Skip to main content
Unit of study_

STAT3022: Applied Linear Models

Linear models are core to a wide range of real-world data analyses, for example in agriculture, health, sport and business. This unit provides an in-depth exploration of various linear models outlining when they can be applied, and how to assess if they are appropriate. The 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 consider linear models and robust regression methods with diagnostics for checking appropriateness of models and strategies for performing feature selection. You will learn to design and analyse experiments considering notions of replication, randomization and ideas of factorial designs. You will apply, construct and interpret multi-way ANOVA models and make inferences, including post-hoc tests and making corrections for multiple comparisons. Throughout the unit you will use the R statistical package to perform analyses and generate statistical graphics. By completing this unit you will learn how to generate, interpret, visualise and critique linear models.

Details

Academic unit Mathematics and Statistics Academic Operations
Unit code STAT3022
Unit name Applied Linear Models
Session, year
? 
Semester 1, 2021
Attendance mode Normal day
Location Remote
Credit points 6

Enrolment rules

Prohibitions
? 
STAT3912 or STAT3012 or STAT3922
Prerequisites
? 
STAT2X11 and (DATA2X02 or STAT2X12)
Corequisites
? 
None
Available to study abroad and exchange students

Yes

Teaching staff and contact details

Coordinator Jennifer Chan, jennifer.chan@sydney.edu.au
Type Description Weight Due Length
Final exam (Take-home short release) Type D final exam Final examination
Final examination
70% Formal exam period 2 hours
Outcomes assessed: LO1 LO7 LO6 LO5 LO4 LO3 LO2
Assignment Assignment 1
Answers with calculations. Submitted through Turnitin
5% Mid-semester break
Due date: 06 Apr 2021
Variable
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Online task Online quiz 1
Online quiz during week 4 Thur lecture
10% Week 04
Due date: 25 Mar 2021

Closing date: 03 Mar 2021
50 minutes
Outcomes assessed: LO1 LO2 LO4
Online task Online quiz 2
Online quiz during week 10 Thur lecture
10% Week 10
Due date: 13 May 2021

Closing date: 13 May 2021
50 minutes
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Assignment Assignment 2
Answers with calculations. Submitted through Turnitin
5% Week 13
Due date: 01 Jun 2021
Variable
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Type D final exam = Type D final exam ?

Detailed information for each assessment can be found on Canvas.

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 sydney.edu.au/students/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.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

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

WK Topic Learning activity Learning outcomes
Week 01 R Programming basics, simple linear regression Lecture (3 hr)  
Week 02 Model diagnostics, inference for linear regression, fitting multiple linear regression models Lecture and tutorial (4 hr)  
Week 03 Inference for multiple regression models, multiple correlation coefficients, Leverage and Cook’s distance, the general F-test Lecture and tutorial (4 hr)  
Week 04 Subset selection using stepwise procedures and AIC, Cp and BIC Lecture and tutorial (4 hr)  
Week 05 Polynomial regression, orthogonal polynomials, Robust regression, 1-way ANOVA Lecture and tutorial (4 hr)  
Week 06 Simultaneous CIs, decomposing sums of squares Lecture and tutorial (4 hr)  
Week 07 Quantitative factors, 2-way ANOVA, interactions Lecture and tutorial (4 hr)  
Week 08 2-way ANOVA with interactions, normality tests Lecture and tutorial (4 hr)  
Week 09 Experimental design, randomised complete block designs, Latin square designs Lecture and tutorial (4 hr)  
Week 10 Incomplete block designs, analysis of covariance, nested factors Lecture and tutorial (4 hr)  
Week 11 Nested designs, random effect model Lecture and tutorial (4 hr)  
Week 12 Variance component estimation, mixed effects models, longitudinal data Lecture and tutorial (4 hr)  
Week 13 Agricultural data, hierarchical data, revision Lecture and tutorial (4 hr)  
Weekly Computer lab on weekly topic Computer laboratory (1 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7

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.

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
Change the weighing of quiz and assessment due to reducing 2 quizzes to 1 and each assignment before week 6 can account for at most 5%.

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.