STATS 7008 - Statistics Topic D
North Terrace Campus - Semester 2 - 2023
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General Course Information
Course Details
Course Code STATS 7008 Course Statistics Topic D Coordinating Unit Mathematical Sciences Term Semester 2 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Available for Study Abroad and Exchange Y Assessment Ongoing assessment, exam Course Staff
Course Coordinator: David Shorten
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
In 2021 STATS 7008 will be offered online through the AMSI ACE Network as the course
Categorical Data Analysis
A link to the subject guide can be found here: Students will need to enrol in the course via the following link: in addition to enrolling in the course through MyAdelaide. A quiz is available to check your background knowledge here:
Note that the course will start on July 19 and end on October 29
The lecturer for the course will be Professor Eric Beh. Lectures will be held via Zoom.
The following is a summary of the course from the above subject guide:
Categorical data abounds in all disciplines as researchers and analysts search for
ways of analysing data collected from surveys or questionnaires. Undergraduate
courses only provide a cursory glance at how categorical data can be analysed. In
this course we will examine some of the core contributions to categorical data
analysis with a focus on measures of association, categorical data visualisation and
modelling categorical data.
The course will include the following topics
• Visualisation of categorical data
• History and development of contingency tables
• Pearson’s chi-squared statistic and related measures
• Features, and variations of the odds ratio for single and stratified data
• Reciprocal averaging and singular value decomposition
• Correspondence analysis
• Modelling categorical data
On successful completion of the course, students will be able to:
1. Gain a deeper understanding of the analysis of categorical data
2. Explore more deeply the issue concerned with Pearson’s chi-squared statistic and related measures of association that reflect symmetric and asymmetric association
3. Apply new statistical tools to numerically and visually analyse multiple categorical variables
4. Apply a variety of correspondence analysis techniques
5. Model categorical data using association models and log-linear models
6. Apply their skills to real-life data using R
7. Undertake basic research skills concerned with categorical data analysis
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) Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
all Attribute 2: Creative and critical thinking, and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.
all Attribute 3: Teamwork and communication skills
Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.
all Attribute 4: Professionalism and leadership readiness
Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.
all Attribute 5: Intercultural and ethical competency
Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.
all Attribute 8: Self-awareness and emotional intelligence
Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.
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Learning Resources
Required Resources
Students are not required to purchase any reference book for this course. Instead lecture notes will be provided. Published articles in commonly available, high-profile, journals will also be made available to students for additional insight and context of the weekly topics; they will need to access this material through their own library (full bibliographic information of each article will be provided)
Students will need to have access to R/Rstudio. They can be freely downloaded from the internet. -
Learning & Teaching Activities
Learning & Teaching Modes
No information currently available.
Workload
No information currently available.
Learning Activities Summary
Week by week topic overview:
Week 1: An overview of categorical data, its history and standard techniques
Week 2: Analysis of a single categorical variable – goodness-of-fit tests
Week 3: The contingency table, chi-squared statistic & related measures
Week 4: Measures of symmetric association for 2x2 contingency tables
Week 5: Measures of symmetric association for IxJ contingency tables
Week 6: Measures of asymmetric association for IxJ contingency tables
Week 7: Scaling categorical data – reciprocal averaging & canonical correlation analysis
Week 8: Simple correspondence analysis
Week 9: Non-symmetric correspondence analysis
Week 10: Multiple correspondence analysis
Week 11: Models of correlation and association
Week 12: Log-linear models -
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
Assessment Percent of final mark Written assignments (3) 45 Final exam 55
The due dates for the assignments are as follows:
Assignment 1: due August 20
Assignment 2: due September 17
Assignment 3: due October 29
Assessment Related Requirements
A final aggregate score of at least 50% is required to pass the course.Assessment Detail
No information currently available.
Submission
All written assignments are to be submitted to the lecturer with a signed cover sheet attached. There will be a maximum two week turn-around time on assignments for feedback to students.
Late assignments will not be accepted, but students may be excused from an assignment for medical or compassionate reasons. In such cases, documentation is required and the lecturer must be notified as soon as possible before the fact.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 .
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Student Feedback
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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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