Unit outline_

# OLET5608: Linear Modelling

## Overview

Linear models form the bedrock of many real-world data analyses. They are versatile, interpretable and easily implemented. This unit provides an overview of two of the most common methods of statistical analysis of data: analysis of variance and regression. You will generate data visualisation and diagnostics plots to interpret and discover the limitations of linear models and identify when more complex approaches may be needed. You will learn to code your analyses and perform reproducible research using the open source statistical package R. A key component of this unit involves generating visualisations, estimating and selecting appropriate linear models using your data. By doing this unit you will learn how to generate, interpret, visualise, discover and critique linear models applied to your original research.

### Unit details and rules

Academic unit Mathematics and Statistics Academic Operations 2 None None DATA2002 or DATA2902 or ENVX2001 Exploratory data analysis, sampling, simple linear regression, t-tests and confidence intervals. Ability to perform data analytics with coding, basic linear algebra. E.g. DATA1001 and OLET5606 (Data wrangling) Yes

### Teaching staff

Coordinator Garth Tarr, garth.tarr@sydney.edu.au

## Assessment

Type Description Weight Due Length
Tutorial quiz Quiz 1
Online quiz
15% Week 02
Due date: 29 Apr 2022 at 23:59
1 hour
Outcomes assessed:
Tutorial quiz Quiz 2
Online quiz
15% Week 03
Due date: 06 May 2022 at 23:59
1 hour
Outcomes assessed:
Presentation Presentation
Oral presentation
30% Week 05
Due date: 19 May 2022 at 15:00
15 minutes
Outcomes assessed:
Assignment Final report
Final report
40% Week 06
Due date: 27 May 2022 at 23:59
10 pages
Outcomes assessed:

### Assessment summary

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.

### 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.

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.

## 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 Learning outcomes
- When to use a linear model Online class (1 hr)
Estimating a linear model Online class (1 hr)
Assumption checking and diagnostics Online class (1 hr)
Categorical predictors Online class (1 hr)
Interaction terms Online class (1 hr)
Inference in linear models Online class (1 hr)
Model selection Online class (1 hr)
Transformations Online class (1 hr)
Using linear models for prediction Online class (1 hr)
Week 04 Drop in help lab Computer laboratory (2 hr)
Week 05 Students deliver their presentations to the class Presentation (2 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 2 credit point unit, this equates to roughly 40-50 hours of student effort in total.

• Faraway, Julian James. Linear Models with R. Second edition. Boca Raton: CRC Press, Taylor & Francis Group, 2015.

## 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.

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

• LO1. formulate domain/context speciﬁc questions and identify appropriate statistical analysis
• LO2. generate, interpret and compare numerical and graphical summaries of different data types
• LO3. identify, justify, implement and evaluate appropriate parametric or non-parametric statistical tests for more than two samples
• LO4. generate, critique and interpret appropriate linear models to predict outcomes and describe relationships between multiple variables.

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

GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

## Responding to student feedback

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

No changes have been made since this unit was last offered.