STATS 7004 - Statistics Topic A

North Terrace Campus - Semester 1 - 2020

Please contact the School of Mathematical Sciences for further details, or view course information on the School of Mathematical Sciences web site at

  • General Course Information
    Course Details
    Course Code STATS 7004
    Course Statistics Topic A
    Coordinating Unit Mathematical Sciences
    Term Semester 1
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Available for Study Abroad and Exchange Y
    Assessment Ongoing assessment, exam
    Course Staff

    Course Coordinator: Andrew Metcalfe

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    In 2019 the topic of this course is DESIGN OF EXPERIMENTS.


    From a statistical perspective there are two types of research studies: observational studies and designed experiments. In a designed experiment the researcher changes the values of inputs to a system and monitors the effect on the outputs from that system. The objective is to understand and improve the system. However, all systems are subject to some random variation, and replicates will not be identical. We need to allow for this random variation in the analysis. The definition of an experimental design is: the specification of the conditions at which experimental data will be observed. The purpose of designing an experiment is to ensure that you will be able to answer the questions posed at the outset of the investigation and to make the most efficient use of resources.

    The assumed knowledge for the course is an introductory statistics course that has covered: probability; descriptive statistics; elementary probability distributions; the sampling distribution of the mean; and preferably something on confidence intervals and regression on a single predictor variable. Notes covering this material can be obtained from the course coordinator.

    The course will cover applications in various disciplines including: agriculture; engineering; management; and medicine.

    Learning outcomes

    On successful completion of this course students will be able to:
    1. understand the need for randomization and replication in experiments;
    2. understand methods for reducing variability in experiments including blocking;
    3. identify possible confounding factors when designing an experiment and allow for these;
    4. advise on a suitable sample size for experiments to avoid wasting resources through an experiment that is too small to demonstrate a
    worthwhile effect or excessively large for demonstrating a worthwhile effect;
    5. design an experiment for a client;
    6. analyze the results of the experiment using the software R;
    7. write a succinct non-technical report of the experiment for a client.
    University Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    University Graduate Attribute Course Learning Outcome(s)
    Deep discipline knowledge
    • informed and infused by cutting edge research, scaffolded throughout their program of studies
    • acquired from personal interaction with research active educators, from year 1
    • accredited or validated against national or international standards (for relevant programs)
    Critical thinking and problem solving
    • steeped in research methods and rigor
    • based on empirical evidence and the scientific approach to knowledge development
    • demonstrated through appropriate and relevant assessment
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    Self-awareness and emotional intelligence
    • a capacity for self-reflection and a willingness to engage in self-appraisal
    • open to objective and constructive feedback from supervisors and peers
    • able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
  • Learning Resources
    Required Resources
    Recommended Resources
    Introduction to the Desin and Analysis of Experiments, GM Clarke & RE Kempson, Arnold, 1997
    The R Book (2e), MJ Crawley, Wiley, 2012
    Data Analysis and Graphics Using R (3e), J Maindonald & WJ Braun Cambridge, 2010
    Design and Analysis of Experiments, DC Montgomery Wiley, 2009
    Online Learning
    Electronic resources, including lecture notes and assignments, will be posted on MyUni. You will also be encouraged to use discussion boards.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    Notes will be provided before the material is taught through lecture classes. The class size is typically small and you will be encouraged to ask questions and contribute to the discussion. You will be asked to present a case of the design and analysis of an experiment as a small group exercise. There will also be a debate if this is feasible with the number of participants.

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    Activity Quantity Hours
    Lectures 24 96
    Assignments 5 50
    Presentation 1 10
    Total 156
    Learning Activities Summary
    1. Comparison against a standard and sample size considerations and calculations
    2. Comparing two treatments - paired and independent samples
    3. Comparison of proportions
    4. Comparison of several means - completely randomised designs and randomised block designs, and multiple comparisons
    5. Fixed and random effects
    6. Latin squares, Graeco-Latin squares
    7. Incomplete block designs
    8. Two factors at several levels
    9. Two level factorial experiments
    10. Central composte designs - response surfaces and concamitant variables
    11. Hill climbing experiments
    12. Robust design
    13. Crossed and nested factors and split plot designs
    14. General linear mixed effects model
    15. Mixture designs
    16. Optimal experimental design
    + if time, some of: lattice squares, cyclic designs, cross-over designs
  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Due to the current COVID-19 situation modified arrangements have been made to assessments to facilitate remote learning and teaching. Assessment details provided here reflect recent updates.

    Component Weighting Outcomes assessed
    Assignment (x6) 30% All
    Exam 70% All
    Assessment Related Requirements
    A final aggregate score of at least 50% is required to pass the course.
    Assessment Detail
    Item Set Due Weighting
    Assignment 1 week 1 week 3 5%
    Assignment 2 week 3 week 5 5%
    Assignment 3 week 5 week 7 5%
    Assignment 4 week 7 week 9 5%
    Assignment 5 week 9 week 11 5%
    Presentation week 3 week 12 5%
    Assignments are to be submitted with a signed cover sheet attached. Assignments will be marked and returned within two weeks of submission.
    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M10 (Coursework Mark Scheme)
    Grade Mark Description
    FNS   Fail No Submission
    F 1-49 Fail
    P 50-64 Pass
    C 65-74 Credit
    D 75-84 Distinction
    HD 85-100 High Distinction
    CN   Continuing
    NFE   No Formal Examination
    RP   Result Pending

    Further details of the grades/results can be obtained from Examinations.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

    Final results for this course will be made available through Access Adelaide.

  • Student Feedback

    The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy ( course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

  • Student Support
  • Policies & Guidelines
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