- Code STAT4026
- Unit Value 6 units
- Offered by Rsch Sch of Finance, Actuarial Studies & App Stats
- ANU College ANU College of Business and Economics
- Course subject Statistics
- Areas of interest Statistics
- Academic career UGRD
- Prof Michael Martin
- Mode of delivery In Person
Second Semester 2023
See Future Offerings
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.
- Construct and interpret in detail graphical displays for dependent data.
- Typical assessment may include, but is not restricted to: exams, assignments, quizzes, presentations and other assessment as appropriate (100) [LO 1,2,3,4,5,6,7]
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Students are expected to commit 130 hours of work in completing this course. This includes time spent in scheduled classes and self-directed study time.
Requisite and Incompatibility
You will need to contact the Rsch Sch of Finance, Actuarial Studies & App Stats to request a permission code to enrol in this course.
Information about the prescribed textbook will be available via the Class Summary.
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
Where there is a unit range displayed for this course, not all unit options below may be available.
- Domestic fee paying students
- International fee paying students
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
|Class number||Class start date||Last day to enrol||Census date||Class end date||Mode Of Delivery||Class Summary|
|5746||24 Jul 2023||31 Jul 2023||31 Aug 2023||27 Oct 2023||In Person||N/A|