Statistical Modelling and Experimental Design
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Online and other materials
Subjects may require attendance
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Upon completion of this subject, students will be able to:
- analyse data, and interpret and communicate results and conclusions, from a wide range of experimental designs;
- fit and interpret more complex statistical models, including the linear model; and
- build on and broaden their theoretical and technical knowledge of statistical terminology, concepts and methodology, which will enable them to read and critically appraise scientific literature with some confidence.
- Topics will be available to enrolled students in the subjects Learning Management System site approximately one week prior to the commencement of the teaching period.
You must either have successfully completed the following subject(s) before starting this subject, or currently be enrolled in the following subject(s) in a prior study period; or enrol in the following subject(s) to study prior to this subject:
Please note that your enrolment in this subject is conditional on successful completion of these prerequisite subject(s). If you study the prerequisite subject(s) in the study period immediately prior to studying this subject, your result for the prerequisite subject(s) will not be finalised prior to the close of enrolment. In this situation, should you not complete your prerequisite subject(s) successfully you should not continue with your enrolment in this subject. If you are currently enrolled in the prerequisite subject(s) and believe you may not complete these all successfully, it is your responsibility to reschedule your study of this subject to give you time to re-attempt the prerequisite subject(s)
To enrol in this subject you will need to pass (UNE-SCI210 or UNE-STAT100) subject. Please note as UNE results are released after the close of enrolment date, your enrolment into this subject will be withdrawn if you do not receive a satisfactory result for (UNE-SCI210 or UNE-STAT100).
- EquipmentDetails - Headphones or speakers (required to listen to lectures and other media) Headset, including microphone (highly recommended) Webcam (may be required for participation in virtual classrooms and/or media presentations).
- SoftwareDetails - Please refer students to link for requirements: http://www.une.edu.au/current-students/support/it-services/hardware It is essential for students to have reliable internet access in order to participate in and complete your units, regardless of whether they contain an on campus attendance or intensive school component.
- TravelDetails - Travel may be required to attend the Final Examination for this subject.
- OtherDetails -
Textbook information is not available until approximately 8 weeks prior to the commencement of the Teaching period.
Students are expected to purchase prescribed material.
Textbook requirements may vary from one teaching period to the next.
Regression models are the foundation of many statistical models and experimental designs. In this unit, students learn to develop and analyse linear and logistic regression-based statistical models, and are taught important experimental designs used by many fields. This unit is appropriate for students in the natural or social sciences interested in developing and applying statistical models in their fields, as well as for students in the computational and mathematical sciences interested in learning about applied statistics.
Topics include: design and analysis of experiments with one or two factors; orthogonal partitions; block designs; multiple regression, including use of indicator variables; and an introduction to logistic regression. Students undertaking this subject at postgraduate level will also be required to complete an advanced special reading topic.
Assessment 1: Online Quiz: Simple Linear Regression and hypothesis testing. Students must obtain at least 40% for the assessments overall. Relates to Learning Outcomes 1, 3. Assessment 2: Multiple Regression. Students must obtain at least 40% for the assessments overall. Relates to Learning Outcomes 1, 2, 3. Assessment 3: Model Building, Variable Screening and Residual Analysis. Students must obtain at least 40% for the assessments overall. Relates to Learning Outcomes 1, 2, 3. Assessment 4: Generalised Linear Models. Students must obtain at least 40% for the assessments overall. Relates to Learning Outcomes 1, 2, 3. Assessment 5: Experimental Design, Multi-factor Designs and Contrasts. Students must obtain at least 40% overall. Relates to Learning Outcomes 1, 2, 3. Final Examination: 2 hrs 15 mins. Notes - Must obtain at least 40% in the final examination and obtain an overall passing grade. Relates to Learning Outcomes 1, 2, 3. UNE manages supervised exams associated with your UNE subjects. Prior to census date, UNE releases exam timetables. They’ll email important exam information directly to your UNE email address.
- Assessment 1 (2.5%)
- Assessment 2 (10%)
- Assessment 3 (12.5%)
- Assessment 4 (12.5%)
- Assessment 5 (12.5%)
- Final Examination (50%)