STATS 4013 - Statistics Topic A - Honours
North Terrace Campus - Semester 1 - 2021
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General Course Information
Course Details
Course Code STATS 4013 Course Statistics Topic A - Honours Coordinating Unit Mathematical Sciences Term Semester 1 Level Undergraduate Location/s North Terrace Campus Units 3 Available for Study Abroad and Exchange Restrictions Honours students only Assessment Ongoing assessment, exam Course Staff
Course Coordinator: Dr Jono Tuke
Course Timetable
The full timetable of all activities for this course can be accessed from .
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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
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Learning Resources
Required Resources
There are no required resources for this course.Recommended Resources
Time Series
- Hyndman: Forecasting.
- Cryer: Time series analysis.
- Cressie: Statistics for spatio-temporal data.
- Wikle: Spatio-temporal statistics with R.
- https://rstudio.github.io/leaflet/
- https://r-spatial.github.io/sf/
- Pinheiro: Mixed-effect models in S and S-plus.
- Zuur: Mixed effects models and extension in ecology with R.
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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
- Workshop 1 - introduction.
- Workshop 2 - organising a course, how to write a lecture.
- Workshop 3 - how to write a practical, how to record a topic video.
- Workshop 4 - how to write an assignment.
- Consulting session 1.
- Consulting session 2.
- Practical 1 - time series.
- Practical 2 - spatial statistics.
- Practical 3 - mixed effects.
- Consulting session 3.
- Consulting session 4.
- Consulting session 5.
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Assessment
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- 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) Grade Grade reflects following criteria for allocation of grade Reported 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 .
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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.
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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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Fraud Awareness
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