Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Study level
- Postgraduate Coursework
- Location
- External
- Attendance mode
- Online
- Units
- 2
- Administrative campus
- Herston
- Coordinating unit
- Public Health School
The aim of this course is to lay the foundation of biostatistical modelling to analyse data from randomised or observational studies. These skills are essential for biostatistics in practice and will be used by students for the remainder of their BCA studies. This unit will introduce the motivation for different regression analyses and how to choose an appropriate modelling strategy. This unit will teach how to use linear regression to analyse continuous outcomes and logistic regression for binary outcomes.
Emphasis will be placed on interpretation of results and checking the model assumptions. Stata and R software will be used to apply the methods to real study datasets.
The focus of this course will be on developing, validating and interpreting multivariable linear and logistic models. Emphasis will be placed on interpretation of results and checking the model assumptions. We also aim to provide a balance between theory and practice: mathematical proofs are not emphasised but mathematical literacy is promoted to both establish a solid grounding in the main concepts and to enable students to build on the basic material covered here when needed with independent study. This course, combined withᅠSTAT7619 Regression Modelling 2, provides the core prerequisite knowledge in statistical regression modelling.
Course requirements
Prerequisites
You'll need to complete the following courses before enrolling in this one:
STAT7617 or (STAT7601 + STAT7614)
Recommended prerequisites
We recommend completing the following courses before enrolling in this one:
STAT7604
Companion or co-requisite courses
You'll need to complete the following courses at the same time:
STAT7604
Incompatible
You can't enrol in this course if you've already completed the following:
STAT7607
Course contact
Course staff
Lecturer
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
The aim of this course is to lay the foundation of biostatistical modelling to analyse data from randomised or observational studies.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Explain the motivations for different regression analyses and be able to select and apply a suitable modelling approach based on the research aim
LO2.
Analyse data using normal linear models, and be able to assess model fit and diagnostics
LO3.
Analyse data using logistic regression models for binary data, and be able to assess model fit and diagnostics
LO4.
Accurately interpret and manipulate mathematical equations that relate to regression analysis
LO5.
Effectively communicate the outcomes and justification of a regression analysis
Assessment
Assessment summary
Category | Assessment task | Weight | Due date |
---|---|---|---|
Paper/ Report/ Annotation, Presentation |
Written report - Weeks 1 - 4
|
30% |
2/09/2024
Refer to Study Guide |
Paper/ Report/ Annotation |
Written Report - Weeks 1 - 8
|
30% |
7/10/2024
Refer to Study Guide |
Paper/ Report/ Annotation |
Written Report - Weeks 1 - 12
|
40% |
4/11/2024
Refer to Study Guide |
Assessment details
Written report - Weeks 1 - 4
- Online
- Mode
- Written
- Category
- Paper/ Report/ Annotation, Presentation
- Weight
- 30%
- Due date
2/09/2024
Refer to Study Guide
Task description
Refer to Study Guide
Submission guidelines
Deferral or extension
You may be able to apply for an extension.
Please see 10. Policies & Guidelines
Written Report - Weeks 1 - 8
- Online
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 30%
- Due date
7/10/2024
Refer to Study Guide
Task description
Refer to Study Guide
Submission guidelines
Deferral or extension
You may be able to apply for an extension.
Please see 10. Policies & Guidelines
Written Report - Weeks 1 - 12
- Online
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 40%
- Due date
4/11/2024
Refer to Study Guide
Task description
Refer to Study Guide
Submission guidelines
Deferral or extension
You may be able to apply for an extension.
Please see 10. Policies & Guidelines
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: (typically 0 - 19) |
2 (Fail) |
Minimal evidence of achievement of course learning outcomes. Course grade description: (typically 20 - 44) |
3 (Marginal Fail) |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: (typically 45 - 49) |
4 (Pass) |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: (typically 50 - 64) |
5 (Credit) |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: (typically 65 - 74) |
6 (Distinction) |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: (typically 75 - 84) |
7 (High Distinction) |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: (typically 85 - 100) |
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Supplementary assessment: The final grade awarded will be based on the results of the supplementary assessment only, and a passing grade will be awarded if, and only if, the student receives at least 50% of the marks on the supplementary assessment.
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
For this subject you can use either the Stata statistical package or R. If using Stata, the notes assume the use of release 12 or later of Stata. Most of the commands we use should work fine in older versions (as long as they are not too old!), although there was an important change relevant to RM1 with the introduction of “factor variables” in Stata 12. Importantly, whichever version you are using, please ensure that you have performed the online update to the latest update of that version. (Use the command update query.) The notes for this course show both R and Stata code whenever possible.
The textbook for this unit shows Stata code only, and so relevant equivalent code for R is shown in the notes.ᅠ
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
Learning period | Activity type | Topic |
---|---|---|
Tutorial |
Module Week 1 Simple linear regression Refer to Study Guide |
|
Tutorial |
Module Week 2 Checking assumptions in linear regression |
|
Tutorial |
Module Week 3 Binary covariates, outliers and influential observations |
|
Tutorial |
Module Week 4 Multiple linear regression application |
|
Tutorial |
Module Week 5 Multiple linear regression theory |
|
Tutorial |
Module Week 6 Interaction and multicollinearity |
|
Tutorial |
Module Week 7 Assumption violations |
|
Tutorial |
Module Week 8 Linear regression model building |
|
Tutorial |
Module Week 9 Logistic regression |
|
Tutorial |
Module Week 10 Confounding and interaction in logistic regression |
|
Tutorial |
Module Week 11 Checking assumptions in logistic regression |
|
Tutorial |
Module Week 12 Logistic regression model building |
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:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.
Course guidelines
School of Public Health (SPH) Guidelines for late submission of progressive assessment - Preamble
To apply for an extension to the due date for a piece of progressive assessment (eg assignments, oral presentations and computer-based assignments) students should complete the online request at https://my.uq.edu.au/node/218/1
Information regarding deferral of in-semester exams and end-of-semester exams is available from https://my.uq.edu.au/information-and-services/manage-my-program/exams-and-assessment/deferring-exam
If requesting an extension on medical grounds, a medical certificate must be provided. The extension will be approved for the number of days included in the medical certificate that the student was not fit to study or work, eg if the medical certificate is for 3 days, an extension will be approved for 3 days maximum regardless of the student's request.
If requesting an extension using a Student Access Plan for Disability (SAPD) as evidence, a maximum of 7-day extension will be approved in the first instance. Updated medical documentation, as well as a copy of the SAPD, is required if requesting an extension for more than 7 days.
The maximum time for an in-semester extension is four weeks.
The following SPH guidelines are consistent with the UQ policy. However, the SPH Guidelines contain specific rules and interpretations for SPH courses, and requests for extension and penalties for late submissions will be judged according to the guidelines outlined in this document. You should read both the information in your my.UQ at the following link: https://my.uq.edu.au/information-and-services/manage-my-program/exams-and-assessment/applying-assessment-extension?p=1#1 and the SPH guidelines (below) before submitting a request for an extension. The SPH Guidelines apply to all courses offered by the School of Public Health unless the ECP explicitly states otherwise.
SPH Guidelines for late submission of progressive assessment
Initial extension for an individual item of assessment – the SPH Teaching & Assessment Support Team and/or the Course Coordinator decides.
This could be for medical or compassionate reasons, or if, in the opinion of the Course Coordinator, there are exceptional circumstances.
Acceptable and unacceptable reasons for an extension are listed at the following link, along with the required evidence to be provided: https://my.uq.edu.au/information-and-services/manage-my-program/exams-and-assessment/applying-assessment-extension?p=1#1
All requests should be lodged at least 24 hours prior to the due date for the assessment.
If applying for an extension after the due date and time of the assessment item, your request may not be approved. An explanation as to why your request was not submitted prior must be included.
If approved, a new due date will be set. This would generally be no later than 7 days after the original due date, however this can be modified to take account of the circumstances of the request and the time that would have been lost from studies.
If the new due date is past the date for submission of end-of-semester results, the student will receive an INC (incomplete) result.
Second and all subsequent extensions for an individual item of assessment – the SPH Teaching & Assessment Support Team and/or the Program Director together with the Course Coordinator decides.
This would only be approved for exceptional circumstance with supporting documentation.
- Online requests must be made at least 24 hours prior to the due date from the first extension.
- The SPH Teaching & Assessment Support Team and/or the Course Coordinator will consult with the Program Director, who will make the final decision.
- If approved, the new due date would generally be no later than 7 days after the first extension due date.
- The Program Director should consider if remedial or other support should be offered to the student.
- The Program Director should provide a report on these matters as needed at SPH Examiners’ Meetings.
Please Note: In order to support course progression, extensions that total more than 14 calendar days from the original due date of an assessment item will only be approved in very exceptional circumstances. These requests are assessed and approved or denied on a case-by-case basis.
If you have been ill or unable to attend class for more than 14 days, we advise you to carefully consider whether you are capable of successfully completing your courses this semester. You might be eligible to withdraw without academic penalty.
Penalty for late submission
Submission of assignments, practical reports, workbooks, and other types of written assessments after the due date specified in the Electronic Course Profile (ECP) will receive a penalty.
The penalty will be a deduction of 10% RELATIVE PERCENTAGE per day (24 hour period or part thereof, including weekends and public holidays) or for work graded on a 1-7 scale, a deduction of one grade per day, e.g If the original mark is 73%, then 10% relative percentage is 10% of this value, ie 7.3%, The final mark for this assessment item after applying the penalty for 1 day late submission would be 73 -7.3 = 65.7% The same outcome is achieved by multiplying the original score by .9; ie 73 x .9 = 65.7%
The penalty for multiple days late is the relative percentage multiplied by the number of days late.
A submission that is not made within 10 days of the due date will receive a mark of 0% for that assessment item.
Where a student has sought more than one extension, the due date for calculating the penalty will be the due date for the most recently approved extension.
Submission of Medical Certificates
Students are responsible for ensuring that any medical documentation they submit is authentic and signed by a registered medical practitioner. Such practitioners can be identified via the AHPRA website. Also note that:
- Not all online medical services are staffed by registered practitioners
- If the registration status of the practitioner cannot be verified, then an alternative practitioner should be sought
- Students will be held fully responsible for all documentation they submit, even if done so in ignorance of the practitioner's registration status
Medical documentation may be subjected to an audit by the University.
SPH Assessment Guidelines
The School of Public Health assessment tasks have been designed to be challenging, authentic and complex. While students may us AI technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference AI use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI tools.