Students

STAT375 – Linear Models

2014 – S1 Evening

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Unit Convenor
Gillian Heller
Contact via gillian.heller@mq.edu.au
E4A 533
Thursday 12-2 pm
Lecturer
Robin Van Den Honert
Contact via rob.vandenhonert@mq.edu.au
Credit points Credit points
3
Prerequisites Prerequisites
39cp including (STAT270(P) or STAT271(P) or BIOL235(P) or PSY222(P) or PSY248(P))
Corequisites Corequisites
Co-badged status Co-badged status
Unit description Unit description
This unit discusses statistical modelling in general and in particular demonstrates the wide applicability of linear and generalised linear models. Topics include multiple linear regression, logistic regression and Poisson regression. The emphasis is on practical issues in data analysis with some reference to the theoretical background. Statistical packages are used for both model fitting and diagnostic testing.

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates

Learning Outcomes

On successful completion of this unit, you will be able to:

  • Define relevant terminology and describe the main concepts of linear models and simple generalized linear models.
  • Formulate and solve theoretical problems in linear modelling (using matrix notation when necessary).
  • Fit a linear model to obtain estimates together with their standard errors in applied problems.
  • Analyse the adequacy of a linear model and suggest appropriate modifications when needed.
  • Formulate and solve applied problems using linear modelling.
  • Use standard statistics packages to carry out these analyses.
  • Communicate clearly their knowledge of the subject matter of linear models and their solutions to problems involving linear modelling.

Assessment Tasks

Name Weighting Due
Assignment 1 15% 31 March
Assignment 2 15% 12 May
Assignment 3 15% 2 June
Tutorials 5% 3 March - 9 June
Examination 50% TBA

Assignment 1

Due: 31 March
Weighting: 15%

There are three assignments, worth 15% each. They should be submitted to the lecturer, by the due time and date. They give you an opportunity to reinforce and apply the concepts covered in lectures and the skills learned in tutorial sessions. Extensions to assignments are at the discretion of the lecturer. It is the responsibility of the student to prove that there has been unavoidable disruption. Marks will be deducted for late submissions in the absence of an approved extension, at a rate of 5% of the total mark per day late.

In order to pass the unit, students need to perform satisfactorily (i.e. achieve at least 50%) on all components of assessment.


On successful completion you will be able to:
  • Define relevant terminology and describe the main concepts of linear models and simple generalized linear models.
  • Formulate and solve theoretical problems in linear modelling (using matrix notation when necessary).
  • Fit a linear model to obtain estimates together with their standard errors in applied problems.
  • Analyse the adequacy of a linear model and suggest appropriate modifications when needed.
  • Formulate and solve applied problems using linear modelling.
  • Use standard statistics packages to carry out these analyses.
  • Communicate clearly their knowledge of the subject matter of linear models and their solutions to problems involving linear modelling.

Assignment 2

Due: 12 May
Weighting: 15%

As Assignment 1.


On successful completion you will be able to:
  • Define relevant terminology and describe the main concepts of linear models and simple generalized linear models.
  • Formulate and solve theoretical problems in linear modelling (using matrix notation when necessary).
  • Fit a linear model to obtain estimates together with their standard errors in applied problems.
  • Analyse the adequacy of a linear model and suggest appropriate modifications when needed.
  • Formulate and solve applied problems using linear modelling.
  • Use standard statistics packages to carry out these analyses.
  • Communicate clearly their knowledge of the subject matter of linear models and their solutions to problems involving linear modelling.

Assignment 3

Due: 2 June
Weighting: 15%

As Assignment 1.


On successful completion you will be able to:
  • Define relevant terminology and describe the main concepts of linear models and simple generalized linear models.
  • Formulate and solve theoretical problems in linear modelling (using matrix notation when necessary).
  • Fit a linear model to obtain estimates together with their standard errors in applied problems.
  • Analyse the adequacy of a linear model and suggest appropriate modifications when needed.
  • Formulate and solve applied problems using linear modelling.
  • Use standard statistics packages to carry out these analyses.
  • Communicate clearly their knowledge of the subject matter of linear models and their solutions to problems involving linear modelling.

Tutorials

Due: 3 March - 9 June
Weighting: 5%

A mark worth 5% of your final mark, will be given for your participation in the laboratory tutorials, on the basis of collected laboratory sheets.


On successful completion you will be able to:
  • Define relevant terminology and describe the main concepts of linear models and simple generalized linear models.
  • Formulate and solve theoretical problems in linear modelling (using matrix notation when necessary).
  • Fit a linear model to obtain estimates together with their standard errors in applied problems.
  • Analyse the adequacy of a linear model and suggest appropriate modifications when needed.
  • Formulate and solve applied problems using linear modelling.
  • Use standard statistics packages to carry out these analyses.
  • Communicate clearly their knowledge of the subject matter of linear models and their solutions to problems involving linear modelling.

Examination

Due: TBA
Weighting: 50%

The examination will cover the material studied in the whole unit and address all the unit outcomes. You may take one A4 sheet, handwritten on both sides, into the final examination.

 

Grading in this Unit

The final Standardised Numerical Grade (SNG) in STAT375 will be based on students’ work during the semester and in the final examination. The determination of the final SNG will be based on performance of individual assessment tasks against criteria and standards as detailed in the Grading Policy (http://mq.edu.au/policy/docs/grading/policy.html). Final grades will be awarded on the basis of students’ overall performance and the extent to which they demonstrate fulfillment of the learning outcomes listed for this unit.

You MUST perform satisfactorily in the final examination in order to pass the unit, regardless of your performance throughout the semester.

A supplementary examination will only be granted if a student has satisfactory coursework (ie. at least 25 marks out of 50). If a supplementary exam is granted as a result of the Special Consideration process, it will be scheduled after the conclusion of the official exam period.

 

The only exception to not sitting an examination at the designated time is because of documented illness or unavoidable disruption. In these circumstances you may wish to consider applying for Special Consideration. It is Macquarie University policy not to set early examinations for individuals or groups of students. You are expected to be available until the end of the teaching semester, that is the final day of the official examination period.

 


On successful completion you will be able to:
  • Define relevant terminology and describe the main concepts of linear models and simple generalized linear models.
  • Formulate and solve theoretical problems in linear modelling (using matrix notation when necessary).
  • Fit a linear model to obtain estimates together with their standard errors in applied problems.
  • Analyse the adequacy of a linear model and suggest appropriate modifications when needed.
  • Formulate and solve applied problems using linear modelling.
  • Use standard statistics packages to carry out these analyses.
  • Communicate clearly their knowledge of the subject matter of linear models and their solutions to problems involving linear modelling.

Delivery and Resources

You should attend the following classes each week:

· 2 hour lecture: Monday 12 - 2pm pm, W5C 302

· 2 hour laboratory tutorial: Monday 4 - 6 pm, E4B 102

Lectures begin in Week 1. Students should print off the course notes from iLearn, and bring them to lectures.

Tutorials begin in week 1 and are based on work from the current week’s lecture. Tutorials are held in the computing lab E4B 102 and allow you to practise techniques learnt in lectures. We will mainly use SPSS, but we will supplement this with other statistical software. You will complete worksheets as part of the learning process. SPSS is installed in the computing labs in E4B, and will be used in tutorial sessions and for assignments. Assignments may be completed in these rooms. It is most convenient to bring a memory stick when using these computers.

Text book The recommended text (available from the Co-op Bookshop) is: Chatterjee S & Hadi AS (2012). Regression Analysis By Example, 5th Edition, Wiley

Calculator  You will need a calculator with statistical mode for the final examination.

Software  The statistical software SPSS will be the main package used. In addition, we will be demonstrating applications using other statistical software such as Minitab and Arc. All of this software is available in the computer labs in E4B.

  • You may wish to buy a copy of SPSS for home use. The Co-op Bookshop has SPSS Grad pack, a full version with a one-year licence.
  • You may also access SPSS remotely, at no cost, via iLab:https://wiki.mq.edu.au/display/iLab/About

Staff consultation hours  Members of the Statistics Department have consultation hours each week when they are available to help students. These consultation hours are listed on the doors of the Statistics staff located on E4A level 5.

 

Changes since previous offering  There are no substantial changes.

 

Unit Schedule

Date

Week

Topic

Text chapter

Assessment

3 Mar

1

Simple linear regression

1,2

 

10 Mar

2

Simple linear regression contd, introduction to multiple linear regression

2

 

17 Mar

3

The model in matrix form, hypothesis tests, residuals, residual & partial regression plots

3,4

Assignment 1 handed out

24 Mar

4

Diagnostics contd: extreme observations (leverage, DFBETAs, Cook’s distances); transformations

4, 6

 

31 Mar

5

Transformations contd; collinearity

6, 9

Assignment 1 handed in

7 April

6

Polynomial regression; categorical covariates

5

 

 

 

Mid-semester break

 

 

28 April

7

Analysis of change

-

Assignment 2 handed out

5 May

8

Interaction and confounding

5

 

12 May

9

Variable selection, model building

11

Assignment 2 handed in 

19 May

10

Introduction to generalized linear models; Logistic regression

12

Assignment 3 handed out

26 May

11

Logistic regression ; Poisson regression

12, 13

 

2 June

12

Poisson regression

13

Assignment 3 handed in

9 June

13

No lecture (public holiday)

 

Assignment 3 handed back in tutorial

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central. Students should be aware of the following policies in particular with regard to Learning and Teaching:

Academic Honesty Policy http://mq.edu.au/policy/docs/academic_honesty/policy.html

Assessment Policy  http://mq.edu.au/policy/docs/assessment/policy.html

Grading Policy http://mq.edu.au/policy/docs/grading/policy.html

Grade Appeal Policy http://mq.edu.au/policy/docs/gradeappeal/policy.html

Grievance Management Policy http://mq.edu.au/policy/docs/grievance_management/policy.html

Disruption to Studies Policy http://www.mq.edu.au/policy/docs/disruption_studies/policy.html The Disruption to Studies Policy is effective from March 3 2014 and replaces the Special Consideration Policy.

In addition, a number of other policies can be found in the Learning and Teaching Category of Policy Central.

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/support/student_conduct/

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

Learning Skills

Learning Skills (mq.edu.au/learningskills) provides academic writing resources and study strategies to improve your marks and take control of your study.

Student Services and Support

Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.

Student Enquiries

For all student enquiries, visit Student Connect at ask.mq.edu.au

IT Help

For help with University computer systems and technology, visit http://informatics.mq.edu.au/help/

When using the University's IT, you must adhere to the Acceptable Use Policy. The policy applies to all who connect to the MQ network including students.

Graduate Capabilities

Capable of Professional and Personal Judgement and Initiative

We want our graduates to have emotional intelligence and sound interpersonal skills and to demonstrate discernment and common sense in their professional and personal judgement. They will exercise initiative as needed. They will be capable of risk assessment, and be able to handle ambiguity and complexity, enabling them to be adaptable in diverse and changing environments.

This graduate capability is supported by:

Learning outcomes

  • Formulate and solve theoretical problems in linear modelling (using matrix notation when necessary).
  • Analyse the adequacy of a linear model and suggest appropriate modifications when needed.
  • Formulate and solve applied problems using linear modelling.
  • Communicate clearly their knowledge of the subject matter of linear models and their solutions to problems involving linear modelling.

Assessment tasks

  • Assignment 1
  • Assignment 2
  • Assignment 3
  • Tutorials
  • Examination

Discipline Specific Knowledge and Skills

Our graduates will take with them the intellectual development, depth and breadth of knowledge, scholarly understanding, and specific subject content in their chosen fields to make them competent and confident in their subject or profession. They will be able to demonstrate, where relevant, professional technical competence and meet professional standards. They will be able to articulate the structure of knowledge of their discipline, be able to adapt discipline-specific knowledge to novel situations, and be able to contribute from their discipline to inter-disciplinary solutions to problems.

This graduate capability is supported by:

Learning outcomes

  • Define relevant terminology and describe the main concepts of linear models and simple generalized linear models.
  • Formulate and solve theoretical problems in linear modelling (using matrix notation when necessary).
  • Fit a linear model to obtain estimates together with their standard errors in applied problems.
  • Analyse the adequacy of a linear model and suggest appropriate modifications when needed.
  • Formulate and solve applied problems using linear modelling.
  • Use standard statistics packages to carry out these analyses.

Assessment tasks

  • Assignment 1
  • Assignment 2
  • Assignment 3
  • Tutorials
  • Examination

Critical, Analytical and Integrative Thinking

We want our graduates to be capable of reasoning, questioning and analysing, and to integrate and synthesise learning and knowledge from a range of sources and environments; to be able to critique constraints, assumptions and limitations; to be able to think independently and systemically in relation to scholarly activity, in the workplace, and in the world. We want them to have a level of scientific and information technology literacy.

This graduate capability is supported by:

Learning outcomes

  • Define relevant terminology and describe the main concepts of linear models and simple generalized linear models.
  • Formulate and solve theoretical problems in linear modelling (using matrix notation when necessary).
  • Fit a linear model to obtain estimates together with their standard errors in applied problems.
  • Analyse the adequacy of a linear model and suggest appropriate modifications when needed.
  • Formulate and solve applied problems using linear modelling.
  • Use standard statistics packages to carry out these analyses.

Assessment tasks

  • Assignment 1
  • Assignment 2
  • Assignment 3
  • Tutorials
  • Examination

Problem Solving and Research Capability

Our graduates should be capable of researching; of analysing, and interpreting and assessing data and information in various forms; of drawing connections across fields of knowledge; and they should be able to relate their knowledge to complex situations at work or in the world, in order to diagnose and solve problems. We want them to have the confidence to take the initiative in doing so, within an awareness of their own limitations.

This graduate capability is supported by:

Learning outcomes

  • Formulate and solve theoretical problems in linear modelling (using matrix notation when necessary).
  • Fit a linear model to obtain estimates together with their standard errors in applied problems.
  • Analyse the adequacy of a linear model and suggest appropriate modifications when needed.
  • Formulate and solve applied problems using linear modelling.
  • Use standard statistics packages to carry out these analyses.

Assessment tasks

  • Assignment 1
  • Assignment 2
  • Assignment 3
  • Tutorials
  • Examination

Creative and Innovative

Our graduates will also be capable of creative thinking and of creating knowledge. They will be imaginative and open to experience and capable of innovation at work and in the community. We want them to be engaged in applying their critical, creative thinking.

This graduate capability is supported by:

Learning outcomes

  • Formulate and solve theoretical problems in linear modelling (using matrix notation when necessary).
  • Fit a linear model to obtain estimates together with their standard errors in applied problems.
  • Analyse the adequacy of a linear model and suggest appropriate modifications when needed.
  • Formulate and solve applied problems using linear modelling.

Assessment tasks

  • Assignment 1
  • Assignment 2
  • Assignment 3
  • Tutorials
  • Examination

Effective Communication

We want to develop in our students the ability to communicate and convey their views in forms effective with different audiences. We want our graduates to take with them the capability to read, listen, question, gather and evaluate information resources in a variety of formats, assess, write clearly, speak effectively, and to use visual communication and communication technologies as appropriate.

This graduate capability is supported by:

Learning outcomes

  • Define relevant terminology and describe the main concepts of linear models and simple generalized linear models.
  • Communicate clearly their knowledge of the subject matter of linear models and their solutions to problems involving linear modelling.

Assessment tasks

  • Assignment 1
  • Assignment 2
  • Assignment 3
  • Tutorials
  • Examination

Engaged and Ethical Local and Global citizens

As local citizens our graduates will be aware of indigenous perspectives and of the nation's historical context. They will be engaged with the challenges of contemporary society and with knowledge and ideas. We want our graduates to have respect for diversity, to be open-minded, sensitive to others and inclusive, and to be open to other cultures and perspectives: they should have a level of cultural literacy. Our graduates should be aware of disadvantage and social justice, and be willing to participate to help create a wiser and better society.

This graduate capability is supported by:

Learning outcomes

  • Formulate and solve applied problems using linear modelling.
  • Communicate clearly their knowledge of the subject matter of linear models and their solutions to problems involving linear modelling.

Assessment tasks

  • Assignment 1
  • Assignment 2
  • Assignment 3
  • Tutorials
  • Examination

Socially and Environmentally Active and Responsible

We want our graduates to be aware of and have respect for self and others; to be able to work with others as a leader and a team player; to have a sense of connectedness with others and country; and to have a sense of mutual obligation. Our graduates should be informed and active participants in moving society towards sustainability.

This graduate capability is supported by:

Learning outcomes

  • Formulate and solve applied problems using linear modelling.
  • Communicate clearly their knowledge of the subject matter of linear models and their solutions to problems involving linear modelling.

Assessment tasks

  • Assignment 1
  • Assignment 2
  • Assignment 3
  • Tutorials
  • Examination