- Class Number 1708
- Term Code 2920
- Class Info
- Unit Value 6 units
- Mode of Delivery In Person
- Dr Xuan Liang
- Dr Xuan Liang
- Class Dates
- Class Start Date 01/03/2019
- Class End Date 10/05/2019
- Census Date 29/03/2019
- Last Date to Enrol 15/03/2019
This course introduces the principles of data representation, summarisation and presentation with particular emphasis on the use of graphics. The course will use the S-Plus 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
- Recognise and critique examples of good and bad graphics
- Explain in detail and apply the principles of good graphic construction
- 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.
- Be able to use diagnostic plots when conducting statistical modelling to explore and refine statistical models for data, including detailed explanations of such use.
- Construct and interpret graphical displays for dependent data.
This course builds upon the foundation in statistical thinking and evidence-based logic that you have acquired from previous statistics courses. The course contents and activities are designed to help you to learn to apply and hone this foundation to achieve the learning outcomes, and to prepare yourself for the remainder of your academic program and life in the workforce. Course contents and activities heavily involve statistical computing with R interfaced through R Studio. Additional materials (e.g. new reports, journal publications) will be discussed as examples and case studies in which research questions relevant to the course are tackled.
Computing equipment during non-intensive weeks:
- You must make your own arrangements to purchase such equipment or to access ANU on-campus computers which are preloaded with course-relevant software R and Rstudio (free to download by clicking the links). More instructions about the software can be found on Wattle.
- Using a tablet (e.g., iPad, Android tablets) for course-related computing is possible via a cloud computing account. See the Wattle course page for instructions to obtain complimentary NeCTAR cloud computing accounts and course-relevant software.
- The Guardian’s unofficial guide to “Studying online: seven ways to stay motivated.”
- Other recommended resources will be listed on the Wattle course page.
Feedback from the teaching staff will aim to facilitate the learner's ongoing self assessment of his/her progress in achieving the learning objectives of the course. There are several ways to get feedback from the teaching staff:
(a) to benefit all the students, teaching staff strongly suggests the learners post course-related question publicly on the discussion forum of Wattle so that the teaching staff can reply those questions publicly.
(b) those students who have private things to discuss can email the teaching staff for consultation.
(c) students can also make an appointment by email to schedule a face-to-face consultation on campus or online consultation by Zoom or Skype.
In order to safeguard student privacy, staff members need to be sure that they are dealing with the right student, therefore course- related messages sent from non-ANU email accounts will be ignored.
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.
Any student identified, either during the current semester or in retrospect, as having used ghost writing services will be investigated under the University’s Academic Misconduct Rule.
|Week/Session||Summary of Activities||Assessment|
|1||Pre-intensive period: Mar 11 - Apr 5 Introduction and graphics in R Representing and comparing distributions Relationship between two variables, scatterplot smoothing, simple linear models Students watch recorded video and self study additional learning materials published on Wattle.||Assignment 1 (20%): available on March 25 (first day of Week 3 in the pre-intensive period) due on April 5 (last day of Week 4 in the pre-intensive period)|
|2||Intensive period: Apr 8 - 12 Daily Lecture + Tutorial: Catch up Relationship between several variables, multiple linear regression, model diagnostics, variable selection Time dependent data: trend, seasonal and irregular components, autoregressive (AR) and moving-average (MA) models A combination of lectures and tutorials will be held during the intensive week. Refer to Wattle for further information one week before the intensive week.||Assignment 2 (30%): available on April 8 (first day of the week in the intensive period)|
|3||Post-intensive period: Apr 15 - May 10 Catch up Summary of graphical construction Students watch recorded video and self study additional learning materials published on Wattle.||Assignment 2 (30%): due on April 23 (Week 2 in the post-intensive period) Assignment 3 (50%): available on April 26 (last day of Week 2 in the post-intensive period) due on May 10 ( last day of Week 4 in post-intensive period)|
Tutorial signup for this course will be done via the Wattle website. Detailed information about signup times will be provided on Wattle.
|Assessment task||Value||Due Date||Return of assessment||Learning Outcomes|
|Assignment 1||20 %||05/04/2019||12/04/2019||1-7|
|Assignment 2||30 %||23/04/2019||03/05/2018||1-5, 7-8|
|Assignment 3||50 %||10/05/2019||24/05/2019||1-5, 8-9|
* 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 ANU Online website. Students may choose not to submit assessment items through Turnitin. In this instance you will be required to submit, alongside the assessment item itself, hard copies of all references included in the assessment item.
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.
Students will be required to be on campus in Canberra during the one-week intensive period.
Assessment Task 1
Learning Outcomes: 1-7
Details: Students are expected to complete the assignment individually. This assignment is designed to cover the topics about representing and comparing distributions. Detailed instructions will be announced on Wattle.
Due date: April 5 by 4pm. Late submissions will receive 0 marks.
Assessment Task 2
Learning Outcomes: 1-5, 7-8
Details: Students are expected to complete the assignment individually. This assignment is designed to cover the topics about relationships between two and several variables. Detailed instructions will be announced on Wattle.
Due date: April 23 by 4pm. Late submissions will receive 0 marks.
Assessment Task 3
Learning Outcomes: 1-5, 8-9
Students are expected to complete the assignment individually. Assignment 3 will consist of two parts:
a. (20% = 4*5%) A collection of four statistical graphics with written comments on each graphic. These graphics should be collected during the semester from published work (eg. paper, book, newspaper or website, etc). Credit will be given for interesting, carefully chosen graphics which show evidence of reasonably wide searching. In addition to including a copy of the graphic itself, you should document the source of the graphic (title of article, authors, page numbers, website link 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 succinct yet insightful.
b. (30%) This part will focus on Topic of time series data.
Detailed instructions will be announced on Wattle.
Due date: May 10 by 4pm. Late submissions will receive 0 marks.
Academic integrity is a core part of our culture as a community of scholars. At its heart, academic integrity is about behaving ethically. This means that all members of the community commit to honest and responsible scholarly practice and to upholding these values with respect and fairness. The Australian National University commits to embedding the values of academic integrity in our teaching and learning. We ensure that all members of our community 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 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 University has policies and procedures in place to promote academic integrity and manage academic misconduct. Visit the following Academic honesty & plagiarism website for more information about academic integrity and what the ANU considers academic misconduct. The ANU offers a number of services to assist students with their assignments, examinations, and other learning activities. The Academic Skills and Learning Centre offers a number of workshops and seminars that you may find useful for your studies.
You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education) submission must be through Turnitin.
Each assignment must be submitted as a single electronic file using Turnitin within the course Wattle site. Each of these assessments will require both statistical computing via R Studio and mathematical derivations (either handwritten or typeset using R Studio). If submitting handwritten mathematical derivations, ensure that your handwriting is legible, and scan the derivations (e.g., by using your smartphone camera) to be incorporated into your single electronic file. Prior to submission, you should practice using the Turnitin system here. Students can upload draft versions to the designated Turnitin web link on the Wattle course page, and change those drafts every 24 hours up until the due date.
Submissions outside of the designated Turnitin web link and/or after the due date will be ignored.
Assessment submission for this course will be online (see above).
No submission of assessment tasks without an extension after the due date will be permitted. If an assessment task is not submitted by the due date, 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.
Graded assignments should be available via Turnitin within 14 days after the due date. Written feedback will be published on Wattle.
Extensions and Penalties
Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. The Course Convener may grant extensions 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
Resubmission of assessments is not allowed under any circumstance.
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
Spatial modeling, panel data, social network data modeling, biostatistics, environmental statistics and time series.
Dr Xuan Liang