MAB141 Mathematics and Statistics for Medical Science

Unit Outline: Semester 2 2024, Gardens Point, Internal

Overview

This introductory unit is designed to meet the mathematical and statistical requirements of medical science students, particularly students enrolled in Vision Science (OP45). Approximately one quarter of the unit focuses on the mathematical foundations for techniques used in manipulating medical science laboratory data. The remainder of the unit considers a range of relevant statistical techniques, addressing concepts such as which analysis methods may be appropriate for testing a given research hypothesis, how the choice of analysis method is affected by the available data and how to interpret the outcome of the formal analysis. This unit will provide you with an essential foundation in the mathematical and statistical concepts and data analysis methods that will be used in later medical science units.

Learning Outcomes

On successful completion of this unit you will be able to:

1. Independently demonstrate your knowledge and understanding of a range of standard basic methods in calculus and statistical science, and in what contexts it is appropriate to apply them.
2. Identify, apply and justify appropriate quantitative methods to analyse quantitative data and solve quantitative problems in a range of contexts, using appropriate software where required.
3. Understand the importance in medical sciences, of responsible and accountable collection, analysis and reporting of data and variation.

Content

Learning Approaches

Prescribed readings and recorded lectures will precede the practical classes and will provide the basis for the examples and exercises covered in practical classes. Interactive lecture classes will also be scheduled, to allow students to raise queries on the readings and the lecture materials and to allow further exposition of this material. These interactive classes will also include worked examples and examples for students to work, in addition to discussion and exposition of the preparatory materials. Practical classes will comprise further homework review, one-to-one discussion on progress with homework and lecture and reading materials, and guided use of statistical computing packages to analyse data. Regular assignments will focus your reading in advance of lectures. Worked solutions to homework will enable you to have timely feedback on that aspect of your private study. Practical work will synthesise concepts, techniques, and skills discussed in lectures. Project work will enable you to bring together topics in this unit through investigation, analysis and reporting, problem-solving and communication skills.

Feedback on Learning and Assessment

Formative feedback will be provided for the in-semester assessment items by way of written comments on the assessment items, student perusal of the marked assessment piece and informal interview as required.

Summative feedback will be provided throughout the semester with progressive posting of results via Canvas.

Assessment

Overview

The assessment items in this unit are designed to determine your level of competency in meeting the unit outcomes while providing you with a range of tasks with varying levels of skill development and difficulty. The range of assessment types reflects the relative importance of statistical problem solving, knowledge of relevant basic content, and skills in applying data analytical methods.

Unit Grading Scheme

7- point scale

Assessment Tasks

Assessment: Mid-Semester Exam

This mid-semester exam is for the mathematics section of the unit. Lecture examples, practical class exercises, homework, and practice quizzes form exemplars for this assessment item. Feedback is available through review of marked exam papers on request.

The late submission period does not apply, and no extensions are available. if you can’t attend this exam due to special circumstances, you may apply to sit a deferred exam.

Assessment: Project (applied)

Statistical data analysis project involving identification of questions of interest; planning, organising, handling of data; exploration, presentation, analysis of data; reporting in context.

This assignment is eligible for the 48-hour late submission period and assignment extensions.

Assessment: Final Exam

This final exam is for the statistics section of the unit. Lecture examples, practical class exercises, homework, and practice quizzes form exemplars for the assessment item. Feedback is available through review of marked exam papers on request.

Academic Integrity

Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.

Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.

You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.

Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.

Resources

Risk Assessment Statement

There are no out-of-the-ordinary risks associated with this unit.

Course Learning Outcomes

OP45 Bachelor of Vision Science

1. Apply critical thinking and knowledge of vision science, ocular anatomy and clinical methods to generate solutions in clinical and scientific settings
   Relates to: Mid-Semester Exam, Project (applied)
2. Apply clinical or technical skills to conduct a safe and effective assessment, data collection, experimental protocol
   Relates to: Final Exam