## STATS 7004 - Statistics Topic A

### North Terrace Campus - Semester 1 - 2020

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

• General Course Information
##### Course Details
Course Code STATS 7004 Statistics Topic A Mathematical Sciences Semester 1 Postgraduate Coursework North Terrace Campus 3 Y Ongoing assessment, exam
##### 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
In 2019 the topic of this course is DESIGN OF EXPERIMENTS.

Description

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.

Learning outcomes

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.

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
• 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
All
• Learning Resources
None.
##### Recommended Resources
Introduction 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 Learning
Electronic 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 Modes
Notes 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 Summary
1. 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
• 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
Due to the current COVID-19 situation modified arrangements have been made to assessments to facilitate remote learning and teaching. Assessment details provided here reflect recent updates.

 Component Weighting Outcomes assessed Assignment (x6) 30% All Exam 70% All
##### Assessment Related Requirements
A final aggregate score of at least 50% is required to pass the course.
##### Assessment Detail
 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%
##### Submission
Assignments 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)
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 (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.

• Student Support
• Policies & Guidelines
• Fraud Awareness

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