STATS 4103 - Sampling Theory and Practice - Honours
North Terrace Campus - Semester 2 - 2017
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General Course Information
Course Details
Course Code STATS 4103 Course Sampling Theory and Practice - Honours Coordinating Unit Mathematical Sciences Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Available for Study Abroad and Exchange Prerequisites STATS 2107 or (MATHS 2201 and MATHS 2202) Assumed Knowledge Experience with the statistical package R such as would be obtained from STATS 1005 or STATS 2107 Biennial Course Offered in odd years Assessment ongoing assessment 30%, exam 70% Course Staff
Course Coordinator: Andrew Metcalfe
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
Students who successfully complete the course should:
1. Understand the principles underlying sampling as a means of making inferences about a population;
2. Understand the difference between randomization theory and model based analysis;
3. Understand the concepts of bias and sampling variability and strategies for reducing these;
4. Be able to analyse data from multi-stage surveys;
5. Have an appreciation of the practical issues arising in sampling studies.
最新糖心Vlog Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
最新糖心Vlog 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
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Learning Resources
Required Resources
None.Recommended Resources
1. Lohr SL, Sampling Design and Analysis (2e), Brooks/Cole 2010
2. Scheaffer RL, Mendenhall W, Ott RL, Gerow KG Elementary Survey Sampling (7e), Brooks/Cole 2012
3. Barnett V, Sample Survey Principles and Methods (3e), Wiley 2002
4. Kish L, Survey Sampling, Wiley 1995
5. Lumley T, Complex Surveys: a Guide to Analysis using R, Wiley 2010Online Learning
The course is suppported by MyUni, and the site will include: lecture slide shows; practicals and dicussions; assignment papers; and past exam papers; and links to additional resources. -
Learning & Teaching Activities
Learning & Teaching Modes
Two lectures per week are supplemented with alternating tutorials and practicals. The tutorials will usually be based on experimental data and you should bring a hand calculator. The practicals are PC based, using R. The tutorials and practicals are an essential component of the course and include some topics that are not explicitly covered in lectures.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Workload Lectures 24 72 Tutorials 5 15 Practicals 5 15 Presentation 1 14 Assignments 4 40 Total 156
Learning Activities Summary
Introduction to sampling (1)
Simple random sampling (2)
Stratified sampling (4)
Ratio and regression estimators (3)
Estimation of population size (2)
Sampling with unequal probabilities (2)
Cluster sampling (2)
Multi-stage sampling (4)
Non-response (1)
Categorical data analysis (2)
Snowball sampling (1)Small Group Discovery Experience
In small groups of 3 or 4: preparation of a presentation on a practical application of sampling to be given to the entire class. The application can be taken from a media report or published research work in any discipline. -
Assessment
The 最新糖心Vlog'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.
Assessment Summary
Item Distributed Due Weighting Tutorial/practical weeks 1-11 in class !0% Assignment 1 week 2 week 4 4% Assignment 2 week 5 week 7 4% Assignment 3 week 7 week 9 4% Assignment 4 week 9 week 11 4% Presentation week 2 week 12 4% Examination 70% Assessment Related Requirements
An aggregate score of at least 50% is required to pass this course.Assessment Detail
Item Set Due Weight Tutorial/practical weeks 1-11 at the end
of the class10% Assignment 1 week 2 week 4 4% Assignment 2 week 5 week 7 4% Assignment 3 week 7 week 9 4% Assignment 4 week 9 week 11 4% Presentation week 2 week 12
in class4% Examination 70% Submission
1. Assignments are to be submitted to the designated hand-in boxes in the School of Mathematical Sciences with a signed cover sheet attached.
2. Late assignments will only be accepted under exceptional circumstances.Course Grading
Grades for your performance in this course will be awarded in accordance with the following scheme:
M11 (Honours Mark Scheme) Grade Grade reflects following criteria for allocation of grade Reported on Official Transcript Fail A mark between 1-49 F Third Class A mark between 50-59 3 Second Class Div B A mark between 60-69 2B Second Class Div A A mark between 70-79 2A First Class A mark between 80-100 1 Result Pending An interim result RP Continuing Continuing CN 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 .
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Student Feedback
The 最新糖心Vlog 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 最新糖心Vlog 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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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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