STATS 7107 - Statistical Modelling and Inference
North Terrace Campus - Trimester 3 - 2023
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General Course Information
Course Details
Course Code STATS 7107 Course Statistical Modelling and Inference Coordinating Unit Mathematical Sciences Term Trimester 3 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact Up to 4 hours per week Available for Study Abroad and Exchange Y Prerequisites MATHS 7027 Assumed Knowledge Experience with the statistical package R such as would be obtained from MATHS 7107 Assessment Ongoing assessment, examination Course Staff
Course Coordinator: Mr Max Glonek
Course Timetable
The full timetable of all activities for this course can be accessed from Course Planner.
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Learning Outcomes
Course Learning Outcomes
Upon successful completion of this course you will be able to:
1 Explore the statistical theory of modelling and analysis. 2 Derive the key results needed for statistical modelling and inference. 3 Identify statistical techniques for parameter estimation. 4 Analyse data using the theory of statistical modelling and inference to solve real-world problems. 5 Discuss the principles and results of statistical modelling and analysis using clear language and appropriate terminology. 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, 4, 5 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, 4 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.
1, 2, 3, 4, 5 Attribute 7: Digital capabilities
Graduates are well prepared for living, learning and working in a digital society.
1, 2, 3, 5 Attribute 8: Self-awareness and emotional intelligence
Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.
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Learning Resources
Required Resources
None.Recommended Resources
- J.A. Rice: Mathematical Statistics and Data Analysis, 3rd edition (2007).
- D.D. Wackerly, W. Mendelhall and R.L. Scheaffer: Mathematical Statistics with Applications, 7th edition (2008).
- R.J. Larsen, M.L. Marx: An Introduction to Mathematical Statistics and its Applications, 5th edition (2012).
Online Learning
This course uses MyUni for providing electronic resources, such as lecture notes, assignments, tutorial, and computer exercises. It is recommended that students make appropriate use of these resources. -
Learning & Teaching Activities
Learning & Teaching Modes
This course is delivered in a semester, trimester and intensive format, although enrolment options may be limited by availability.
This course offers opportunities for you to learn through blended learning approaches, meaning some of the learning is done autonomously online and some of the learning is done through face-to-face engagement. This blended approach is used to create a rich scaffolded and supportive learning experience.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
This is a 3-unit course. In the semester or trimester format, you are expected to allocate the following study time to fully meet the Course Learning Outcomes (CLOs) for this course. Please note that students work at different paces, so this indicates the approximate time required to complete this course.
Learning Activity Hours/Week Duration Total Online learning activities 2 hour 12 weeks 24 hours Face-to-face learning activities (workshops, tutorials, computer exercises) 2 hours 12 weeks 24 hours Assessment tasks (online quizzes, assignment, tests) 5 hours 12 weeks 60 hours Independent study 4 hours 12 weeks 48 hours Expected total student workload 156 hours Learning Activities Summary
Online Learning Material Outline- Estimation
- Confidence Intervals and Hypothesis Testing
- P-values, Sample Size, and Sampling Distributions
- More Distributions, t-tests, and Inference for Variance
- Pivotal Quantities and Inference for Two Samples
- Simple Linear Regression
- Residuals and Multiple Regression
- Multiple Regression Mypotheses and Model Selection
- Polynomial Regression and Variable Transformations
- Regression Models, ANOVA, and ANCOVA
- Likelihood Theory
- Parameter Transformations in Likelihood Estimation
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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
Assessment
TaskWeighting Due Task Type Learning
OutcomesQuizzes 5% Weeks
1-7, 9-11Formative All Assignments 20% Weeks 3,5,7,11 Summative All Theory Test 12.5% Week 8 Summative All Practical Test 12.5% Week 12 Summative All Examination 50% Examination period Summative All Assessment Related Requirements
An aggregate final score of at least 50% is required to pass the course.Assessment Detail
No information currently available.
Submission
Unless otherwise specified, submit all of your assessments to the Assignments space in the MyUni course site for this course. For written assessments, your submissions will go through Turnitin to check for originality. Make sure your submissions adhere to the University of Adelaide Academic Integrity policies.Course Grading
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending 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.
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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.
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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
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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
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