One 1.00 h 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 1.00 h 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 1.00 h practical per week on weekdays during the day from week 32 to week 43 and delivered via face-to-face.

Assessments

Assessment elementCommentsCategoryContributionHurdle% ILO*
2 Assignments (equivalent to 1000 words)N/AN/AN/ANo20 SILO1, SILO2, SILO3, SILO4
2-hour short answer Final Examination (2000 word equiv)N/AN/AN/ANo50 SILO1, SILO2, SILO3, SILO4
Two 5-minute oral presentations (500 word equivalent) Hurdle: satisfactory oral presentations must be givenN/AN/AN/AYes5 SILO4
Consulting role-play (1000 word equiv.)N/AN/AN/ANo25 SILO2, SILO3, SILO4, SILO5