STATS 7053 - Statistics in Engineering

North Terrace Campus - Semester 2 - 2015

Probability and statistical methods play an important role in many aspects of engineering including forecasts of extreme operating conditions, optimisation of industrial processes, quality assurance and design of reliable systems. This course provides an introduction to the theory and practice of probability and statistics in the context of engineering, with an emphasis on modelling. It will provide students with experience in using the high level statistical package R for the analysis of examples from real applications. Topics covered are: review of descriptive statistics and basic probability, Bayes' theorem; random variables, commonly used discrete and continuous distributions including Bernoulli, binomial, geometric, hypergeometric, Poisson, uniform, exponential, normal, log-normal, the Poisson process, linear combinations of random variables, central limit theorem; inference for normal means, hypothesis tests, type 1 and type 2 error rates, confidence intervals, single sample, two independent samples, paired samples, inference for proportions; SPC, the Xbar chart, control limits, runs rules, process capability, general control charts, cusum charts; regression and correlation, simple linear regression, least squares estimation, inference for regression coefficients, prediction and estimation, regression diagnostics; multiple linear regression, least squares estimation, inference, prediction and estimation, diagnostics

  • General Course Information
    Course Details
    Course Code STATS 7053
    Course Statistics in Engineering
    Coordinating Unit Statistics
    Term Semester 2
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Contact Up to 3 hours per week
    Available for Study Abroad and Exchange Y
    Assumed Knowledge Level I Mathematics or equivalent, introductory statistics or equivalent background reading
    Assessment assignments 30%, Final exam 70%
    Course Staff

    Course Coordinator: Andrew Metcalfe

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes

    No information currently available.

    University Graduate Attributes

    No information currently available.

  • Learning Resources
    Required Resources
    Recommended Resources
    A beginner's guide to R, A.F. Zuur, E.N. Ieno, E.H.W.G. Meesters, Springer 2009
    Introductory statistics with R, P. Dalgaard, Springer 2008
    The R Book, M.J. Crawley, Wiley 2007
    Statistical Methods for Engineers, G. Vining, Thomson
    Probability and Statistics, J.L. Devore, Thomson
    Handbook of Monte Carlo Methods, Kroese DP, Taimre T, Botev ZI, Wiley 2011
    Online Learning
    This course uses MyUni exclusively for providing electronic resources, such as lecture notes, assignment papers, sample solutions, discussion boards, etc. It is recommended that the students make appropriate use of these resources.

    Link to MyUni login page: 
  • Learning & Teaching Activities
    Learning & Teaching Modes
    This course relies on lectures as the primary delivery mechanism for the material. Tutorials supplement the lectures by providing exercises and example problems to enhance the understanding obtained through lectures. A sequence of written assignments provides the assessment opportunities for students to gauge their progress and understanding.

    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 18
    Assignments 5 48
    Practicals 6 18
    TOTALS 156
    Learning Activities Summary
    Lecture Outline
    1. Review of descriptive statistics (1 lecture)
    2. Review of basic probability (1 lecture)
    3. Discrete probability distributions (2 lectures)
    4. Continuous probability distributions (3 lectures)
    5. linear combinations of random variables (1 lecture)
    6. Central limit theorem (1 lecture)
    7. Comparing populations (6 lectures)
    8. Statistical process control (2 lectures)
    9. Regression on one predictor variable and correlation (3 lectures)
    10. Multiple regression (4 lectures)

    Tutorial Outline
    1. Stratified sampling of water distribution zones
    2. Paired comparison of reaction times
    3. Industrial process investigation
    4. Analysis of survey with multiple regession
    5. Weibull analysis
    6. Factorial experiment

    Practical Outline

    1. Introduction to R
    2. Comparisons of populations using R
    3. Correlation using R
    4. Regression on one predictor using R
    5. Multiple regression using R
    6. Multiple regression using R
  • 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 Outcomes Assessed
    Assignments 25% All
    Practicals 5% All
    Tutes 5% All
    Examination 65% All
    Assessment Related Requirements
    Aggregate score of at least 50%
    Assessment Detail
    Assessment Item Distributed Due Date Weighting
    Assignment 1 1 August 15 August 5%
    Assignment 2 15 August 29 August 5%
    Assignment 3 29 August 19 September 5%
    Assignment 4 19 September 10 October 5%
    Assignment 5 10 October 24 October 5%

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

    Late assignments will not be accepted.

    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

    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

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