This course introduces students to the philosophy and methods of modern statistical data analysis and inference, with a particular focus on applications to the life sciences.
Why and how to use: tables to organise and summarise data; graphics to present statistical information; measures of location and spread for univariate distributions. Concepts of randomness, uncertainty, random variables, probability distributions (including uniform, binomial, normal), and sampling distributions and how to apply these for inference from small and large samples through: confidence intervals; hypothesis testing in one and two sample cases; p-values; linear regression models and analysis of variance. Examples and applications will be drawn extensively from the life sciences, particularly Biology. The course has a strong emphasis on computing and graphical methods, and uses a variety of real-world problems to motivate the theory and methods required for carrying out statistical data analysis. This course makes extensive use of R statistical analysis package interfaced through R Studio.
Upon successful completion, students will have the knowledge and skills to:
Upon successful completion of the requirements for this course, students should have the knowledge and skills to:
- Summarise and graph data appropriately;
- Work with random variables and probability distributions and understand the rationale behind them;
- Understand and use the normal distribution appropriately;
- Identify when and how to carry out basic statistical inference including confidence intervals, hypothesis testing and regression and ANOVA; and,
- Identify contexts in which a method may be appropriate (e.g. using a large sample method when sample size is small).
See the course outline on the College courses page. Outlines are uploaded as they become available.
Indicative AssessmentTypical assessment may include, but is not restricted to: assignments, quizzes and a final exam.
The ANU uses 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. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
Students are expected to commit at least 10 hours per week to completing the work in this course. This will include at least 3 contact hours per week and up to 7 hours of private study time.
Requisite and Incompatibility
Prescribed TextsDavid M Diez, Christopher D Barr, Mine Çetinkaya-Rundel (2015), OpenIntro Statistics, 3rd Edition
(online text from https://www.openintro.org/stat/textbook.php)
Assumed KnowledgeYou are assumed to have already mastered some basic postsecondary mathematical concepts and techniques (summation sets, simple functions and their derivatives and integrals). If you aren't sure whether you already have that background training, you should audit the 5-week Introductory Mathematics supplementary lectures (not part of the STAT1003 curriculum). More information will be available on the course outline.
Tuition fees are for the academic year indicated at the top of the page.
If you are a domestic graduate coursework or international student you will be required to pay tuition fees. Tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
- Student Contribution Band:
- Unit value:
- 6 units
If you are an undergraduate student and 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). You can find your student contribution amount for each course at Fees. Where there is a unit range displayed for this course, not all unit options below may be available.
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|
|2625||20 Feb 2017||27 Feb 2017||31 Mar 2017||26 May 2017||In Person||N/A|