STATS 4101 - Statistical Modelling - Honours
North Terrace Campus - Semester 1 - 2023
-
General Course Information
Course Details
Course Code STATS 4101 Course Statistical Modelling - Honours Coordinating Unit Mathematical Sciences Term Semester 1 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Available for Study Abroad and Exchange Y Prerequisites STATS 2107 or (MATHS 2201 and MATHS 2202) or (MATHS 2106 and MATHS 2107) Assumed Knowledge Experience with the statistical package R such as would be obtained from STATS 1005 or STATS 2107 Restrictions Honours students only Assessment ongoing assessment, exam Course Staff
Course Coordinator: Dr John Maclean
Course Timetable
The full timetable of all activities for this course can be accessed from Course Planner.
-
Learning Outcomes
Course Learning Outcomes
1. Explain the mathematical basis of the general linear model and its extensions to multilevel models and logistic regression.
2. Use the open source programming language R for the analysis of data arising from both observational studies and designed experiments.
3. Explain the role of statistical modelling in discovering information, making predictions and decision making in a range of applications including medicine, engineering, science and social science.University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
1,2,3 Attribute 2: Creative and critical thinking, and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.
1,2,3 Attribute 4: Professionalism and leadership readiness
Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.
2 -
Learning Resources
Required Resources
There is no prescribed text for this course.Recommended Resources
The following references are recommended reading:
1. A. Agresti. Foundations of linear and generalized linear models. Wiley, 2015.
2. A. Dobson and A. Barnett. An introduction to generalised linear models. Third Edition, Chapman & Hall/CRC, 2008.
3. P. McCullagh and J. Nelder. Generalised linear models. Second Edition, Chapman & Hall/CRC, 1989.
4. W. Venables and B. Ripley. Modern Applied Statistics with S. Fourth Edition, Springer, 2002.Online Learning
This course uses MyUni-Canvas for providing course materials and resources, including lecture notes, assignment papers, tutorial and computing worksheets, solutions, project materials and so on. Students should check their email and MyUni announcements for this course regularly for any notices or correspondence from the Course Coordinator and tutors. -
Learning & Teaching Activities
Learning & Teaching Modes
The lecturer guides the students through the course material in 24 lectures. Students are expected to prepare for lectures by reading the printed notes in advance of the lecture, and by engaging with the material in the lectures. Students are expected to attend all lectures, but lectures will be recorded (where appropriate facilities are available) to help with occasional absences and for revision purposes.
In the fortnightly tutorials, students will work on and discuss their solutions in groups, and seek help from the tutors as needed. These exercises will be further supplemented by the fortnightly computing practical sessions during which students will work under guidance on practical data analysis and develop more advanced computing skills using R. A series of three homework assignments and a group project build on the tutorial and practical material, and provides students with the opportunity to gauge their progress and understanding of the course material.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Workload Hours Lectures 24 72 Tutorials 6 12 Practicals 6 12 Assignments 3 15 Test 3 15 Online quizzes 9 30 Total 156 Learning Activities Summary
Week: Topics covered 1. Introduction, matrix notation, multiple regression. The linear regression model. 2. Estimable functions and best linear unbiased estimates. The Gauss-Markov Theorem. Inference for multiple regression. Prediction. Symbolic specification of linear models. 3. Factors and contrasts. The marginality principle. 4. Regression diagnostics; influence diagnostics. Leverage and Cook's distance. 5. Model building: variance reduction in randomised experiments; observational studies and quasi-experiments. The role of predictor variables. 6. Model selection algorithms: forwards, backwards and stepwise selection procedures. Box-Cox transformation and the profile likelihood. 7. Generalised least squares (GLS). The geometry of least squares; orthogonal projections. 8. The geometry of least squares (cont.). Estimation of the residual variance; estimable functions; hypothesis tests; expected mean squares. 9. Orthogonality of hypotheses. Extension to generalised least squares. Multistratum experiments and random effects models. 10. Expected mean squares for the split plot experiment. Least squares estimates for balanced factorial experiments. Averaging operators. 11. Logisitic regression; maximum likelihood estimation; inference for regression coefficient. Hypotheses concerning several parameters. 12. Logisitic regression: common odds rations for several 2x2 tables. Prospective and retrospective studies. Model fit and overdispersion.
Specific Course Requirements
Students completing this course as an Honours course will complete additional work, in particular additional advanced assignment and project work. -
Assessment
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Summary
Component Assessment mode Week Set Week Due Weighting Learning
outcomesAssignments Formative & summative 1, 4, 8 2, 6, 10 30% All Online quizzes Formative & summative 1, 2, 3, 5, 6, 7, 9, 10, 11 10% All Examination Summative 30% All Online test Summative 4, 8, 12 4, 8, 12 30% All Assessment Related Requirements
An aggregate final mark of at least 50% is required to pass the course.Assessment Detail
No information currently available.
Submission
No information currently available.
Course Grading
Grades for your performance in this course will be awarded in accordance with the following scheme:
M11 (Honours Mark Scheme) Grade Grade reflects following criteria for allocation of grade Reported on Official Transcript Fail A mark between 1-49 F Third Class A mark between 50-59 3 Second Class Div B A mark between 60-69 2B Second Class Div A A mark between 70-79 2A First Class A mark between 80-100 1 Result Pending An interim result RP Continuing Continuing CN Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
-
Student Feedback
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
-
Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- International Student Support
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
- YouX Student Care - Advocacy, confidential counselling, welfare support and advice
-
Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Elder Conservatorium of Music Noise Management Plan
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
-
Fraud Awareness
Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.
The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.