STATS 4008 - Statistics Topic D - Honours
North Terrace Campus - Semester 2 - 2024
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General Course Information
Course Details
Course Code STATS 4008 Course Statistics Topic D - Honours Coordinating Unit Mathematical Sciences Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Available for Study Abroad and Exchange Y Restrictions Honours students only Assessment Ongoing assessment, exam Course Staff
Course Coordinator: David Shorten
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 2024 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 or Quarto.
Learning Outcomes
1. Understand the mathematical structures 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.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) 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 3: Teamwork and communication skills
Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.
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 5: Intercultural and ethical competency
Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.
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, Australia. otexts.com/fpp3.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.
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 156Learning 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 -
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 In-class assessment 20 All Literature Review (peer assessed) 10 All Data and Methods section 10 All Prac Test 30 All Final Project 30 All
Assessment Related Requirements
An aggregate score of at least 50% is required to pass the course.Assessment Detail
The main assessments will be the practical test and the research project (split into components such as the literature review, methods and final report).
The practical test will be performed in class. Students will be required to analyse a dataset during the test and submit a short report.
The research project will focus on a specific time series dataset. A number of specific research questions about the dataset will have to be addressed. This will culminate in a detailed final report on all work conducted.Submission
Submissions will be via MyUni.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
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