• Class Number 4160
• Term Code 3430
• Class Info
• Unit Value 6 units
• Mode of Delivery In Person
• COURSE CONVENER
• Dr Luca Maestrini
• LECTURER
• Dr Luca Maestrini
• Class Dates
• Class Start Date 19/02/2024
• Class End Date 24/05/2024
• Census Date 05/04/2024
• Last Date to Enrol 26/02/2024
SELT Survey Results

Graphical Data Analysis (STAT4026)

This course introduces the principles of data representation, summarisation and presentation with particular emphasis on the use of graphics. The course will use the R Language in a modern computing environment. Topics to be discussed include:

·        Data representation; examples of good and bad graphics; principles of graphic construction; some pitfalls to be avoided; presentation graphics.

·        Graphics environments; interactive graphics; windows; linked windows; graphics objects.

·        Statistical graphics; stem and leaf plots, box plots, histograms; smoothing histograms; quantile-quantile plots; representing multivariate data; scatterplots; clustering; stars and faces; dynamic graphics including data rotation and brushing.

·        Relationships between variables; smoothing scatterplots; simple regression; modelling and diagnostic plots; exploring surfaces; contour plots and perspective plots; multiple regression; relationships in time and space; time series modelling and diagnostic plots.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

1. Demonstrate detailed knowledge of the R statistical computing language, particularly graphical capabilities.
2. Explain in detail and be able to apply the principles of good data representation.
3. Explain in detail and be able to use various graphics environments, interactive graphics and graphics objects.
4. Construct graphical representations of one dimensional data.
5. Construct graphical representations for multivariate data including scatterplots, and dynamic graphics.
6. Use diagnostic plots when conducting statistical modelling to explore and refine statistical models for data, including detailed explanations of such use.
7. Construct and interpret in detail graphical displays for dependent data.

Research-Led Teaching

Statistics is a discipline that informs many other disciplines - basically, any discipline that generates or uses data (of almost any kind) can benefit from Statistics. This makes Statistics very naturally a companion to research-led teaching, and data visualisation is a key element of the way in which Statistics can allow researchers to see structure in high-dimensional data. In this course, we will look at a number of real data sets that address real research problems, and we will see (literally) how statistics can lead to a deeper understanding of the structures underlying the data.

Examination Material or equipment

There is no final exam for this course.

Required Resources

Lecture notes, tutorials, computer code, assignments, datasets and other relevant materials (available on Wattle)

Class materials, including lecture notes, tutorials, computer code, datasets, assignments and other relevant materials, will be made available on the class web page hosted on Wattle at http://wattle.anu.edu.au. To log on to Wattle, you need to have an ANU ID (your student number) and a password (the same as for obtaining your e-mail). In order to access the class web page within Wattle, you will need to be formally enrolled in the course or you will need to have arranged access with me (e.g. if you are an Honours student). The class web page will be updated with new information on a regular basis, and will also contain links to other places of interest (such as an R workshop initially). It is essential that you visit the class web page regularly.

The course makes extensive use of the free R software and its RStudio interface for statistical computing. PC/Mac labs are located at many places around campus: for an exhaustive list, visit https://services.anu.edu.au/information-technology/software-systems

A number of data sets will be analysed during lectures, live using R as much as possible. To assist you in understanding the data analyses, the R code used to produce displays discussed in class will be made available to you on the class web page. You are free to use and modify this code in conducting your own analyses.

Learning R can be a daunting task, but there are numerous easy-to-read guides to R on the web. Also, you can use own-paced, interactive lessons within R using the swirl package (http://www.swirlstats.com).

There are a variety of online platforms that can be used to participate in your study program. These could include videos for lectures and other instruction, two-way video conferencing for interactive learning, email and other messaging tools for communication, interactive web apps for formative and collaborative activities, print and/or photo/scan for handwritten work and drawings, and home-based assessment.

ANU outlines recommended student system requirements to ensure you are able to participate fully in your learning. Other information is also available about the various Learning Platforms you may use.

Staff Feedback

1. Students will be given feedback in the following forms in this course:
• Individual feedback on assignments will be posted to Wattle within the Gradebook.
• Only you will be able to see feedback on your assignment.
• Summary feedback reflecting patterns in data analyses will be posted to Wattle. This
• feedback will not identify individuals, but will rather be broad in scope and describe general patterns of response to the analyses of the data.

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). Feedback can also be provided to Course Conveners and teachers via the Student Experience of Learning & Teaching (SELT) feedback program. SELT surveys are confidential and also provide the Colleges and ANU Executive with opportunities to recognise excellent teaching, and opportunities for improvement.

Class Schedule

Week/Session Summary of Activities Assessment
1 Introduction and getting to know R  Chapter 1, R Workshop, Tutorial 1; note that assessment items are available on Wattle at the start of the course
2 R, Graphics in R  Chapter 1, R Quiz (Wattle) available
3 Representing and comparing distributions  Chapter 2, Tutorial 2
4 Representing and comparing distributions  Chapter 2, Tutorial 3
5 Relationships between 2 variables  Chapter 3, ASSESSMENT: Assignment 1 due
6 Relationships between 2 variables  Chapter 3, Tutorial 4
7 Relationships between 3 and more variables  Chapter 4
8 Relationships between 3 and more variables  Chapter 4, Tutorial 5
9 Relationships between 3 and more variables  Chapter 4
10 Relationships between 3 and more variables/Time dependent data  Chapter 4, Chapter 5, Tutorial 6
11 Time Series and Dependent Data Chapter 5, Tutorial 7, ASSESSMENT: Assignment 2 due
12 Graphical Construction Chapter 6, Tutorial 8, ASSESSMENT: Project due on the day before the beginning of the exam period

Tutorial Registration

There are no separate tutorials. Some tutorial / computer lab demonstrations will be run during the lecture time.

Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
R Quiz 0 % 04/03/2024 04/03/2024 1
Assignment 1 20 % 22/03/2024 28/03/2024 1, 2, 3, 4
Assignment 2 20 % 17/05/2024 24/05/2024 1, 2, 3, 6, 7
Project 60 % 30/05/2024 27/06/2024 1, 2, 3, 4, 5, 6

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details

Policies

ANU has educational policies, procedures and guidelines , which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Integrity Rule before the commencement of their course. Other key policies and guidelines include:

Assessment Requirements

The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the Academic Skills website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.

Participation

In-person attendance is encouraged. In-person classes (lectures, including tutorials/demonstrations using R) will be held as timetabled and recorded. All course materials, including recordings, will be posted on the main Wattle page and Echo360.

Examination(s)

There is no final exam for this course.

Value: 0 %
Due Date: 04/03/2024
Return of Assessment: 04/03/2024
Learning Outcomes: 1

R Quiz

Learning R is critical for this course. The R Quiz is a brief online Quiz on Wattle that will allow you to check on how much you have learned about R as we begin the course after the introductory R material is introduced in the course.

The R Quiz will be available on Wattle at the start of the course, carries no formal assessment weight, and can be attempted multiple times.

Value: 20 %
Due Date: 22/03/2024
Return of Assessment: 28/03/2024
Learning Outcomes: 1, 2, 3, 4

Assignment 1

Analyse data given and present analysis using R software. The answers should be written in report style in clear language. Hand in both text and graphics as part of your answer. The graphics you hand in should be RELEVANT: that is DO NOT HAND IN EVERY GRAPHIC YOU PRODUCE IN THE PROCESS OF WORKING THROUGH THE ASSIGNMENT. Marks will be deducted if you hand in irrelevant graphics, and if your graphics are not sufficiently adorned with explanatory titles and axis labels and so on. The text part of your answer should be in the form of a report: it is not sufficient to merely annotate the graphics you produce. The text part of your report must be CONCISE and TO THE POINT: answers that are too lengthy may also be penalised. Assignment 1 concerns the topic of representing and comparing distributions. Assignment 1 has a page limit of 4 (FOUR) pages (single-sided). Submissions that exceed the page limit will only have material up to the page limit assessed (i.e. additional material will be disregarded).

The task sheet for this assignment and more details about what is expected will be posted on Wattle.

Value: 20 %
Due Date: 17/05/2024
Return of Assessment: 24/05/2024
Learning Outcomes: 1, 2, 3, 6, 7

Assignment 2

Analyse data given and present analysis using R software. The answers should be written in report style in clear language. Hand in both text and graphics as part of your answer. The graphics you hand in should be RELEVANT: that is DO NOT HAND IN EVERY GRAPHIC YOU PRODUCE IN THE PROCESS OF WORKING THROUGH THE ASSIGNMENT. Marks will be deducted if you hand in irrelevant graphics, and if your graphics are not sufficiently adorned with explanatory titles and axis labels and so on. The text part of your answer should be in the form of a report: it is not sufficient to merely annotate the graphics you produce. The text part of your report must be CONCISE and TO THE POINT: answers that are too lengthy may also be penalised. Assignment 2 concerns the topic of time series and dependent data. Assignment 2 has a page limit of 4 (FOUR) pages (single-sided). Submissions that exceed the page limit will only have material up to the page limit assessed (i.e. additional material will be disregarded).

The task sheet for this assignment and more details about what is expected will be posted on Wattle.

Value: 60 %
Due Date: 30/05/2024
Return of Assessment: 27/06/2024
Learning Outcomes: 1, 2, 3, 4, 5, 6

Project

The project is a vital part of the course. The purpose of the project is to (1) encourage you to be more aware and to examine graphics more critically; and (2) enable you to put the principles and methods of graphical data analysis to work on a substantial problem. The project is intended to be a piece of independent work that is carried out essentially without assistance. The project consists of two parts, both of which are compulsory.

1. Graphic Awareness. A collection of five statistical graphics with written comments on each graphic. These graphics should be collected during the semester from published work. You may not draw your own graphics or ask a friend (or anybody, for that matter) to do so for you. Credit will be given for interesting, carefully chosen graphics which show evidence of reasonable wide searching. In addition to including a copy of the graphic itself, you should document the source of the graphic (title of article, authors, source including title, page numbers, etc.) and discuss the graphic. Your discussion may include the reason for the graphic, strengths and weaknesses, etc, and may include redrawn, improved versions of the graphic. The discussion should be brief, relevant and insightful, not longwinded.
2. Graphical Analysis. A few weeks into the course, I will provide a short list of data sets and some documentation for them. You must choose one of these data sets, analyse it, and prepare for submission a concise, well-organised report on your analysis. Your analysis must be appropriate and it must be substantially (though not necessarily exclusively) graphical. Your report should begin with a clear statement of the problem you are addressing and the context in which it arises. You should describe what you have done and why. Relevant graphics and/or output should be included in the report, and all such results should be discussed and interpreted in the text. The entire report must be shorter than 8 pages including graphics and data (any page after the 8th will be ignored), and the written part should not be longer than 4 to 5 pages. Attempts to defy the spirit of the page limit by using unreadable typefaces and so on will be noticed, so please don't do it. Submissions that exceed the page limit will only have material up to the page limit assessed (i.e. additional material will be disregarded).

The task sheet for this project and more details about what is expected will be posted on Wattle.

Academic integrity is a core part of the ANU culture as a community of scholars. The University’s students are an integral part of that community. The academic integrity principle commits all students to engage in academic work in ways that are consistent with, and actively support, academic integrity, and to uphold this commitment by behaving honestly, responsibly and ethically, and with respect and fairness, in scholarly practice.

The University expects all staff and students to be familiar with the academic integrity principle, the Academic Integrity Rule 2021, the Policy: Student Academic Integrity and Procedure: Student Academic Integrity, and to uphold high standards of academic integrity to ensure the quality and value of our qualifications.

The Academic Integrity Rule 2021 is a legal document that the University uses to promote academic integrity, and manage breaches of the academic integrity principle. The Policy and Procedure support the Rule by outlining overarching principles, responsibilities and processes. The Academic Integrity Rule 2021 commences on 1 December 2021 and applies to courses commencing on or after that date, as well as to research conduct occurring on or after that date. Prior to this, the Academic Misconduct Rule 2015 applies.

The University commits to assisting all students to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. All coursework students must complete the online Academic Integrity Module (Epigeum), and Higher Degree Research (HDR) students are required to complete research integrity training. The Academic Integrity website provides information about services available to assist students with their assignments, examinations and other learning activities, as well as understanding and upholding academic integrity.

Online Submission

Materials will be submitted via Turnitin. You will be required to electronically sign a declaration when you submit your assessable work. Please retain a copy of your assignment for your records.

Hardcopy Submission

Online submission only.

Late Submission

Individual assessment tasks may or may not allow for late submission. Policy regarding late submission is detailed below:

• Late submission not permitted. If submission of assessment tasks without an extension after the due date is not permitted, a mark of 0 will be awarded.

Referencing Requirements

The Academic Skills website has information to assist you with your writing and assessments. The website includes information about Academic Integrity including referencing requirements for different disciplines. There is also information on Plagiarism and different ways to use source material.

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Not applicable

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.

Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

Convener

 Dr Luca Maestrini

luca.maestrini@anu.edu.au

Research Interests

Luca Maestrini is a Lecturer of Statistics. His research interests range from methodological and computational statistics to statistical theory, with a particular focus on variational approximations and generalised linear mixed models.

Dr Luca Maestrini

 Thursday 16:00 17:00 Thursday 16:00 17:00

Instructor

 Dr Luca Maestrini luca.maestrini@anu.edu.au

Dr Luca Maestrini

 Thursday 16:00 17:00 Thursday 16:00 17:00