- Class Number 8234
- Term Code 3060
- Class Info
- Unit Value 6 units
- Mode of Delivery In Person
- Prof Michael Martin
- Prof Michael Martin
- Class Dates
- Class Start Date 27/07/2020
- Class End Date 30/10/2020
- Census Date 31/08/2020
- Last Date to Enrol 03/08/2020
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.
Upon successful completion, students will have the knowledge and skills to:
- Demonstrate detailed knowledge of the R statistical computing language, particularly graphical capabilities;
- Explain in detail and be able to apply the principles of good data representation;
- Explain in detail and be able to use various graphics environments, interactive graphics and graphics objects;
- Construct graphical representations of one dimensional data;
- Construct graphical representations for multivariate data including scatterplots, and dynamic graphics;
- Use diagnostic plots when conducting statistical modelling to explore and refine statistical models for data, including detailed explanations of such use; and,
- Construct and interpret graphical displays for dependent data.
Statistics is a discipline 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
R (free to download)
The course makes extensive use of the free R software 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
Students should be able to use their student cards to access these computing laboratories and should have a computer account automatically set up for them upon registration for this course. If you have not registered for the course, your card will not allow you access to the lab. To get started with the computing requirements for the course, students should make sure that they read the document "Introductory R Worksheet 1: PC familiarity" (linked to from the R workshop page, a link to which appears on the class home page). This document describes how you can log in to the PC’s. This document will also tell you how you can obtain or view other documents that you will find useful to learn about the computing setup for the course.
After this initial handout, all handouts will be available only through the class web page at http://wattle.anu.edu.au
From the web page, you will be able to print out all of the lecture notes, all of the computer code used in the class, and all the tutorials and solutions. No handouts will be made available except on the class web page. 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.
Printed Lecture Notes (available on Wattle). Class materials, including detailed lecture notes, class lecture demonstrations, tutorials, 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 R statistical programming language. 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)
- 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.
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). The feedback given in these surveys is anonymous and provides the Colleges, University Education Committee and Academic Board with opportunities to recognise excellent teaching, and opportunities for improvement. The Surveys and Evaluation website provides more information on student surveys at ANU and reports on the feedback provided on ANU courses.
|Week/Session||Summary of Activities||Assessment|
|1||Introduction and getting to know R||Chapter 1, R Workshop, Tutorial 1|
|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|
|Assessment task||Value||Due Date||Return of assessment||Learning Outcomes|
|R Quiz||0 %||07/08/2020||07/08/2020||1|
|Assignment 1||20 %||27/08/2020||21/09/2020||1, 2|
|Assignment 2||20 %||21/10/2020||05/11/2020||1,5|
* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details
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 Misconduct Rule before the commencement of their course. Other key policies and guidelines include:
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 Integrity . 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.
There are no marks for "participation".
Assessment Task 1
Learning Outcomes: 1
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.
Assessment Task 2
Learning Outcomes: 1, 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 penalized. Assignment 1 concerns the topic of representing and comparing distributions. Assignment 1 has a page limit of 4 (FOUR) pages (single-sided).
The task sheet for this Project is available on Wattle at the start of the course under the Assignments section.
Assessment Task 3
Learning Outcomes: 1,5
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 penalized. Assignment 2 concerns the topic of time series and dependent data.
The task sheet for this Assignment is available on Wattle at the start of the course under the Assignments section.
Assessment Task 4
Learning Outcomes: 1,2,3,4,5,6
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.
- 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.
- 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-organized 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.
The task sheet for this Project is available on Wattle at the start of the course under the Assignments section.
Academic integrity is a core part of the ANU culture as a community of scholars. At its heart, academic integrity is about behaving ethically, committing to honest and responsible scholarly practice and upholding these values with respect and fairness.
The ANU commits to assisting all members of our community to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. The ANU expects staff and students to be familiar with the academic integrity principle and Academic Misconduct Rule, uphold high standards of academic integrity and act ethically and honestly, to ensure the quality and value of the qualification that you will graduate with.
The Academic Misconduct Rule is in place to promote academic integrity and manage academic misconduct. Very minor breaches of the academic integrity principle may result in a reduction of marks of up to 10% of the total marks available for the assessment. The ANU offers a number of online and in person services to assist students with their assignments, examinations, and other learning activities. Visit the Academic Skills website for more information about academic integrity, your responsibilities and for assistance with your assignments, writing skills and study.
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.
Online submission only
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.
Accepted academic practice for referencing sources that you use in presentations can be found via the links on the Wattle site, under the file named “ANU and College Policies, Program Information, Student Support Services and Assessment”. Alternatively, you can seek help through the Students Learning Development website.
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.
Resubmission of Assignments
Distribution of grades policy
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).
- ANU Health, safety & wellbeing for medical services, counselling, mental health and spiritual support
- ANU Diversity and inclusion for students with a disability or ongoing or chronic illness
- ANU Dean of Students for confidential, impartial advice and help to resolve problems between students and the academic or administrative areas of the University
- ANU Academic Skills and Learning Centre supports you make your own decisions about how you learn and manage your workload.
- ANU Counselling Centre promotes, supports and enhances mental health and wellbeing within the University student community.
- ANUSA supports and represents undergraduate and ANU College students
- PARSA supports and represents postgraduate and research students
Michael Martin is Professor of Statistics. He has a PhD in Statistics from ANU, and has been lecturing at ANU since 1994. His research interests include bootstrap and resampling methods and applications of statistics to problems from a wide range of disciplines. He is also interested in Statistical Education, and higher education in general, and is Principal Fellow of the Higher Education Academy (PFHEA).
Prof Michael Martin