STATS 4013 - Statistics Topic A - Honours

North Terrace Campus - Semester 1 - 2021

Please contact the School of Mathematical Sciences for further details, or view course information on the School of Mathematical Sciences web site at http://www.maths.adelaide.edu.au

  • General Course Information
    Course Details
    Course Code STATS 4013
    Course Statistics Topic A - Honours
    Coordinating Unit School of Mathematical Sciences
    Term Semester 1
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Available for Study Abroad and Exchange
    Restrictions Honours students only
    Course Description Please contact the School of Mathematical Sciences for further details, or view course information on the School of Mathematical Sciences web site at http://www.maths.adelaide.edu.au
    Course Staff

    Course Coordinator: Dr Jono Tuke

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    In 2021 the topic of this course is Dealing with dependency in time and space

    Overview

    Three common ways to deal with dependency are

    • Time series,
    • Mixed effects models, and
    • Spatial statistics.

    In this course you will learn the basics of each of these methods. For each method, we will see the general models of the method, look at how to perform exploratory data analysis, and finally learn how to fit the standard models of the method in R. 

    Prerequisites

    The third year course Statistical Modelling III, or equivalent. Students should also be familiar with R, RStudio, and tidyverse. 

    Learning Outcomes

    1. Produce teaching materials to explain the fundemental models of each method
    2. Produce topic videos on the methods. 
    3. Produce and supervise a practical on one of the methods. 
    4. Complete a full analysis of real data using one of the methods. 


    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)
    All
    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
    All
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    All
    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
    All
  • Learning Resources
    Required Resources
    There are no required resources for this course.
    Recommended Resources
    Time Series

    Spatial statistics

    • Cressie: Statistics for spatio-temporal data.
    • Wikle: Spatio-temporal statistics with R.
    • https://rstudio.github.io/leaflet/
    • https://r-spatial.github.io/sf/
    Mixed effects modelling

    Textbooks will be supplied by Jono. 
    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.
    Workload

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

    Activity Quantity Hours
    Workshops 12 36
    Assignments 1 10
    Project 1 30
    Development of material 1 80
    Total 156
    Learning Activities Summary
    1. Workshop 1 - introduction.
    2. Workshop 2 - organising a course, how to write a lecture. 
    3. Workshop 3 - how to write a practical, how to record a topic video.
    4. Workshop 4 - how to write an assignment. 
    5. Consulting session 1.
    6. Consulting session 2.
    7. Practical 1 - time series. 
    8. Practical 2 - spatial statistics.
    9. Practical 3 - mixed effects. 
    10. Consulting session 3. 
    11. Consulting session 4.
    12. Consulting session 5.


  • 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
    Component Weighting Outcomes assessed
    Assignments 30% All
    Peer assessment 10% All
    Assessment of material 30% All
    Project 30% All


    Assessment Related Requirements
    A mark of 50% is required to pass this course.
    Assessment Detail
    Item Set Due Weighting
    Assignment Week 9 Week 12 30%
    Project Week 1 Week 13 30%
    Submission
    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:

    M11 (Honours Mark Scheme)
    GradeGrade reflects following criteria for allocation of gradeReported on Official Transcript
    Fail A mark between 1-49 F
    Third Class A mark between 50-59 3
    Second Class Div B A mark between 60-69 2B
    Second Class Div A A mark between 70-79 2A
    First Class A mark between 80-100 1
    Result Pending An interim result RP
    Continuing Continuing CN

    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 (http://www.adelaide.edu.au/policies/101/) 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
  • Fraud Awareness

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