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Course profile

Problems & Applications in Modern Statistics (STAT3500)

Study period
Sem 2 2024
Location
St Lucia
Attendance mode
In Person

Course overview

Study period
Semester 2, 2024 (22/07/2024 - 18/11/2024)
Study level
Undergraduate
Location
St Lucia
Attendance mode
In Person
Units
2
Administrative campus
St Lucia
Coordinating unit
Mathematics & Physics School

Students investigate a number of problems and applications that have a significant role in modern statistics. The course develops skills in writing and presenting statistics for general and specialist audiences.

This course comprehensively covers applied statistical methods with an emphasis on statistical modelling using maximum likelihood theory and with a view to enabling a statistical expert to provide comprehensible statistical assistance to non-statistical clients.ᅠTopics to be covered include extensions of linear models to generalized linear models (GLIMs) and generalized estimating equations,ᅠthe EM algorithm, finite mixture models, and bootstrap methods, Students will also be shown how to perform statistical modelling using R, a powerful commonly used programming languageᅠfor statistical modelling.

Course requirements

Assumed background

It will be assumed that the content of STAT3001 has been covered.ᅠ In addition, the material from the earlier courses STAT1201, STAT2003 and STAT2004 will also be assumed toᅠhave been covered.

Prerequisites

You'll need to complete the following courses before enrolling in this one:

STAT3001

Incompatible

You can't enrol in this course if you've already completed the following:

STAT3002

Course contact

Course staff

Lecturer

Tutor

Mr Nhat Pham

Timetable

The timetable for this course is available on the UQ Public Timetable.

Additional timetable information

Lectures are given on campus and streamed live. Recordings will be available via Blackboard.

Tutorials are scheduled for every week during the semester. Tutorial hours are to be usedᅠfor working on assignments,ᅠstatistical analyses and report preparation.ᅠ

Consult your personal timetable for times and locations. Students are expected to attend these sessions in person unless they have a valid reason for being unable to attend (such as illness).

Important: if you are ill, then do not attend any classes in person. Alternative arrangements can be organised – consult Blackboard for details. 

Aims and outcomes

The course aims to equip students with the knowledge and skills needed to perform statistical analyses on data with moderately complex distributional characteristics and sampling structure --- the type frequently encountered in scientific investigations across a broad spectrum of disciplines.

Learning outcomes

After successfully completing this course you should be able to:

LO1.

build an appropriate statistical model to represent the process generating the data.

LO2.

apply likelihood based methods to problems of estimation and hypothesis testing.

LO3.

apply regression diagnostics in order to verify model assumptions.

LO4.

recognise the limitations of linear models and generalized linear models.

LO5.

communicate the results of statistical analysis clearly and accurately.

LO6.

use the statistical package R appropriately for the analysis and graphical representation of data.

Assessment

Assessment summary

Category Assessment task Weight Due date
Paper/ Report/ Annotation Assignments 60% ,15% each

Assignment 1: 16/08/2024 2:00 pm

Assignment 2: 30/08/2024 2:00 pm

Assignment 3: 4/10/2024 2:00 pm

Assignment 4: 25/10/2024 2:00 pm

Examination, Participation/ Student contribution Final examination
  • Hurdle
40%

End of Semester Exam Period

2/11/2024 - 16/11/2024

A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.

Assessment details

Assignments

Mode
Written
Category
Paper/ Report/ Annotation
Weight
60% ,15% each
Due date

Assignment 1: 16/08/2024 2:00 pm

Assignment 2: 30/08/2024 2:00 pm

Assignment 3: 4/10/2024 2:00 pm

Assignment 4: 25/10/2024 2:00 pm

Task description

This assignment will be on work covered in the lectures up to the setting of the assignment.

Submission guidelines

Submission via Blackboard

Deferral or extension

You may be able to apply for an extension.

See ADDITIONAL ASSESSMENT INFORMATION for extension and deferred examination information relating to this assessment item.

 

Late submission

A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.

You are required to submit assessable items on time. If you fail to meet the submission deadline for any assessment item a penalty of 10% of the maximum possible mark allocated for the assessment item (or equivalent penalty for other grading schemes) will be deducted per day for up to 7 calendar days, at which point any submission will not receive any marks unless an extension has been approved. Each 24-hour block is recorded from the time the submission is due


Final examination

  • Hurdle
Mode
Written
Category
Examination, Participation/ Student contribution
Weight
40%
Due date

End of Semester Exam Period

2/11/2024 - 16/11/2024

Hurdle requirements

Student must get a mark worth 40% of total marks for the exam to achieve a pass in the course.

Exam details

Planning time 10 minutes
Duration 120 minutes
Calculator options

No calculators permitted

Open/closed book Closed Book examination - no written materials permitted
Exam platform Paper based
Invigilation

Invigilated in person

Submission guidelines

Deferral or extension

You may be able to defer this exam.

See ADDITIONAL ASSESSMENT INFORMATION for extension and deferred examination information relating to this assessment item.

Course grading

Full criteria for each grade is available in the Assessment Procedure.

Grade Description
1 (Low Fail)

Absence of evidence of achievement of course learning outcomes.

Course grade description: You will be able to partially analyse very few data settings. Written reports will be poor and accuracy in graphical and numerical work will be low. To earn a Grade of 1, you must merit a mark between 0-19%.

2 (Fail)

Minimal evidence of achievement of course learning outcomes.

Course grade description: To earn a Grade of 2, you must merit a mark between 20-44% by demonstrating some knowledge of the basic concepts of the course. You will be able to partially analyse a few important data settings. Written reports may be poor and accuracy in graphical and numerical work may be low.

3 (Marginal Fail)

Demonstrated evidence of developing achievement of course learning outcomes

Course grade description: To earn a Grade of 3, you must merit a mark between 45-49% by demonstrating some knowledge of the basic concepts of the course. You will be able to analyse important data settings. You will demonstrate the ability to write statistical reports and show accuracy in graphical and numerical work.

4 (Pass)

Demonstrated evidence of functional achievement of course learning outcomes.

Course grade description: To earn a Grade of 4, you mustmerit a mark between 50-64% by demonstrating an understanding of the basic concepts of the course. You will be able to analyse the important data settings, identifying some key assumptions that might affect the analysis. You will demonstrate the ability to write statistical reports and show accuracy in graphical and numerical work.

5 (Credit)

Demonstrated evidence of proficient achievement of course learning outcomes.

Course grade description: To earn a Grade of 5, you must merit a final mark between 65-74% by demonstrating an adequate understanding of the course material. You will be able to analyse many data settings, identifying the key assumptions that might affect the analysis. You will demonstrate the ability to write statistical reports and show accuracy in graphical and numerical work.

6 (Distinction)

Demonstrated evidence of advanced achievement of course learning outcomes.

Course grade description: To earn a Grade of 6, you must merit a mark between 75-84% by demonstrating a comprehensive understanding of the course material. You will be able to analyse most data settings, identifying important assumptions and other factors that might affect the analysis. You will demonstrate proficiency in communicating statistical ideas in writing and a high level of accuracy in graphical and numerical work.

7 (High Distinction)

Demonstrated evidence of exceptional achievement of course learning outcomes.

Course grade description: To earn a Grade of 7, you must merit a mark between 85-100% by demonstrating an excellent understanding of the course material. You will be able to analyse a broad range of data settings, providing insight and thoroughness in the form of necessary assumptions and other factors that might affect the analysis. You will demonstrate excellent proficiency in communicating statistical ideas in writing and a high level of accuracy in graphical and numerical work.

Supplementary assessment

Supplementary assessment is available for this course.

Should you fail a course with a grade of 3, you may be eligible for supplementary assessment. Refer to my.UQ for information on supplementary assessment and how to apply. 

Supplementary assessment provides an additional opportunity to demonstrate you have achieved all the required learning outcomes for a course.  

If you apply and are granted supplementary assessment, the type of supplementary assessment set will consider which learning outcome(s) have not been met. 

Supplementary assessment in this course will be a 2-hour examination similar in style to the end-of-semester examination. To receive a passing grade of 3S4, you must obtain a mark of 50% or more on the supplementary assessment. 

Additional assessment information

Artificial Intelligence

The assessment tasks in this course evaluate students’ abilities, skills and knowledge without the aid of Artificial Intelligence (AI) additional to what is covered in the course. Students are advised that the use of AI technologies beyond those in the course to develop responses is strictly prohibited and may constitute misconduct under the Student Code of Conduct.

It is your responsibility to check that assessment marks have been correctly entered on Blackboard > My Grades.  If you feel an error has been made in assessing your work, then contact the course co-ordinator.

Applications for Extensions to Assessment Due Dates

Extension requests are submitted online via my.UQ – applying for an extension. Extension requests received in any other way will not be approved. Additional details associated with extension requests, including acceptable and unacceptable reasons, may be found at my.UQ.

Please note:

  • Requests for an extension to an assessment due date must be submitted through your my.UQ portal and you must provide documentation of your circumstances, as soon as it becomes evident that an extension is needed. Your application must be submitted on or before the assessment item's due date and time.
  • Applications for extension can take time to be processed so you should continue to work on your assessment item while awaiting a decision. We recommend that you submit any completed work by the due date, and this will be marked if your application is not approved. Should your application be approved, then you will be able to resubmit by the agreed revised due date.
  • If an extension is approved, you will be notified via your my.UQ portal and the new date and time for submission provided. It is important that you check the revised date as it may differ from the date that you requested.
  • If the basis of the application is a medical condition, applications should be accompanied by a medical certificate dated prior to the assignment due date. If you are unable to provide documentation to support your application by the due date and time you must still submit your application on time and attach a written statement (Word document) outlining why you cannot provide the documentation. You must then upload the documentation to the portal within 24 hours.
  • If an extension is being sought on the basis of exceptional circumstances, it must be accompanied by supporting documentation (eg. Statutory declaration).
  • For extensions based on a SAP you may be granted a maximum of 7 days (if no earlier maximum timeframe applies). See the Extension or Deferral availability section of each assessment for details. Your SAP is all that is required as documentation to support your application. However, additional extension requests for the assessment item will require the submission of additional supporting documentation e.g., a medical certificate. All extension requests must be received by the assessment due date and time.
  • An extension for an assessment item due within the teaching period in which the course is offered, must not exceed four weeks in total. If you are incapacitated for a period exceeding four weeks of the teaching period, you are advised to apply for Removal of Course.
  • If you have been ill or unable to attend class for more than 14 days, you are advised to carefully consider whether you are capable of successfully completing your courses this semester. You might be eligible to withdraw without academic penalty - seek advice from the Faculty that administers your program.
  • Students may be asked to submit evidence of work completed to date. Lack of adequate progress on your assessment item may result in an extension being denied.
  • There are no provisions for exemption from an assessment item within UQ rules. If you are unable to submit an assessment piece then, under special circumstances, you may be granted an exemption, but may be required to submit alternative assessment to ensure all learning outcomes are met.

Applications to defer an exam

In certain circumstances you can apply to take a deferred examination for in-semester and end-of-semester exams. You'll need to demonstrate through supporting documentation how unavoidable circumstances prevented you from sitting your exam. If you can’t, you can apply for a one-off discretionary deferred exam.

Deferred Exam requests are submitted online via mySi-net. Requests received in any other way will not be approved. Additional details associated with deferred examinations, including acceptable and unacceptable reasons may be found at my.UQ.

Please note:

  • Applications can be submitted no later than 5 calendar days after the date of the original exam.
  • There are no provisions to defer a deferred exam. You need to be available to sit your deferred examination.
  • Your deferred examination request(s) must have a status of "submitted" in mySI-net to be assessed.
  • All applications for deferred in-semester examinations are assessed by the relevant school. Applications for deferred end-of-semester examinations are assessed by the Academic Services Division.
  • You’ll receive an email to your student email account when the status of your application is updated.
  • If you have a medical condition, mental health condition or disability and require alternative arrangements for your deferred exam you’ll need to complete the online alternative exam arrangements through my.UQ. This is in addition to your deferred examinations request. You need to submit this request on the same day as your request for a deferred exam or supplementary assessment. Contact Student Services if you need assistance completing your alternative exam arrangements request.

Learning resources

You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.

Library resources

Find the required and recommended resources for this course on the UQ Library website.

Additional learning resources information

Handouts relating to the lecture material will be available.

Learning activities

The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.

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Learning period Activity type Topic
Multiple weeks
Tutorial

Applied statistics with R

These tutorial sessions will cover lecture materials and demonstrate how to use the statistical package R in the analysis and graphical representation of data.

Lecture

Applied statistics

Series of lectures on the analysis of data with the focus on modelling and point estimation via likelihood-based methods.

Policies and procedures

University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:

Learn more about UQ policies on my.UQ and the Policy and Procedure Library.