STATS 7061 - Statistical Analysis

North Terrace Campus - Semester 1 - 2016

General introductory course on mathematical statistics. Summary statistics and statistical inference. Histograms and sample statistics. Probability and probability distributions. Detailed coverage of Gaussian (normal) distribution and the lognormal distribution. Sampling distributions and tests of significance. Analysis of variance. Multiple variables with emphasis on the bivariate case. Correlation and regression. Bayes' theorem and introduction to Bayesian statistics. Gy's sampling theory for the sampling of particulate materials.

  • General Course Information
    Course Details
    Course Code STATS 7061
    Course Statistical Analysis
    Coordinating Unit Mathematical Sciences
    Term Semester 1
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Contact 1 week intensive - a minimum of 36 hours
    Available for Study Abroad and Exchange Y
    Prerequisites C&ENVENG 7043
    Assumed Knowledge elementary statistics (mean, variance, histogram)
    Assessment coursework 50%, formal written exam 50%
    Course Staff

    Course Coordinator: Dr Tyman Stanford

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    Students who successfully complete the course should:
    1. understand the concept of a frequency distribution for sample data, and be able to summarise the distribution by diagrams and statistics,
    2. understand the principles of probability and the concept of probability distributions,
    3. become familiar with binomial, Poisson, normal and log-normal probability distributions,
    4. understand linear combinations of random variables and the Central Limit Theorem,
    5. understand the concepts of confidence intervals and hypothesis tests,
    6. be able to make statistical comparisons of means (paired and unpaired samples), proportions and variances,
    7. understand the concepts of ANOVA and be familiar with one-way, two-way, and two-way with interaction ANOVA,
    8. understand correlation and regression, and be able to make predictions and understand their limitations,
    9. understand the concept of sample preparation error within a geostatistical sampling context.
    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)
    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
    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
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
  • Learning Resources
    Required Resources
    Course notes: Statistical Analysis by P.A. Dowd.
    Recommended Resources
    1. Statistics and Data Analysis in Geology (3e), J.C. Davis, Wiley 2002
    2. Geostatistics for Natural Resources, P. Goovaerts, Oxford 1997
    3. Geostatistics for Evironmental Scientists, R. Webster, M.A. Oliver, Wiliey 2007
    4. Statistical Methods for Engineers, G.G. Vining, S. Kowalski, Duxbury 2010
    5. Probability and Statistics for Engineering and the Sciences (8e), J. Devore, Thomson
    6. The R Book, M.J. Crawley, Wiley 2007
    Online Learning
    The lectures are given over a single week and it is essential to follow these up through MyUni. The MyUni site includes: lecture slide shows; reordings of the lectures; assignments; data; past examination papers; and a discussion board.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    The course is taught over five days through a programme of lectures interspersed with tutorial exercises. Six written assignments are distributed at the end of the week and provide opportunities for students to gauge their progress and understanding. The submitted solutions to the assignments form part of the assessment. There is also a written examination.

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    Activity Quantity Workload hours
    Lectures 26 78
    Tutorial exercises 10 10
    Assignments 6 68
    Total 156
    Learning Activities Summary
    Lectures (L) and tutorial exercises (T)
    L1 Variables and data summaries
    L2 Measures of location
    L3 Measures of dispersion
    T1 Histograms
    L4 Populations and probability
    L5 Binomial and Poisson distributions
    L6 Gaussian (normal) distribution
    T2 Normal distribution
    L7 Log-normal distribution
    L8 Three parameter log-normal distribution
    L9 Relationships between normal and log-normal distributions
    T3 Log-normal distribution
    L10 Estimation and confidence intervals
    L11 Hypothesis testing
    L12 Chi-square distribution
    T4 Chi-square distribution
    L13 Student's t-distribution
    T5 t-distribution
    L14 F-distribution
    T6 F-distribution
    L15 One way ANOVA
    L16 Two way ANOVA
    T7 ANOVA
    L17 Interaction
    L18 Correlation and regression
    T8 Correlation
    L19 Regression
    L20 Multivariate distributions
    L21 Bivariate normal distribution
    L22 Regression in the bivariate normal distribution
    T9 Regression
    L23 Maximum likelihood estimation
    L24 Bayesian analysis
    L25 Sampling errors for particulates and Gy's formula
    T10 Gy's formula
    L26 Summary
    Small Group Discovery Experience
    The class is divided into small groups, between 2 and 5, for the 10 tutorial exercises.
  • 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
    Component Weighting Objectives assessed
    Assignments 50% all
    Examination 50% all
    Assessment Related Requirements
    An aggregate mark of at least 50% is required for a pass.
    Assessment Detail
    Assessment item Distributed Due date Weighting
    Assignment 1 27 February 27 March 9%
    Assignment 2 27 February 27 March 8%
    Assignment 3 27 February 27 March 8%
    Assignment 4 27 february 27 March 8%
    Assignment 5 27 February 27 March 8%
    Assignment 6 27 February 10 April 9%
    1. Assignments can either be posted to:

    Tyman Stanford
    School of Mathematical Sciences
    Level 6, Ingkarni Wardli Building
    North Terrace Campus
    The University of Adelaide
    SA 5005

    or sent as a PDF to:

    (The Microsoft Word file format can render differently on different computers, especially with different add-ons. Should you not be able to convert Microsoft Word files to PDF in the Microsoft Word program itself, please use The University of Adelaide's free PDF conversion form at:

    2. A School of Mathematical Sciences cover sheet should be attached to your assignments.

    3. Assignments may be submitted at any time before the due date.

    4. Assignments will be marked and feedback given within one week of submission.

    5. If you need extensions beyond the due date, please make a request before the due date.X
    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
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