• Class Number 3990
  • Term Code 3330
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • Dr Yuan Gao
    • Dr Yuan Gao
  • Class Dates
  • Class Start Date 20/02/2023
  • Class End Date 26/05/2023
  • Census Date 31/03/2023
  • Last Date to Enrol 27/02/2023
SELT Survey Results

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. 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.       

Learning Outcomes

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

  1. Summarise and graph data appropriately;
  2. Work with random variables and probability distributions and describe the rationale behind them;
  3. Describe and use the normal distribution appropriately;
  4. Identify when and how to carry out basic statistical inference including confidence intervals, hypothesis testing, regression and analysis of variance; and,
  5. Identify contexts in which particular statistical methods may be inappropriate.

Research-Led Teaching

This course aims to provide you with a foundation in statistical thinking and evidence-based logic, two elements that are integral to any academic program and life in the work

force beyond your university degree. Almost all areas of research require both elements. Any research that involves data also involves statistical computing. We do so with the

software package R (https://www.r-project.org) at an elementary level.

I will also introduce examples, whenever applicable, from my current research areas in class to further illustrate concepts and the use of statistics.

Examination Material or equipment

Examination material and condition will be notified to all students via wattle and the examination office. Details will be provided no later than Week 4 for the mid semester exam

and Week 10 for the final exam.

Required Resources

Course text (free e-book): OpenIntro Statistics, 3rd Edition by David M Diez, Christopher D Barr, Mine Çetinkaya-Rundel (https://www.openintro.org/stat/ )

Whether you are on campus or studying remotely, there are a variety of online platforms you will use 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

Students will be given feedback in the following forms in this course:

  • in lectures where general comments are given to the whole class;
  • in tutorials through interactions with tutors and discussions with other students;
  • through assessments where students' work will be marked and solutions will be given;
  • through consulting teaching staff during their consultation time.

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 to data No tutorials in Week 1
2 Introduction to data Tutorials begin
3 Probability Release of Assignment 1
4 Discrete random variables
5 Continuous random variables Assignment 1 due
6 Sampling distribution Mid-semester exam, Week 6 or 7
7 Point and interval estimators Mid-semester exam, Week 6 or 7
8 Hypothesis testing
9 Comparing two and more populations Release of Assignment 2
10 Chi-square tests
11 Simple linear regression Assignment 2 due
12 Multiple linear regression

Tutorial Registration

Tutorials will be held weekly (starting from week 2). Tutorials will be available on campus, live through scheduled Zoom sessions and as pre-recorded videos. Students should enrol in their tutorial using MyTimetable. 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. https://www.anu.edu.au/students/program-administration/timetabling].

Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Assignment 1 5 % 22/03/2023 26/03/2023 1,2
Mid-semester exam 20 % 21/04/2023 05/05/2023 1,2,3
Assignment 2 10 % 17/05/2023 31/05/2023 1,2,3,4,5
Final exam 65 % 01/06/2023 29/06/2023 1,2,3,4,5

* 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 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.


Course content delivery will take the form of on-campus weekly lectures (with record available via Echo 360 on Wattle) and weekly tutorials delivered in hybrid format (on campus, live through scheduled Zoom sessions and as pre-recorded videos).

Attendance at lectures and tutorials, while not compulsory, is expected in line with “Code of Practice for Teaching and Learning”, clause 2 paragraph (b).


Centrally administered examinations through Examinations, Graduations & Prizes will be timetabled prior to the examination period. Please check ANU Timetabling for further information.

Assessment Task 1

Value: 5 %
Due Date: 22/03/2023
Return of Assessment: 26/03/2023
Learning Outcomes: 1,2

Assignment 1

The assignment will be done via Wattle Quiz. The Wattle Quiz window will open in Week 3 and close in Week 5. In this assignment, you will need to use the tools you learn in Week 1 and 2 to analyse a dataset and use R to do some simple calculations and plotting.The marked assignment will be returned to students by the end of Week 5. This assignment is not redeemable.

Assessment Task 2

Value: 20 %
Due Date: 21/04/2023
Return of Assessment: 05/05/2023
Learning Outcomes: 1,2,3

Mid-semester exam

The mid-semester examination will be held during week 6 or 7 (subject to confirmation from the Examinations Office), covering material from Weeks 1–5 or 1-6, inclusive

(depending on when the exam is held). It will be a 90-minute (inc reading time) examination. Students will be provided with further details regarding the exam no later than

Week 4. The mid-semester exam is redeemable towards the final exam.

Assessment Task 3

Value: 10 %
Due Date: 17/05/2023
Return of Assessment: 31/05/2023
Learning Outcomes: 1,2,3,4,5

Assignment 2

Answer specified questions based on materials from Weeks 1–10. The questions will be made available in Week 9. Submission through Turnitin is required. You may type

your answers in a word-processing program or you may handwrite your answers, or a combination of the two. Please ensure that your handwriting is legible and

submitted electronically as instructed. The questions may require you to include certain R output. Although verbal discussions with others (fellow students, tutors,

lecturer) are encouraged, the contents of your assignment must be produced by you as an individual and must comply with academic integrity policies given at

http://www.anu.edu.au/students/academic-skills/academic-integrity and https://www.cbe.anu.edu.au/current-students/policies/examinations-assessment/. The

assignment will be returned to students by the end of the semester. The assignment is not redeemable.

Assessment Task 4

Value: 65 %
Due Date: 01/06/2023
Return of Assessment: 29/06/2023
Learning Outcomes: 1,2,3,4,5

Final exam

This final exam will be based on all the materials covered throughout the duration of the semester. The final examination is a compulsory piece of assessment and worth

65% of the final raw score, or more if the mid-semester exam is redeemed. It will be a three-hour open-book exam (inc reading time). The final exam will be held during the exam period with details to be advised no later than Week 10.

Academic Integrity

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

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.

As a further academic integrity control, students may be selected for a 15 minute individual oral examination of their written assessment submissions.

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 Integrity Rule.

Hardcopy Submission

There is no hardcopy submission in the course.

Late Submission

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

  • Late submission not permitted for Assignment 1. If submission of assessment tasks without an extension after the due date is not permitted, a mark of 0 will be awarded.
  • Late submission permitted for Assignment 2. Late submission of assessment tasks without an extension are penalised at the rate of 5% of the possible marks available per working day or part thereof. Late submission of assessment tasks is not accepted after 10 working days after the due date, or on or after the date specified in the course outline for the return of the assessment item.
  • Late submission is not accepted for examinations.

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.

Resubmission of Assignments

Resubmission of Assignments after the due date is not allowed.

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.
In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
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.

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).

Dr Yuan Gao
+61 2 612 57290

Research Interests

Functional data analysis; time series forecasting; High-dimensional data analysis

Dr Yuan Gao

Thursday 15:00 16:00
Thursday 15:00 16:00
Dr Yuan Gao
+61 2 612 57290

Research Interests

Dr Yuan Gao

Thursday 15:00 16:00
Thursday 15:00 16:00

Responsible Officer: Registrar, Student Administration / Page Contact: Website Administrator / Frequently Asked Questions