Statistical Modelling and Experimental Design
UndergraduateUNE-STAT2102025
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- Study method
- 100% online
- Assessments
- Subject may require attendance
- Entry requirements
- Part of a degree
- Duration
- 16 weeks
- Start dates
- 24 Feb 2025
- Loan available
- FEE-HELP available
Statistical Modelling and Experimental Design
About this subject
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.
Are you interested in developing and applying statistical models for the natural or social sciences? Do you want to learn more about the principles of designing a data collection? This subject will help you develop the core skills and knowledge needed for experimental designs and applied statistical models that are used in many scientific fields. Studying this subject, you will learn to develop and analyse various types of linear regression models which are the foundation of many statistical analyses. You will also explore some common experimental designs such as factorial design and randomised block design. Focusing on both the theoretical and technical aspects of key statistical concepts, topics include multiple linear regression with quantitative and qualitative explanatory variables, polynomial regression and generalised linear models.
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, 2;
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, experimental design - Students must obtain at least 40% for the assessments overall. Relates to Learning Outcomes 1, 2, 3;
Final Examination: 2 hrs 15 mins. Notes - Students must obtain at least 40% in the final examination and obtain a 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 (5%)
- Assessment 2 (15%)
- Assessment 3 (15%)
- Assessment 4 (15%)
- Final Invigilated Examination (50%)
For textbook details check your university's handbook, website or learning management system (LMS).
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Entry requirements
Part of a degree
To enrol in this subject you must be accepted into one of the following degrees:
Elective
- UNE-INF-DIP-2025 - Diploma in Information Technology
- UNE-DSC-DIP-2025 - Diploma in Science
Prior study
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:
one of
- UNE-SCI210-Introduction to Scientific Programming
- UNE-STAT100-Introduction to Statistical Modelling
- UNE-AMTH250-Computational Mathematics
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).
Others
Candidature in the Diploma in Information Technology or the Diploma in Science.
To enrol in this subject you will need to pass the Prerequisite/s. 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 pass the prerequisite subject/s.
Additional requirements
- Equipment requirements - 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).
- Software requirements - 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. Please refer students to link for requirements: http://www.une.edu.au/current-students/support/it-services/hardware
- Other requirements -
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.
Study load
- 0.125 EFTSL
- This is in the range of 10 to 12 hours of study each week.
Equivalent full time study load (EFTSL) is one way to calculate your study load. One (1.0) EFTSL is equivalent to a full-time study load for one year.
Find out more information on Commonwealth Loans to understand what this means to your eligibility for financial support.
Related degrees
Once you’ve completed this subject it can be credited towards one of the following courses
Diploma in Information Technology
UndergraduateUNE-INF-DIP
UndergraduateUNE-DSC-DIP