STATS 7004 - Statistics Topic A
North Terrace Campus - Semester 1 - 2017
General Course Information
Course Code STATS 7004 Course Statistics Topic A Coordinating Unit School of Mathematical Sciences Term Semester 1 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Available for Study Abroad and Exchange Y Course Description Please contact the School of Mathematical Sciences for further details, or view course information on the School of Mathematical Sciences web site at http://www.maths.adelaide.edu.au
Course Coordinator: Andrew Metcalfe
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesIn 2017 the topic of this course is DESIGN OF EXPERIMENTS.
From a statistical perspective there are two types of research studies: observational studies and designed experiments. In a designed experiment the researcher changes the values of inputs to a system and monitors the effect on the outputs from that system. The objective is to understand and improve the system. However, all systems are subject to some random variation, and replicates will not be identical. We need to allow for this random variation in the analysis. The definition of an experimental design is: the specification of the conditions at which experimental data will be observed. The purpose of designing an experiment is to ensure that you will be able to answer the questions posed at the outset of the investigation and to make the most efficient use of resources.
The assumed knowledge for the course is an introductory statistics course that has covered: probability; descriptive statistics; elementary probability distributions; the sampling distribution of the mean; and preferably something on confidence intervals and regression on a single predictor variable. Notes covering this material can be obtained from the course coordinator.
The course will cover applications in various disciplines including: agriculture; engineering; management; and medicine.
On successful completion of this course students will be able to:
1. understand the need for randomization and replication in experiments;
2. understand methods for reducing variability in experiments including blocking;
3. identify possible confounding factors when designing an experiment and allow for these;
4. advise on a suitable sample size for experiments to avoid wasting resources through an experiment that is too small to demonstrate a
worthwhile effect or excessively large for demonstrating a worthwhile effect;
5. design an experiment for a client;
6. analyze the results of the experiment using the software R;
7. write a succinct non-technical report of the experiment for a client.
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 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
1,2,3 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
Recommended ResourcesIntroduction to the Desin and Analysis of Experiments, GM Clarke & RE Kempson, Arnold, 1997
The R Book (2e), MJ Crawley, Wiley, 2012
Data Analysis and Graphics Using R (3e), J Maindonald & WJ Braun Cambridge, 2010
Design and Analysis of Experiments, DC Montgomery Wiley, 2009
Online LearningElectronic resources, including lecture notes and assignments, will be posted on MyUni. You will also be encouraged to use discussion boards.
Learning & Teaching Activities
Learning & Teaching ModesNotes will be provided before the material is taught through lecture classes. The class size is typically small and you will be encouraged to ask questions and contribute to the discussion. You will be asked to present a case of the design and analysis of an experiment as a small group exercise. There will also be a debate if this is feasible with the number of participants.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Hours Lectures 24 96 Assignments 5 50 Presentation 1 10 Total 156
Learning Activities Summary1. Comparison against a standard and sample size considerations and calculations
2. Comparing two treatments - paired and independent samples
3. Comparison of proportions
4. Comparison of several means - completely randomised designs and randomised block designs, and multiple comparisons
5. Fixed and random effects
6. Latin squares, Graeco-Latin squares
7. Incomplete block designs
8. Two factors at several levels
9. Two level factorial experiments
10. Central composte designs - response surfaces and concamitant variables
11. Hill climbing experiments
12. Robust design
13. Crossed and nested factors and split plot designs
14. General linear mixed effects model
15. Mixture designs
16. Optimal experimental design
+ if time, some of: lattice squares, cyclic designs, cross-over designs
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.
Component Weighting Outcomes assessed Assignments 25% All Group presentation 5% All Exam 70% All
Assessment Related RequirementsA final aggregate score of at least 50% is required to pass the course.
Item Set Due Weighting Assignment 1 week 1 week 3 5% Assignment 2 week 3 week 5 5% Assignment 3 week 5 week 7 5% Assignment 4 week 7 week 9 5% Assignment 5 week 9 week 11 5% Presentation week 3 week 12 5%
SubmissionAssignments are to be submitted with a signed cover sheet attached. Assignments will be marked and returned within two weeks of submission.
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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