STATS 7107 - Statistical Modelling and Inference

North Terrace Campus - Trimester 3 - 2024

Course Content: Statistical methods underpin disciplines which draw inference from data and this includes just about everything: for example, the sciences, humanities, technology, education, engineering, government, industry and medicine. Analysis of the complex problems arising in practice requires an understanding of fundamental statistical principles together with knowledge of how to use suitable modelling techniques. Computing using high-level software is also an essential element of modern statistical practice. This course provides you with these skills by giving an introduction to the principles of statistical inference and linear statistical models using the freely available statistical package R. Topics covered are: point estimates, unbiasedness, mean-squared error, confidence intervals, tests of hypotheses, power calculations, derivation of one and two-sample procedures: simple linear regression, regression diagnostics, and prediction: linear models, analysis of variance (ANOVA), multiple linear regression, factorial experiments, analysis of covariance models including parallel and separate regressions, and model building; maximum likelihood methods for estimation and testing, and goodness-of-fit tests.

  • 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 3 hours per week
    Available for Study Abroad and Exchange Y
    Prerequisites MATHS 7027
    Assumed Knowledge MATHS 7103. 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.

  • 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.

    4, 5
  • Learning Resources
    Required Resources
    Recommended Resources
    1. J.A. Rice: Mathematical Statistics and Data Analysis, 3rd edition (2007).
    2. D.D. Wackerly, W. Mendelhall and R.L. Scheaffer: Mathematical Statistics with Applications, 7th edition (2008).
    3. 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.

    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 hours 12 weeks 24 hours
    Face-to-face learning activities (seminars, workshops, practicals) 2 hours 12 weeks 24 hours
    Assessment tasks (online quizzes, assignment, tests) 4.5 hours 12 weeks 54 hours
    Independent study 4 hours 12 weeks 48 hours
    Expected total student workload 150 hours
    Learning Activities Summary
    Online Learning Material Outline
    1. Estimation
    2. Confidence Intervals and Hypothesis Testing
    3. P-values, Sample Size, and Sampling Distributions
    4. More Distributions, t-tests, and Inference for Variance
    5. Pivotal Quantities and Inference for Two Samples
    6. Simple Linear Regression
    7. Residuals and Multiple Regression
    8. Multiple Regression Mypotheses and Model Selection
    9. Polynomial Regression and Variable Transformations
    10. Regression Models, ANOVA, and ANCOVA
    11. Likelihood Theory
    12. Parameter Transformations in Likelihood Estimation
  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Weighting Due Task Type Learning
    Quizzes 5% Weeks
    1-7, 9-11
    Formative 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.

    This course also contains an exam hurdle. Students must achieve a grade of at least 40% in the final exam in order to pass the course.

    Assessment Detail


    There are four (4) written assignments for the course. Written assignments will adhere to the following schedule:

    Assignment Released Due
    1 Week 2 Week 3
    2 Week 4 Week 5
    3 Week 6 Week 7
    4 Week 10 Week 11

    All assignments are weighted equally.


    There are ten (10) online quizzes for the course. Quizzes will be due every week except in Weeks 8 and 12. All quizzes are weighted equally.


    A theory test will be held in Week 8.

    A practical test will be held in Week 12.

    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.

  • 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 ( 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
  • Policies & Guidelines
  • 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.

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