• Class Number 5602
• Term Code 3040
• Class Info
• Unit Value 6 units
• Mode of Delivery Online
• COURSE CONVENER
• Linh Nghiem
• Class Dates
• Class Start Date 25/05/2020
• Class End Date 24/07/2020
• Census Date 12/06/2020
• Last Date to Enrol 29/05/2020
• TUTOR
• Hao Wen
SELT Survey Results

Regression Modelling (STAT6038)

Regression Modelling is a course in applied statistics that studies the use of linear regression techniques for examining relationships between variables. The course emphasises the principles of statistical modelling through the iterative process of fitting a model, examining the fit to assess imperfections in the model and suggest alternative models, and continuing until a satisfactory model is reached. Both steps in this process require the use of a computer: model fitting uses various numerical algorithms, and model assessment involves extensive use of graphical displays. The R statistical computing package is used as an integral part of the course.

## Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

1. Demonstrate a thorough understanding of the R statistical computing language, particularly the graphical capabilities;
2. Fit simple linear regression models, interpret model parameters and relate theses back to the underlying research question;
3. Analyse and interpret relationships between a response variable and a covariate;
4. Analyse and interpret relationships between a response variable and several covariates;
5. Assess and refine simple and multiple linear regression models based on diagnostic measures, including identifying and discuss the implications of outlying and influential data points; and,
6. Select and discuss a useful multiple linear regression model from a number of competing models.

## Research-Led Teaching

My teaching in this introductory course in statistical modeling will draw on examples from my experience in statistical research and consulting.

## Required Texts

This year's textbook:

• Applied Linear Regression Models (4th Edition): by Michael H. Kutner, Christopher J. Nachtsheim, John Neter. ISBN: 9780073014661
• The ebook can be bought here

To access the ebook, you will need the Vitalsource app which can be downloaded on various platforms (PC, Mac, iOS and Android). VitalSouce Bookshelf app

You need to have access to only 1 of these, either the print or the eBook version but not both. The books have also been requested to be added to the short-term loan of Hancock library.

All other required materials will be provided via wattle.

## Technology and Software

The application of modern statistical techniques requires familiarity with a statistical computing package. Examples provided in lectures, tutorials, and work related to the assignments will entail the use of the statistical computer packages R and RStudio, which are freely available at www.r - project.org and https://www.rstudio.com . The program code used for examples provided in lectures and tutorials will be available on the course Wattle site.

These are some other textbooks and resources you may find very useful:

• Faraway, Julian J. (2015) Linear Models with R, 2nd Edn, CRC/Chapman & Hall.

There are multiple copies of this text on 2 hour reserve in the ANU Hancock library (Call # QA279.F37 2015) and the Harry Hartog Bookshop has copies available for purchase (cheaper e-book versions or second-hand copies of the first edition readily available, which would be fine).

For students who would like additional help getting started with R, I also recommend:

• Chester Ismay and Albert Y. Kim. (2017) Modern Dive: An Introduction to Statistical and Data Sciences via R. http://moderndive.com

## Staff Feedback

You will be given individual feedback by your tutor, who will mark your assignments. Solutions to the assignments will be provided on Wattle. Additionally, general verbal comments will be provided to the whole class.

You are also welcome to ask questions of me or any of the class tutors at consultations or during classes. If you wish to ask me questions immediately following a lecture, please wait for me out- side the lecture theatre, so that I can clean-up and log-off in preparation for the next class that will be using the same venue.

## Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). The feedback given in these surveys is anonymous and provides the Colleges, University Education Committee and Academic Board with opportunities to recognise excellent teaching, and opportunities for improvement. The Surveys and Evaluation website provides more information on student surveys at ANU and reports on the feedback provided on ANU courses.

## Other Information

Support for Students

The University offers a number of support services for students. Information on these is available online from http : //students.anu.edu.au/studentlife/

Communication via Email

If I, or anyone in the School, College or University administration, need to contact you, we will do so via your official ANU student email address, which you need to check regularly. If you have any questions for the teaching and course convenor make sure you email them using your ANU email address. Emails from personal email accounts will not be answered.

Announcements

Students are expected to check the Wattle site for announcements about this course, e.g. changes to timetables or notifications of cancellations.

Assessment Requirements

Any student identified, either during the current semester or in retrospect, as having used ghost writing services will be investigated under the University’s Academic Misconduct Rule.

## Class Schedule

Week/Session Summary of Activities Assessment
1 Introduction and review of basic statistical concepts. Tutorial 1: R introduction. Wattle Quiz 1
2 Overview of regression analysis and linear model.
3 Simple linear regression model (SLR): model descriptions, assumptions, and model fitting through ordinary least squares. Properties of estimators.
4 SLR: Inference and prediction. ANOVA.
5 SLR: Quality of fitted model and basic model diagnostics. Tutorial 2. Assignment 1
6 Review of Matrix and Linear Algebra. Wattle Quiz 2
7 Multiple linear regression model (MLR): model descriptions, assumptions and model fitting thorough ordinary least squares. Properties of estimators.
8 MLR inference and prediction. Tutorial 3.
9 MLR: Multicollinearity. Models with qualitative variables and interactions.
10 MLR: Quality of fitted models and model diagnostics.
11 MLR: Variable selection and model selection. Tutorial 4. Assignment 2
12 Weighted Least Squares and Robust Regression.
13 More on Multicollinearity: Principal Component Regression and Ridge Regression. Tutorial 5. Final Group Project

## Assessment Summary

Assessment task Value Due Date Learning Outcomes
Wattle Quiz 1 5 % 01/06/2020 1-5
Assignment 1 (Simple Linear Regression) 20 % 15/06/2020 1-3
Wattle Quiz 2 (Matrix and Basic Linear Algebra) 5 % 22/06/2020 4-6
Assignment 2 (Multiple Regression) 30 % 06/07/2020 4-6
Final Group Project 40 % 24/07/2020 1-6

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details

## Policies

ANU has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Misconduct Rule before the commencement of their course. Other key policies and guidelines include:

## Assessment Requirements

The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the ANU Online website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.

## Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.

## Examination(s)

Any student identified, either during the current semester or in retrospect, as having used ghost writing services will be investigated under the University’s Academic Misconduct Rule.

## Assessment Task 1

Value: 5 %
Due Date: 01/06/2020
Learning Outcomes: 1-5

Wattle Quiz 1

The quiz is designed to help you reinforce basic statistical concepts, including confidence interval and hypothesis testing, which will be used extensively later in the class later.

## Assessment Task 2

Value: 20 %
Due Date: 15/06/2020
Learning Outcomes: 1-3

Assignment 1 (Simple Linear Regression)

This assignment is designed to help you understand simple linear regression models. This assignment involves both mathematical/conceptual questions and data analysis in R. This assessment is to be completed individually and is redeemable.

## Assessment Task 3

Value: 5 %
Due Date: 22/06/2020
Learning Outcomes: 4-6

Wattle Quiz 2 (Matrix and Basic Linear Algebra)

This assessment is designed for you to study or review basic matrix linear algebra knowledge that is going to used extensively to describe and analyze the multiple linear regression model.

## Assessment Task 4

Value: 30 %
Due Date: 06/07/2020
Learning Outcomes: 4-6

Assignment 2 (Multiple Regression)

This assignment is designed to help you understand multiple regression models. This assignment involves both mathematical/conceptual questions and data analysis in R. This assessment is to be completed individually and is redeemable.

## Assessment Task 5

Value: 40 %
Due Date: 24/07/2020
Learning Outcomes: 1-6

Final Group Project

This final group project is designed to mimic the process of data analysis in practice. You will work in a group of no more than three people and analyze a unique dataset using techniques covered in the class. No group will have the same dataset. The written report will be evaluated in terms of (1) technical details, (2) interpretation of the results of statistical analyses in the context, and (3) quality of writing and presentation. More details will be uploaded to the Wattle site by June 25.

## Academic Integrity

Academic integrity is a core part of the ANU culture as a community of scholars. At its heart, academic integrity is about behaving ethically, committing to honest and responsible scholarly practice and upholding these values with respect and fairness.

The ANU commits to assisting all members of our community to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. The ANU expects staff and students to be familiar with the academic integrity principle and Academic Misconduct Rule, uphold high standards of academic integrity and act ethically and honestly, to ensure the quality and value of the qualification that you will graduate with.

The Academic Misconduct Rule is in place to promote academic integrity and manage academic misconduct. Very minor breaches of the academic integrity principle may result in a reduction of marks of up to 10% of the total marks available for the assessment. The ANU offers a number of online and in person services to assist students with their assignments, examinations, and other learning activities. Visit the Academic Skills website for more information about academic integrity, your responsibilities and for assistance with your assignments, writing skills and study.

## Online Submission

You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education) submission must be through Turnitin.

## Hardcopy Submission

For some forms of assessment (hand written assignments, art works, laboratory notes, etc.) hard copy submission is appropriate when approved by the Associate Dean (Education). Hard copy submissions must utilise the Assignment Cover Sheet. Please keep a copy of tasks completed for your records.

## Late Submission

Late submission of assessment tasks without an extension are penalised at the rate of 5% of the possible marks available per working day or part thereof. Late submission of assessment tasks is not accepted after 10 working days after the due date, or on or after the date specified in the course outline for the return of the assessment item.

## Referencing Requirements

Accepted academic practice for referencing sources that you use in presentations can be found via the links on the Wattle site, under the file named “ANU and College Policies, Program Information, Student Support Services and Assessment”. Alternatively, you can seek help through the Students Learning Development website.

## Returning Assignments

Assignment will be returned online.

## Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

## Resubmission of Assignments

Assignments may not be resubmitted.

## Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

## Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.

Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

## Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

## Convener

 Linh Nghiem U1074273@anu.edu.au

## Tutor

 Hao Wen u5883475@anu.edu.au

### Hao Wen

Responsible Officer: Registrar, Student Administration / Page Contact: Website Administrator / Frequently Asked Questions