STATS 2107 - Statistical Modelling and Inference II

North Terrace Campus - Semester 2 - 2019

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 2107
    Course Statistical Modelling and Inference II
    Coordinating Unit School of Mathematical Sciences
    Term Semester 2
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 4 hours per week
    Available for Study Abroad and Exchange Y
    Prerequisites MATHS 1012 and (STATS 1000 or STATS 1004 or STATS 1005 or MATHS 2201)
    Assumed Knowledge MATHS 2103. Experience with the statistical package R such as would be obtained from STATS 1005.
    Course Description 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.
    Course Staff

    No information currently available.

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    On successful completion of this course, students will be able to

    1. demonstrate their understanding of the mathematics of statistical inference;
    2. derive the distributional results needed for statistical inference;
    3. conduct appropriate hypothesis tests for comparing two means and for regression;
    4. recognise that hypothesis tests, regression and analysis of variance belong to the same theory of linear models;
    5. demonstrate their understanding of the theory of maximum likelihood estimation for a scalar parameter;
    6. analyse a variety of datasets and fit linear regression models using R; and
    7. interpret and communicate the results of statistical analyses, orally and in writing.

    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)
    Deep discipline knowledge
    • informed and infused by cutting edge research, scaffolded throughout their program of studies
    • acquired from personal interaction with research active educators, from year 1
    • accredited or validated against national or international standards (for relevant programs)
    all
    Critical thinking and problem solving
    • steeped in research methods and rigor
    • based on empirical evidence and the scientific approach to knowledge development
    • demonstrated through appropriate and relevant assessment
    all
    Teamwork and communication skills
    • developed from, with, and via the SGDE
    • honed through assessment and practice throughout the program of studies
    • encouraged and valued in all aspects of learning
    all
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    all
    Self-awareness and emotional intelligence
    • a capacity for self-reflection and a willingness to engage in self-appraisal
    • open to objective and constructive feedback from supervisors and peers
    • able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
    all
  • Learning Resources
    Required Resources
    There are no required resources for this course. Lecture notes are provided and R and RStudio are freely available.
    Recommended Resources
    I will refer to the following two text books throughout the course. Both books contain material directly relevant to the content and objectives of the course, and are available in the Barr Smith library:

    J. A. Rice: Mathematical Statistics and Data Analysis. Third edition (2007).
    D.D. Wackerly, W. Mendelhall and R.L. Scheaffer: Mathematical Statistics with Applications. Seventh edition (2008).
    Online Learning
    This course uses MyUni for providing electronic resources, such as lecture notes, assignments, tutorial and practicals. It is recommended that the students make appropriate use of these resources.

    Link to MyUni login page:
    https://myuni.adelaide.edu.au/webapps/login/
  • Learning & Teaching Activities
    Learning & Teaching Modes
    The lecturer guides the students through the course material in 30 lectures. Students are expected to prepare for lectures by reading the printed notes in advance of the lecture, and to engage with the material in the lectures. Interaction with the lecturer and discussion of any difficulties that arise during the lecture is encouraged. Students are expected to attend all lectures, but lectures will be recorded to help with occasional absences and for revision purposes. In the fortnightly tutorials, students will discuss their solutions in groups and present them to the class on the board. 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 computing skills using R.  Five homework assignments build on the tutorial and practical material and help students strengthen their understanding of the theory and practical work, and gives them 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 30 90
    Tutorials 6 18
    Assignments 5 30
    Practicals 6 18
    TOTALS 156
    Learning Activities Summary
    Lecture Outline

    1. Introduction to statistical inference: notation, mean squared error (Week 1)
    2. Best Linear Unbiased Estimation (BLUE) (Week 2)
    3. Confidence intervals, tests of hypotheses and power calculations (Week 3)
    4. Inference for a single sample, unknown variance; pivotal quantities (Week 4)
    5. Inference for two independent samples (Week 5)
    6. Regression modelling and least squares estimation (Week 6)
    7. Prediction for regression and residuals (Week 7)
    8. Multiple linear regression and least squares estimation (Week 8)
    9. BLUE and tests of hypotheses (Week 9)
    10. Applications to prediction, polynomial regression and one-way analysis of variance (Week 10)
    11. Analysis of covariance and two-way analysis of variance (Week 11)
    12. Maximum likelihood (ML) estimation (Week 11)
    13. Inference for ML estimators and tests based on the likelihood (Week 12)

    Tutorial Outline

    1. MSE, BLUE, expectation and MGFs
    2. Chi-squared distribution, inference for two independent samples
    3. Regression and properties of estimators
    4. Multiple regression, matrix formulation
    5. Parallel regression and other applications
    6. Maximum likelihood estimation and hypothesis tests

    Practical Outline


    1. Introduction to R, reading data into R.
    2. Summary statistics and cleaning data.
    3. Using ggplot to visualise data.
    4. Using Rmarkdown to write statistical reports.
    5. Modelling data part 1. 
    6. Modelling data part 2.
  • 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
    Assessment
    task 
    Weighting Due Task type Learning
    outcomes
    Project 10% Weeks 12 Summative All
    Flipped classroom 5% Weeks 7, 8 Summative All
    Assignments 15% Weeks 3,5,7,9,11 Summative All
    Examination 70% Examination period Summative All
    Assessment Related Requirements
    An aggregate final score of at least 50% is required to pass the course.
    Assessment Detail

    Assessment Item Distributed Due Date Weighting
    Assignment 1 Week 1 Week 3 4%
    Assignment 2 Week 3 Week 5 4%
    Assignment 3 Week 5 Week 7 4%
    Assignment 4 Week 7 Week 9 4%
    Assignment 5 Week 9 Week 11 4%
    Submission

    All written assignments are to be submitted to the designated hand in box within the School of Mathematical Sciences with a signed cover sheet attached.

    Late assignments will not be accepted unless with permission by the lecturer.

    Assignments will have a two week turn-around time for feedback to students.

    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

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    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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  • Policies & Guidelines
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