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Unit of study_

BSTA5002: Principles of Statistical Inference

Semester 1, 2021 [Online] - Camperdown/Darlington, Sydney

The aim of this unit is to provide a strong mathematical and conceptual foundation in the methods of statistical inference, with an emphasis on practical aspects of the interpretation and communication of statistically based conclusions in health research. Content covered includes: review of the key concepts of likelihood and construction of Normal­theory confidence intervals; frequentist theory of estimation including hypothesis tests; methods of inference based on likelihood theory, including information and likelihood ratio; Wald and score tests; an introduction to the Bayesian approach to inference; an introduction to distribution­free statistical methods.

Unit details and rules

Unit code BSTA5002
Academic unit Public Health
Credit points 6
Prohibitions
? 
None
Prerequisites
? 
BSTA5023
Corequisites
? 
None
Assumed knowledge
? 

None

Available to study abroad and exchange students

No

Teaching staff

Coordinator Liz Barnes, liz.barnes@sydney.edu.au
Type Description Weight Due Length
Assignment Module 3 Exercise
One exercise for submission
3.34% Mid-semester break
Due date: 11 Apr 2021 at 23:59
Mar 29 - Apr 11
Outcomes assessed: LO4
Assignment Assignment 2
Multiple questions covering material from all modules (Modules 1-6)
40% STUVAC
Due date: 14 Jun 2021 at 23:59

Closing date: 18 Jun 2021
May 28 - Jun 14
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
Assignment Module 1 Exercise
One exercise for submission
3.33% Week 02
Due date: 14 Mar 2021 at 23:59
Mar 01 - Mar 14
Outcomes assessed: LO1
Assignment Module 2 Exercise
One exercise for submission
3.33% Week 04
Due date: 28 Mar 2021 at 23:59
Mar 15 - Mar 28
Outcomes assessed: LO2 LO3
Assignment Assignment 1
Multiple questions covering material in Modules 1-3
40% Week 07
Due date: 25 Apr 2021 at 23:59

Closing date: 09 May 2021
Apr 09 - Apr 25
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment Module 4 Exercise
One exercise for submission
3.33% Week 09
Due date: 07 May 2021 at 23:59
Apr 26 - May 07
Outcomes assessed: LO5
Assignment Module 5 Exercise
One exercise for submission
3.33% Week 11
Due date: 19 May 2021 at 23:59
May 08 - May 19
Outcomes assessed: LO6 LO7
Assignment Module 6 Exercise
One exercise for submission
3.34% Week 13
Due date: 31 May 2021 at 23:59
May 20 - May 31
Outcomes assessed: LO8

Assessment summary

6 submissible module exercises, of which the best 5 marks will each contribute 4% towards the total mark.

2 major assignments, each of which contributes 40% towards the total mark.

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.

* Your best five module marks will each contribute 4% towards the total of 20% for the module exercises

 

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.

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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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
Multiple weeks Module 1 - Likelihood Independent study (20 hr) LO1
Module 2 - Estimation methods Independent study (20 hr) LO2 LO3
Module 3 - Hypothesis testing concepts Independent study (20 hr) LO4
Module 4 - Hypothesis testing methods Independent study (20 hr) LO5
Module 5 - Bayesian inference Independent study (20 hr) LO6 LO7
Module 6 - Further inference topics Independent study (20 hr) LO8

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.

Required readings

TEXTBOOK:

Marchner, I.C.(2014). Inference Principles for Biostatisticians. Chapman and Hall / CRC. ISBN: 9781482222234.  http://www.crcpress.com/product/isbn/9781482222234

Note, a digital copy of this text may be available through the university library if you do not wish to purchase a copy. 

OTHER READINGS:

Sterne, J.A., Smith, G.D. (2001). Sifting the evidence-what's wrong with significance tests? BMJ, 322: 226-231.  http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1119478/

Reese, R.A. (2004). Does significance matter? Significance, 1: 39-40. http://onlinelibrary.wiley.com/doi/10.1111/j.1740-9713.2004.00009.x/pdf

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. Write a likelihood function.
  • LO2. Derive and calculate the maximum likelihood estimate.
  • LO3. Derive and calculate the expected information.
  • LO4. Calculate and interpret p-values, power and confidence intervals correctly.
  • LO5. Derive a Wald test, Score test and likelihood ratio test.
  • LO6. Use a Bayesian approach to derive a posterior distribution.
  • LO7. Calculate and interpret posterior probabilities and credible intervals.
  • LO8. Apply and explain an exact method, non-parametric and sampling-based method.

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.

The unit is regularly updated in response to student feedback. Since Semester 1 2019 the exercises have been altered to clarify cross-referencing between exercises and material referred to in previous modules. We have introduced recorded video lectures to complement the textbook readings, recorded worked video solutions to the non-assessed module exercises to further reinforce concepts, and will be providing the opportunity for live consultation (either in the form of tutorial or Q&A sessions, depending on demand) via videoconferencing to increase engagement and interactivity with the teaching team.

Required Mathematical Background: Students should be familiar with the mathematical background covered as part of MBB, including basic factorisation, rules for exponents and natural logarithms, differentiation and partial differentiation, and basic matrix manipulations (e.g., inverse of a matrix).

Disclaimer

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

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