STATS 4013 - Statistics Topic A - Honours
North Terrace Campus - Semester 1 - 2022
General Course Information
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 This course is available for students taking an honours degree in Mathematical Sciences. The course will cover an advanced topic in statistics. For details of the topic offered this year please refer to the Course Outline.
Course Coordinator: Dr Melissa Humphries
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesIn 2022 the topic of this course is Time Series
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.
The third year course Statistical Modelling III, or equivalent. Students should also be familiar with R, RStudio and RMarkdown.
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.
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.
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.
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.
Required ResourcesReading each week will come from https://online.stat.psu.edu/stat510/ This will be supported with additional resources provided on MyUni.
Recommended ResourcesThere 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 LearningElectronic 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 ModesEach 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.
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 SummaryTopics 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
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.
Component Weighting Outcomes assessed In Class assessment 20% All Literature Review
10% All Methods section 10% All Prac Test 30% Final Project 30% All
Assessment Related RequirementsA mark of 50% is required to pass this course.
Set (Week) Due (Week) Weight (%) In-class Assessment 2 6 20 Literature Review
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.
SubmissionAssignments are to be submitted on MyUni and will be run through a plagiarism checker.
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 Access Adelaide.
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.
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