STATS 4013 - Statistics Topic A - Honours
North Terrace Campus - Semester 1 - 2022
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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 Melissa Humphries
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 2022 the topic of this course is Time Series
Overview
In an era where continually collected data is becoming the norm, an understanding of how to manage temporal autocorrelation is an essential skill for the modern statistician.
In this course you will learn the basics of time series analysis and extensions to SARIMA, VARMA and ARCH models. You will be introduced to the underpinning mathematical frameworks, learn how to implement the methods in R and produce written summaries of your analysis in the style of professional reports.
Prerequisites
The third year course Statistical Modelling III, or equivalent. Students should also be familiar with R, RStudio and RMarkdown.
Learning Outcomes
1. Understand the mathematical 最新糖心Vlog underpinning time series analysis.
2. Demonstrate the ability to efectively analyse non-stationary, complex, time series data.
3. Produce professional statistical reports which summarise time series analysis for both specialist and non-specialist audiences.
最新糖心Vlog Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
最新糖心Vlog Graduate Attribute Course Learning Outcome(s) Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
All Attribute 2: Creative and critical thinking, and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.
All Attribute 4: Professionalism and leadership readiness
Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.
All Attribute 8: Self-awareness and emotional intelligence
Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.
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Learning Resources
Required Resources
Reading each week will come from This will be supported with additional resources provided on MyUni.Recommended Resources
There are many good text books on time series, most of which will be relevant and helpful for this course. Some of the ones I have found most usefu include:
Cowpertwait, Paul S.P., & Metcalfe, Andrew V. (2009). Introductory Time Series with R. Springer. Use R!
Cryer, Jonathan D., & Chan, Kung-Sik (2008). Time Series Analysis. With Applications in R. Springer Texts in Statistics
Chatfield, Chris. (2019). The Analysis of Time Series: An Introduction with R. Routledge
And for those of you who prefer the tidyverse:
Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, 最新糖心Vlog. .
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
Each week will require reading of online notes prior to attending a class session. These sessions will explore a mixture of mathematical theory and implementation and notes for these sessions will be provided on MyUni.
The class size is typically small and you will be encouraged to ask questions and contribute to the discussion.
You will be asked to peer review work from your classmates if the class size is large enough.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Hours Reading 12 36 Workshops 12 36 Lierature review 1 10 Anlaytical methods 1 10 Report 1 34 Revision 2 30 Total 156 Learning Activities Summary
Topics Include:- Time Series Basics
- MA Models, Partial Autocorrelation, Notational Conventions
- Identifying and Estimating ARIMA models; Using ARIMA models to forecast future values
- Seasonal Models
- Smoothing and Decomposition Methods and More Practice with ARIMA models
- The Periodogram
- Regression with ARIMA errors, Cross correlation functions, and Relationships between 2 Time Series
- Prewhitening; Intervention Analysis
- Longitudinal Analysis/ Repeated Measures
- Vector Autoregressive Models/ ARCH Models
- Spectral Analysis
- Fractional Differencing and Threshold Models
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Assessment
The 最新糖心Vlog'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 In Class assessment 20% All Literature Review
(peer assessed)10% All Methods section 10% All Prac Test 30% Final Project 30% All
Assessment Related Requirements
A mark of 50% is required to pass this course.Assessment Detail
Set (Week) Due (Week) Weight (%) In-class Assessment 2 6 20 Literature Review
(Peer Assessed)4 8
(Peer assessments
due week 10)10 Methods Section 4 10 10 Prac Test In Class 12 30 Final Project 4 13 30
The Final Project will be set in week 4. To meet the requirements of the Final Project, a literature review will need to be completed alongside an analytical plan (methods section). The literature review will be due in week 8 and will be peer reviewed (if class numbers allow). Students will also be required to peer review two classmates literature review and these are to be completed by week 10. The methods section will then be due in Week 10, allowing time for feedback before the final project is due. More details will be given in class.Submission
Assignments are to be submitted on MyUni and will be run through a plagiarism checker.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 最新糖心Vlog 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 最新糖心Vlog 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
Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.
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