STAT4022: Semester 1, 2021

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Units STAT4022 Semester 1 2021 [Normal day]

Unit of study_

STAT4022: Linear and Mixed Models

Semester 1, 2021 [Normal day] - Camperdown/Darlington, Sydney

Overview

Classical linear models are widely used in science, business, economics and technology. This unit will introduce the fundamental concepts of analysis of data from both observational studies and experimental designs using 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, randomization and ideas of factorial designs. Throughout the course you will use the R statistical package to give analyses and graphical displays. This unit includes material in STAT3022, but has an additional component on the mathematical techniques underlying applied linear models together with proofs of distribution theory based on vector space methods.

Unit details and rules

Unit code	STAT4022
Academic unit	Mathematics and Statistics Academic Operations
Credit points	6
Prohibitions ?	STAT3012 or STAT3912 or STAT3022 or STAT3922 or STAT3004 or STAT3904.
Prerequisites ?	None
Corequisites ?	None
Assumed knowledge ?	Material in DATA2X02 or equivalent and MATH1X02 or equivalent; that is, a knowledge of applied statistics and an introductory knowledge to linear algebra, including eigenvalues and eigenvectors.
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Jennifer Chan, jennifer.chan@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Take-home short release)	Final Exam Final examination	60%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Assignment 1 Answers with calculations. Submitted through Turnitin	5%	Mid-semester break Due date: 06 Apr 2021 at 23:59	Variable
Outcomes assessed:
Online task	Online Quiz 1 Online Quiz 1 during Thursday lecture (10am-11am)	10%	Week 04 Due date: 25 Mar 2021 at 09:55	50 minutes
Outcomes assessed:
Online task	Online Quiz 2 Online Quiz 2 during Thursday lecture (10am-11am)	10%	Week 10 Due date: 13 May 2021 at 09:55	50 mnutes
Outcomes assessed:
Online task	Advanced Quiz Advanced Quiz during Thursday lecture (1pm-2pm)	10%	Week 13 Due date: 03 Jun 2021 at 13:05	50 minutes
Outcomes assessed:
Assignment	Assignment 2 Answers with calculations. Submitted through Turnitin	5%	Week 13 Due date: 01 Jun 2021 at 23:59	Variable
Outcomes assessed:
= Type D final exam ?

Assessment summary

Detailed information for each assessment can be found on Canvas.

Assessment criteria

Resultname	Markrange	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.

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.

Learning support

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.

Weekly schedule

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

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

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.

Outcomes
Graduate qualities

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

LO1. Formulate, interpret and compare multiple types of linear regression and making inferences on all parameters of the model.
LO2. Construct, interpret, and apply multi-strata ANOVA tables
LO3. Explain the theoretical aspects of linear models and linear mixed models.
LO4. Design and explain appropriate schemes and analysis for treatment allocation and data collection in common experimental designs.
LO5. Identify and explain important features of experimental designs.
LO6. Apply, formulate and interpret linear mixed models.
LO7. Devise an experimental design or modelling approach to solve a problem and communicate the outcomes using the statistical programming language R.
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
Learning outcomes

Responding to student feedback

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

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%.

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.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

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