Students

STAE270 – Applied Statistics

2014 – S2 External

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Lecturer
Peter Petocz
Contact via 9850 9174
E4A 529
Monday 9-11
Lecturer
Tim Keighley
Contact via 0406 016 227
off campus
by phone or email
Credit points Credit points
3
Prerequisites Prerequisites
Admission to GCertSc
Corequisites Corequisites
Co-badged status Co-badged status
STAT270 Applied Statistics
Unit description Unit description
This unit aims to extend and broaden statistical experience from 100-level statistics units. It focuses on relationships between categorical or continuous explanatory variables and a continuous response variable using the techniques of one-way and two-way analysis of variance and simple and multiple linear regression. Data management, graphical presentation of results, and power analysis are also investigated. The unit has a strong practical component built around a substantial collaborative project planned and carried out during the semester, and graduate capabilities such as communication, teamwork, problem solving and ethics are addressed in this context.

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:

  • • Discuss the role of independent (predictor) and dependent (response) variables and distinguish between categorical and quantitative variables.
  • • Formulate a research question and select an appropriate statistical model for investigating it (from the range of models studied in this unit).
  • • Understand and appreciate the ethical aspects of carrying out statistical research and analysis.
  • • Plan and organise the collection of appropriate data, and manage these data electronically.
  • • Apply and interpret the statistical methods covered in this unit to analyse data and provide answers to a research question.
  • • Understand the assumptions underlying these statistical methods and decide whether they are reasonable in any particular case.
  • • Use standard statistics packages to carry out these analyses.
  • • Communicate clearly the results from these statistical analyses and relate these results to the original research question.

Assessment Tasks

Name Weighting Due
Class Test 10% Week 6
Group project - written 25% 31/10/2014
Group Project - presentation 5% 13/11/2014
Individual reflection 10% 7/11/2014
Final examination 50% Exam period

Class Test

Due: Week 6
Weighting: 10%

A short class test will be held in week 6, after the presentation of the basic material. It will give you a good indication of how well prepared you are for the rest of the unit. Should you be sick or miss the test (with appropriate documentation), the percentage allocated to the test will be moved to the final exam (so your exam would then be worth 60%).


On successful completion you will be able to:
  • • Discuss the role of independent (predictor) and dependent (response) variables and distinguish between categorical and quantitative variables.
  • • Apply and interpret the statistical methods covered in this unit to analyse data and provide answers to a research question.
  • • Understand the assumptions underlying these statistical methods and decide whether they are reasonable in any particular case.
  • • Use standard statistics packages to carry out these analyses.
  • • Communicate clearly the results from these statistical analyses and relate these results to the original research question.

Group project - written

Due: 31/10/2014
Weighting: 25%

The project is an essential component of this unit. Since it represents a substantial piece of work, we will provide several opportunities for you to get feedback on it. First, you need to get your lecturer’s approval of your project proposal before you start collecting any data. Second, you can ask questions about your project from your tutor or lecturer at any time during the process. Third, you can ask your tutor or lecturer for comments on a draft of your project during the week before it is due. Finally, if your submitted project is not quite satisfactory, you will have an opportunity revise it. You will be asked to sign (or modify) a statement saying that all members of the group have contributed equally.


On successful completion you will be able to:
  • • Discuss the role of independent (predictor) and dependent (response) variables and distinguish between categorical and quantitative variables.
  • • Formulate a research question and select an appropriate statistical model for investigating it (from the range of models studied in this unit).
  • • Understand and appreciate the ethical aspects of carrying out statistical research and analysis.
  • • Plan and organise the collection of appropriate data, and manage these data electronically.
  • • Apply and interpret the statistical methods covered in this unit to analyse data and provide answers to a research question.
  • • Understand the assumptions underlying these statistical methods and decide whether they are reasonable in any particular case.
  • • Use standard statistics packages to carry out these analyses.
  • • Communicate clearly the results from these statistical analyses and relate these results to the original research question.

Group Project - presentation

Due: 13/11/2014
Weighting: 5%

Each group will be asked to make a short presentation of their project during the last week of classes. You will get more details about this later (the time allocation depends on the number of projects). The same mark will be allocated for all group members, as long as they have participated (approximately) equally in the presentation. Externally enrolled students can make their presentation using an electronic format (I’ll suggest some options).


On successful completion you will be able to:
  • • Plan and organise the collection of appropriate data, and manage these data electronically.
  • • Communicate clearly the results from these statistical analyses and relate these results to the original research question.

Individual reflection

Due: 7/11/2014
Weighting: 10%

Each student will be asked to submit an individual analysis and reflection on their project and on the process of carrying it out. This will give you an opportunity to discuss any problems you may have had, and to highlight any particularly successful features. You will also be asked about your specific contribution to the project. A short form will be available for this analysis and reflection.


On successful completion you will be able to:
  • • Understand and appreciate the ethical aspects of carrying out statistical research and analysis.
  • • Plan and organise the collection of appropriate data, and manage these data electronically.
  • • Communicate clearly the results from these statistical analyses and relate these results to the original research question.

Final examination

Due: Exam period
Weighting: 50%

The final 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. You must perform satisfactorily in the final examination in order to pass the unit regardless of your performance throughout the semester. If you fail the final examination, your overall result will be the minimum of your coursework and final exam mark (%).


On successful completion you will be able to:
  • • Discuss the role of independent (predictor) and dependent (response) variables and distinguish between categorical and quantitative variables.
  • • Formulate a research question and select an appropriate statistical model for investigating it (from the range of models studied in this unit).
  • • Apply and interpret the statistical methods covered in this unit to analyse data and provide answers to a research question.
  • • Understand the assumptions underlying these statistical methods and decide whether they are reasonable in any particular case.
  • • Use standard statistics packages to carry out these analyses.
  • • Communicate clearly the results from these statistical analyses and relate these results to the original research question.

Delivery and Resources

We will be using material from several online textbooks:

SurfStat at http://surfstat.anu.edu.au/surfstat-home/surfstat.html is a complete introductory statistics course, with a useful section on Statistical Inference with a sub-section on correlation and regression (but no ANOVA). HyperStat Online at http://davidmlane.com/hyperstat/index.html is at an intermediate level, chapter 12 and first part of 13, and chapter 15 cover the material (with background in chapter 1). Chapter 5 contains the best online table of the normal distribution (see http://davidmlane.com/hyperstat/z_table.html - try it!) StatSoft Electronic Textbook at http://www.statsoft.com/textbook/stathome.html is more advanced, and material is covered in sections called ANOVA/MANOVA and Linear Regression (with Elementary Concepts and Basic Statistics for background).

You may prefer a physical textbook: the material is covered in chapters 10–13 of D. Moore, G. McCabe & B. Craig (2012), Introduction to the Practice of Statistics, 7th Edition, W.H. Freeman. This book (and many others) provides lots of good explanations and examples, and includes a useful summary of earlier material required by this course. I prefer not to make its use compulsory, but it is available from the Co-op Bookshop (or elsewhere), and any previous edition (by these three authors or by Moore & McCabe) is equally useful.

Although the course will not focus on calculations, you will need to have (and be able to use) a calculator with statistical mode for the final examination (the one you used for your first-year unit in statistics will be fine).

The statistical software Minitab version 17 will be the main package used, and is installed in the computer labs used for classes. In addition, we will be using a software package called Arc.

As a Macquarie student, you can obtain a copy of Minitab for home use, downloaded from the student portal from the tab labelled ‘software downloads’. This location gives details of how to download and install the package onto your machine. Arc is also available as a free download from http://www.stat.umn.edu/arc/ (I’ll give more details when we start to use it). We will also occasionally use other (free) software packages, to illustrate particular points in lectures.

Unit Schedule

Stat270/Stae270 Applied Statistics

Unit Schedule Semester 2, 2014

 

 

Date

(Thur)

Wk

Lecture (E6A 108)

Tut/Lab (E6A 108 )

7 Aug

 

 

1

Introduction – types of variables, relationships, planning and running a statistical investigation

(collect data by questionnaire – in first lecture – to use during future lectures)

14 Aug

 

 

2

Summary: one-way anova, types of hypotheses, assumptions, graphics

Tut: research question, planning the study – variables and subjects

21 Aug

 

 

3

Summary: simple linear regression, transforming variables, assumptions, graphics

Lab: graphics and analysis of given data using computer package Minitab

28 Aug

 

 

4

Summary: multiple linear regression, indicator variables

Tut: questions using one-way anova and SLR

4 Sept

 

 

5

Summary: two-way anova

Lab: graphics and analysis of given data using computer package Arc

11 Sept

 

 

6

In-class test (10%) – first 30 mins

Ethical aspects, project preparation

Tut: questions using two-way anova and MLR

Project sign-off by end of week

18 Sept

 

 

7

Data collection, cleaning and management

Lab: data management from Excel to Minitab and Arc

 

25 Sept

 

 

Mid-semester break

 

 

 

2 Oct

 

 

Mid-semester break

 

 

 

9 Oct

 

 

8

Graphical tools in modelling

Lab: summarising data graphically, graphical justification of assumptions

16 Oct

 

 

9

Statistical evidence – hypotheses

Tut: hypothesis tests in modelling

First draft of project for comment

23 Oct

 

 

10

Communicating results – reports

Exercise: summary report

 

30 Oct

 

 

11

Statistical power

Tut/Lab: (work on own project, tutor available)

Project due Friday (25%)

6 Nov

 

 

12

Anova-regression connection

Lab: power and sample size

 

Individual analysis due Friday (10%)

13 Nov

 

 

13

Project presentations (5%)

 

Marked projects returned

Tut: previous exam questions

 

(Final exam 50% - during exam period)

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

  • • Understand and appreciate the ethical aspects of carrying out statistical research and analysis.
  • • Plan and organise the collection of appropriate data, and manage these data electronically.
  • • Understand the assumptions underlying these statistical methods and decide whether they are reasonable in any particular case.
  • • Use standard statistics packages to carry out these analyses.
  • • Communicate clearly the results from these statistical analyses and relate these results to the original research question.

Assessment tasks

  • Group project - written
  • Group Project - presentation
  • Individual reflection
  • Final examination

Commitment to Continuous Learning

Our graduates will have enquiring minds and a literate curiosity which will lead them to pursue knowledge for its own sake. They will continue to pursue learning in their careers and as they participate in the world. They will be capable of reflecting on their experiences and relationships with others and the environment, learning from them, and growing - personally, professionally and socially.

This graduate capability is supported by:

Learning outcomes

  • • Discuss the role of independent (predictor) and dependent (response) variables and distinguish between categorical and quantitative variables.
  • • Apply and interpret the statistical methods covered in this unit to analyse data and provide answers to a research question.
  • • Communicate clearly the results from these statistical analyses and relate these results to the original research question.

Assessment tasks

  • Group project - written
  • Group Project - presentation
  • Individual reflection

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

  • • Discuss the role of independent (predictor) and dependent (response) variables and distinguish between categorical and quantitative variables.
  • • Formulate a research question and select an appropriate statistical model for investigating it (from the range of models studied in this unit).
  • • Plan and organise the collection of appropriate data, and manage these data electronically.
  • • Apply and interpret the statistical methods covered in this unit to analyse data and provide answers to a research question.
  • • Understand the assumptions underlying these statistical methods and decide whether they are reasonable in any particular case.
  • • Use standard statistics packages to carry out these analyses.

Assessment tasks

  • Class Test
  • Group project - written
  • Final 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

  • • Discuss the role of independent (predictor) and dependent (response) variables and distinguish between categorical and quantitative variables.
  • • Formulate a research question and select an appropriate statistical model for investigating it (from the range of models studied in this unit).
  • • Apply and interpret the statistical methods covered in this unit to analyse data and provide answers to a research question.
  • • Understand the assumptions underlying these statistical methods and decide whether they are reasonable in any particular case.
  • • Use standard statistics packages to carry out these analyses.

Assessment tasks

  • Class Test
  • Group project - written
  • Final 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

  • • Discuss the role of independent (predictor) and dependent (response) variables and distinguish between categorical and quantitative variables.
  • • Formulate a research question and select an appropriate statistical model for investigating it (from the range of models studied in this unit).
  • • Plan and organise the collection of appropriate data, and manage these data electronically.
  • • Apply and interpret the statistical methods covered in this unit to analyse data and provide answers to a research question.
  • • Understand the assumptions underlying these statistical methods and decide whether they are reasonable in any particular case.
  • • Use standard statistics packages to carry out these analyses.
  • • Communicate clearly the results from these statistical analyses and relate these results to the original research question.

Assessment tasks

  • Class Test
  • Group project - written
  • Final 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

  • • Discuss the role of independent (predictor) and dependent (response) variables and distinguish between categorical and quantitative variables.
  • • Formulate a research question and select an appropriate statistical model for investigating it (from the range of models studied in this unit).
  • • Apply and interpret the statistical methods covered in this unit to analyse data and provide answers to a research question.
  • • Communicate clearly the results from these statistical analyses and relate these results to the original research question.

Assessment tasks

  • Class Test
  • Group project - written
  • Group Project - presentation
  • Individual reflection
  • Final 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

  • • Understand and appreciate the ethical aspects of carrying out statistical research and analysis.
  • • Plan and organise the collection of appropriate data, and manage these data electronically.
  • • Communicate clearly the results from these statistical analyses and relate these results to the original research question.

Assessment tasks

  • Class Test
  • Group project - written
  • Group Project - presentation
  • Individual reflection
  • Final 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 outcome

  • • Understand and appreciate the ethical aspects of carrying out statistical research and analysis.

Assessment tasks

  • Group project - written
  • Group Project - presentation
  • Individual reflection

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 outcome

  • • Understand and appreciate the ethical aspects of carrying out statistical research and analysis.

Assessment tasks

  • Group project - written
  • Group Project - presentation
  • Individual reflection