|Available teaching periods||Delivery mode||Location|
|View teaching periods|| On-Campus
|| UC - Canberra, Bruce
|0.125||3||Faculty Of Science And Technology|
|Discipline||Study level||HECS Bands|
|Academic Program Area - Science||Level 2 - Undergraduate Intermediate Unit|| Band 2 2021 (Commenced After 1 Jan 2021)
Band 3 2021 (Commenced Before 1 Jan 2021)
Learning outcomesOn successful completion of this unit, students will be able to:
1. Be able to apply prior knowledge of scientific methods in the analysis and interpretation of scientific data;
2. Demonstrate knowledge of existing statistical methodologies such as linear regression, ANOVA and hypothesis testing using parametric and non-parametric tests;
3. Develop an understanding of the key concepts underpinning each method and the associated assumptions and limitations. Acquiring problem solving skills to enable the resolution of new problems and data types
4. Be able to communicate effectively using well-developed scientific thinking; and
5. Develop independent critical scientific thinking.
Graduate attributes1. UC graduates are professional - communicate effectively
1. UC graduates are professional - display initiative and drive, and use their organisation skills to plan and manage their workload
1. UC graduates are professional - employ up-to-date and relevant knowledge and skills
1. UC graduates are professional - take pride in their professional and personal integrity
1. UC graduates are professional - use creativity, critical thinking, analysis and research skills to solve theoretical and real-world problems
1. UC graduates are professional - work collaboratively as part of a team, negotiate, and resolve conflict
2. UC graduates are global citizens - behave ethically and sustainably in their professional and personal lives
2. UC graduates are global citizens - communicate effectively in diverse cultural and social settings
2. UC graduates are global citizens - adopt an informed and balanced approach across professional and international boundaries
PrerequisitesData Analysis in Science, 1809.
Equivalent units6913 Biometry AND 6542 Experiment Design and Analysis AND 7904 Forensic Statistics
|Year||Location||Teaching period||Teaching start date||Delivery mode||Unit convener|
|2022||UC - Canberra, Bruce||Semester 1||07 February 2022||On-Campus||Dr Bernd Gruber|
Textbook (highly recommended, but not required, some copies are available at the library)
As the unit consists of a variety of statistical methods there is no single book covering all the material taught in the unit. Additional material will be supplied via Canvas for more specialised methods. A deep understanding of linear models and all of its variants are the most important part of the unit therefore the following books are highly recommended:
Andy Field et al., Discovering Statistics Using R 1st Edition, Sage, ISBN-13: 978-1446200469
Crawley, Michael J , Statistics: An Introduction Using R 2nd Edition, Wiley, ISBN-13: 978-1118941096
Additional recommended books are:
Alan Zuur, Mixed Effect Models and Extensions in Ecology with R, Springer, ISBN 978-0-387-87458-6
Borcard et al., Numerical Ecology using R, Springer, ISBN 978-1-4419-7976-6
John K. Kruschke , Doing Bayesian Analysis using R, Elsevier, ISBN: 9780124058880
Other Recommended Reading:
There are multiple excellent resources freely available, which will be provided via links on the unit's Canvas site.
Submission of assessment items
Special assessment requirements
Assessable exercises are completed in and out of class and are to be submitted electronically. Detailed description of each assessable exercise and instructions are provided via Canvas. Details of expectations, submission requirements, assessment criteria and other information are given in the work descriptions in addition to the desired generic skills and graduate attributes to be achieved. All generic skills and graduate attributes are relevant in these assessable exercises.
Students should keep a back-up copy of any assessment item that has been submitted.
The unit convener reserves the right to question students orally on any of their submitted work.
Students must achieve a minimum of 45% in the final assignment in order to pass the unit.
Attendance at the practical lab classes is compulsory. Persistent absences of less than 9 out of 11 (less than 80%) from practical lab classes without explanation will exclude a student from the unit and a fail result recorded for that student. Attendance and participation at practical classes will be taken into account when deciding final grades.
Students have a responsibility to uphold University standards on ethical scholarship. Good scholarship involves building on the work of others and use of others' work must be acknowledged with proper attribution made. Cheating, plagiarism, and falsification of data are dishonest practices that contravene academic values. Refer to the University's Student Charter for more information.
To enhance understanding of academic integrity, all students are expected to complete the Academic Integrity Module (AIM) at least once during their course of study. You can access this module within UCLearn (Canvas) through the 'Academic Integrity and Avoiding Plagiarism' link in the Study Help site.
Use of Text-Matching Software
The University of Canberra uses text-matching software to help students and staff reduce plagiarism and improve understanding of academic integrity. The software matches submitted text in student assignments against material from various sources: the internet, published books and journals, and previously submitted student texts.
Lectures are recorded and available via the canvas site. To successfully complete the unit it is expected to spend around 150 hours of work on the unit, which equates around 6-7 hours per week in addtion to lectures, labs and assignments. An indicative work load breakdown can be found below:
Teaching philosophy and conduct
The teaching philosophy of the unit convener and associated staff is that all work in this 2nd year unit is not just a one-way stream of information from the staff to the students, but a collaborative discovery journey of both students and staff. To promote deep understanding the unit is designed to guide and challenge the students to reinvent "the wheel(s)" for themselves rather than provide straight recipes. It is therefore necessary and expected that the students fully commit themselves to the unit, by coming prepared to lectures, participate actively in group activities, lectures, tutorials and labs and spend abundant time on self-study. The students may expect that the staff will create a supportive intellectual environment and teach and mentor to the best of their abilities in a professional respectful manner. The staff and unit convener expect in exchange that the students will behave in an equally professional and respectful manner. The staff may, from time to time, make general (or discrete individual) suggestions towards improving professional behaviour, with the aim to improve the learning experience in the unit or to guide students in their efforts to increase their chances of future professional employment.
Attendance at labs is compulsory and highly recommended. Students will have difficulty completing computer practical exercises without the knowledge imparted during lectures. Therefore it is important that students come prepared to lectures by reading the suggested material and revising the content of the previous weeks.
Participation in tutorials/labs is a compulsory condition of this unit, and attendance will be recorded. You must participate in at least 80% of tutorials classes (9 out of 11) to pass this unit. In the event that you cannot attend your assigned laboratory class due to illness or unavoidable commitments, contact the Unit Convener as soon as possible to negotiate an alternate lab class later in the week (if available).
In all cases of absence, sickness or personal problems it is the student's responsibility to ensure that the unit Convener is informed. The minimum participation requirement must be met in order to pass the unit.
Required IT skills
Students are expected to have a basic level of IT understanding and computer literacy. Moreover the ability to work within Rstudio is a requirement. In case students are not feeling comfortable with this requirement, please contact the unit convenor before beginning of the course to explore avenues to meet this requirement at the start of the unit. Specialist IT skills are expected to be acquired over the semester in practical lab classes. Generic skills and graduate attributes 8 is specially relevant here.
The discussion forum at the unit's Canvas site is for asynchronous communication with other students and staff. It is asynchronous because the parties communicating with each other do not all have to be sitting at the computer at the same time.
You do not have to install any special software to access the discussion forum - just use your browser.
To use the Discussion Forum, you need to be able to:
- Navigate through the various topics and conversations
- Add your own topics and converse on topics (threads)
Please note that the Discussion Forum is visible to all academic staff and all students enrolled in the unit. If you have a private enquiry, use email or the personal messaging facility.
There are some costs associated with this Unit such as the purchase of recommended books. Software is provided with no cost associated with it. Students are able to install R and Rstudio on their own computers, but should be aware that the final assessment will be in class using a Windows system, so students need to make sure they are familiar with such a system..
Work placement, internships or practicums
Students should keep a back-up copy of any assessment item that has been submitted.