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Unit outline_

BSTA5023: Probability and Distribution Theory

Semester 1, 2022 [Online] - Camperdown/Darlington, Sydney

This unit will focus on applying the calculus-based techniques learned in Mathematical Background for Biostatistics (MBB) to the study of probability and statistical distributions. These two units, together with the subsequent Principles of Statistical Inference (PSI) unit, will provide the core prerequisite mathematical statistics background required for the study of later units in the Graduate Diploma or Masters degree. Content: This unit begins with the study of probability, random variables, discrete and continuous distributions, and the use of calculus to obtain expressions for parameters of these distributions such as the mean and variance. Joint distributions for multiple random variables are introduced together with the important concepts of independence, correlation and covariance, marginal and conditional distributions. Techniques for determining distributions of transformations of random variables are discussed. The concept of the sampling distribution and standard error of an estimator of a parameter is presented, together with key properties of estimators. Large sample results concerning the properties of estimators are presented with emphasis on the central role of the Normal distribution in these results. General approaches to obtaining estimators of parameters are introduced. Numerical simulation and graphing with Stata is used throughout to demonstrate concepts.

Unit details and rules

Academic unit Public Health
Credit points 6
Prerequisites
? 
BSTA5001
Corequisites
? 
None
Prohibitions
? 
BSTA5100
Assumed knowledge
? 

None

Available to study abroad and exchange students

No

Teaching staff

Coordinator Erin Cvejic, erin.cvejic@sydney.edu.au
Type Description Weight Due Length
Assignment Assignment 1
Written assessment
40% Week 07 8-10 pages
Outcomes assessed: LO3 LO1 LO2
Assignment Assignment 2
Written assessment
60% Week 13 12-15 pages
Outcomes assessed: LO1 LO2 LO3 LO4 LO5

Assessment summary

  • Assignment 1 will require students to complete numerical and short response questions covering Topics 1 to 6
  • Assignment 2 will require students to complete numerical and short respond questions covering all Topics (1 to 12)

Additional assessment information will be provided on eLearning

Assessment criteria

Grade

Mark Range

Description

AF

Absent fail

Range from 0 to 49

To be awarded to students who fail to demonstrate the learning outcomes for the unit at an acceptable standard through failure to submit or attend compulsory assessment tasks or to attend classes to the required level. In cases where a student receives some marks but fails the unit through failure to attend or submit a compulsory task, the mark entered shall be the marks awarded by the faculty up to a maximum of 49. This grade should not be used in cases where a student attempts all assessment tasks but fails to achieve a mandated minimum standard in one or more task. In such cases a Fail (FA) grade and a mark less than 50 should be awarded.

FA

Fail

Range from 0 to less than 50

To be awarded to students who, in their performance in assessment tasks, fail to demonstrate the learning outcomes for the unit at an acceptable standard established by the faculty. This grade, with corresponding mark, should also be used in cases where a student fails to achieve a mandated standard in a compulsory assessment, thereby failing to demonstrate the learning outcomes to a satisfactory standard.

PS

Pass

Range from 50 to less than 65

To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an acceptable standard

CR

Credit

Range from 65 to less than 75

To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a good standard

D

Distinction

Range from 75 to less than 85

To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a very high standard

HD

High distinction

Range from 85 to 100 inclusive

To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an exceptional standard

 

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

The standard BCA policy for late penalties for submitted work is a 5% deduction from the earned mark for each day the assessment is late, up to a maximum of 10 days (including weekends and public holidays).

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 Experiments, sample spaces, Probability Rules, Permutations and Combinations Independent study (10 hr) LO1
Week 02 Conditional Probability, Independence, Bayes’ Theorem Independent study (10 hr) LO1
Week 03 Random Variables, Probability Functions, Discrete Probability Distributions, Cumulative Distribution functions, Expected value and Variance, Moments Independent study (10 hr) LO2
Week 04 Important Discrete Distributions: Bernoulli, Binomial, Geometric, and Poisson. Independent study (10 hr) LO2 LO3
Week 05 Moment generating functions. Discrete Distributions: Negative Binomial and Hypergeometric. Independent study (10 hr) LO2 LO3
Week 06 Introduction to Continuous random variables. Cumulative distribution function Independent study (10 hr) LO2 LO3
Week 07 Continuous Distributions: Uniform, Exponential. Independent study (10 hr) LO2 LO3
Week 08 Normal distribution Independent study (10 hr) LO2 LO3
Week 09 Continuous Distributions: Gamma and Beta Distributions. Chebyshev’s Theorem Independent study (10 hr) LO2 LO3
Week 10 Sampling distributions Independent study (10 hr) LO1 LO2 LO3 LO4
Week 11 Joint Distributions: Discrete and Continuous cases Independent study (10 hr) LO1 LO4
Week 12 Introduction to stochastic processes. Markov Chains. Independent study (10 hr) LO1 LO5

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. Analyse probability and conditional probability of an event by applying a probabilistic model for an experiment
  • LO2. Apply a range of strategies to find and interpret the moments of discrete and continuous random variables including their expected values and variances
  • LO3. Apply the Law of Large Numbers (LLN) and the Central Limit Theorem (CLT) to find asymptotic distribution of a sample mean
  • LO4. Analyse a bivariate probability distribution to find and interpret corresponding covariances, correlations, marginal and conditional probability distributions
  • LO5. Apply Markov Chain (MC) theory to practical problems and tasks

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

PDT was last delivered by Monash University in Semester 2 2021. The current offering was developed by Georgy Sofronov and Houying Zhu from Macquarie University, and is the first time this version of the unit is being delivered into the BCA.

This unit is externally delivered through the Biostatistics Collaboration of Australia (BCA).

This is a “legacy” unit made available via teach-out arrangement and delivered by Macquarie University only for students who are tranisitioning between the old (pre-2022) and new (2022) BCA program structures who have completed BSTA5001 (and therefore are not eligible for BSTA5100).

This unit will be delivered via the Macqurie University eLearning platform (iLearn) and timetable. 

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.