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Unit outline_

STAT3922: Applied Linear Models (Advanced)

Semester 1, 2022 [Normal day] - Remote

This unit will introduce the fundamental concepts of analysis of data from both observational studies and experimental designs using classical linear methods, together with concepts of collection of data and design of experiments. You will first consider linear models and regression methods with diagnostics for checking appropriateness of models, looking briefly at robust regression methods. Then you will consider the design and analysis of experiments considering notions of replication, randomisation and ideas of factorial designs. Throughout the course you will use the R statistical package to give analyses and graphical displays. This unit is essentially an Advanced version of STAT3012, with additional emphasis on the mathematical techniques underlying applied linear models together with proofs of distribution theory based on vector space methods.

Unit details and rules

Academic unit Mathematics and Statistics Academic Operations
Credit points 6
Prerequisites
? 
STAT2X11 and [a mark of 65 or greater in (STAT2X12 or DATA2X02)]
Corequisites
? 
None
Prohibitions
? 
STAT3912 or STAT3012 or STAT3022 or STAT4022
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Linh Nghiem, linh.nghiem@sydney.edu.au
Type Description Weight Due Length
Final exam (Take-home short release) Type D final exam Final examination
Final examination
50% Formal exam period 2 hours
Outcomes assessed: LO8 LO1 LO2 LO3 LO4 LO5 LO6 LO7
Assignment Advanced Assignment
Answers with calculations. Submitted through Turnitin
10% Mid-semester break
Due date: 22 Apr 2022 at 23:59

Closing date: 22 Apr 2022
Variable
Outcomes assessed: LO8 LO7 LO6 LO5 LO4 LO3 LO2 LO1
Online task Online Quiz 1
Online Quiz 1 during Thursday lecture
10% Week 04 50 minutes
Outcomes assessed: LO1 LO4 LO2
Online task Advanced Online Quiz 2
Online Quiz 2 during Thursday lecture
10% Week 10 50 minutes
Outcomes assessed: LO8 LO7 LO6 LO5 LO4 LO3 LO2 LO1
Assignment group assignment Advanced Project
Project with report and/or presentation
20% Week 13
Due date: 29 May 2022 at 23:59

Closing date: 29 May 2022
Variable
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
group assignment = group assignment ?
Type D final exam = Type D final exam ?

Assessment summary

Detailed information for each assessment can be found on Canvas.

Final exam: If a second replacement exam is required, this exam may be delivered via an alternative assessment method, such as a viva voce (oral exam). The alternative assessment will meet the same learning outcomes as the original exam. The format of the alternative assessment will be determined by the unit coordinator. 

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

Representing complete or close to complete mastery of the material.

Distinction

75 - 84

Representing excellence, but substantially less than complete mastery.

Credit

65 - 74

Representing a creditable performance that goes beyond routine knowledge and understanding, but less than excellence.

Pass

50 - 64

Representing at least routine knowledge and understanding over a spectrum of topics and important ideas and concepts in the course.

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory 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.

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 R Programming basics, simple linear regression Lecture (3 hr)  
Week 02 Review of Linear algebra, fitting multiple linear regression models Lecture and tutorial (4 hr)  
Week 03 Inference for multiple regression model, extra sum of square Lecture and tutorial (4 hr)  
Week 04 Model diagnostics, leverage, outliers and influential analysis; multicollinearity Lecture and tutorial (4 hr)  
Week 05 Model evaluation, variable selection, automated search procedure Lecture and tutorial (4 hr)  
Week 06 Polynomial regression, additive and interaction models, models with qualitative variables Lecture and tutorial (4 hr)  
Week 07 One-way and two-way ANOVA models, multiple comparison Lecture and tutorial (4 hr)  
Week 08 Basic principles of experimental design, complete and fractional factorial designs Lecture and tutorial (4 hr)  
Week 09 Block designs: complete, incomplete block designs, Latin-square designs Lecture and tutorial (4 hr)  
Week 10 Analysis of covariance, nested factors, nested designs Lecture and tutorial (4 hr)  
Week 11 Fixed and random effects, variance component estimation Lecture and tutorial (4 hr)  
Week 12 Mixed effects models Lecture and tutorial (4 hr)  
Week 13 Longitudional data, revision Lecture and tutorial (4 hr)  
Weekly Computer lab on weekly topic Computer laboratory (1 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 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.

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. apply, formulate, interpret and compare multiple linear regression including evaluation of model diagnostics and outlier detection
  • LO2. apply, construct and interpret multi-way ANOVA models and make inference on all parameters
  • LO3. conduct and master correction for multiple pairwise comparisons by applying the Tukey, Scheffe and Bonferroni correction
  • LO4. perfectly calculate and interpret confidence intervals for all parameters in linear regression and distinguish the difference between confidence intervals and prediction intervals
  • LO5. implement the R function lmer for the fitting of mixed models and explain these complicated models
  • LO6. design of an appropriate scheme for treatment allocation and data collection as well as the correct analysis for complete randomised designs (CBD), randomised CBD (RCBD), Latin square designs (LSD), incomplete block designs (IBD) and balanced IBD (BIBD), ANCOVAs, and nested designs
  • LO7. identify and explain blocks, nested factors, interactions terms, experimental units, observational units, confounding and pseudo-replication in experimental designs
  • LO8. derive and re-create proofs of theoretical aspects of regression methods.

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.

Final examination is down-weighted to 50%, a new assessment (Project) is added and weighted 20%.

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

General Laboratory Safety Rules

  • No eating or drinking is allowed in any laboratory under any circumstances
  • A laboratory coat and closed-toe shoes are mandatory
  • Follow safety instructions in your manual and posted in laboratories
  • In case of fire, follow instructions posted outside the laboratory door
  • First aid kits, eye wash and fire extinguishers are located in or immediately outside each laboratory
  • As a precautionary measure, it is recommended that you have a current tetanus immunisation. This can be obtained from University Health Service: unihealth.usyd.edu.au/

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.