- Class Number 9704
- Term Code 3060
- Class Info
- Unit Value 6 units
- Mode of Delivery In Person
- Dr Yuan Gao
- Dr Yuan Gao
- Class Dates
- Class Start Date 27/07/2020
- Class End Date 30/10/2020
- Census Date 31/08/2020
- Last Date to Enrol 03/08/2020
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.
Upon successful completion, students will have the knowledge and skills to:
- Demonstrate a thorough understanding of the R statistical computing language, particularly the graphical capabilities;
- Fit simple linear regression models, interpret model parameters and relate theses back to the underlying research question;
- Analyse and interpret relationships between a response variable and a covariate;
- Analyse and interpret relationships between a response variable and several covariates;
- 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,
- Select and discuss a useful multiple linear regression model from a number of competing models.
My teaching in this introductory course in statistical modelling will draw on examples from my experience in statistical research and consulting.
Examination Material or equipment
You will need access to a scientific calculator for the Final Examination.
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. (Call # QA278.2 .N46 2004 )
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. Note: students will not be able to use any statistical package during the exam.
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
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.
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.
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.
Students are expected to check the Wattle site for announcements about this course, e.g. changes to timetables or notifications of cancellations.
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.
|Week/Session||Summary of Activities||Assessment|
|1||Introduction. Getting started with R. Simple Linear Regression (re- vision). Parameter interpretation/estimation.||No tutorials in week 1|
|2||Properties of least squares estimators. ANOVA||Weekly exercise|
|3||Hypothesis testing and interval estimation in a SLR context. Prediction intervals.||Weekly exercise|
|4||Regression diagnostics (residual plots). Outliers and influential observations.||Weekly exercise|
|5||Scale transformations. Matrix approach to linear regression.||Weekly exercise; Wattle Quiz|
|6||Introduction to Multiple Regression. Model interpretation and estimation.||Weekly exercise|
|7||Model interpretation continued||Weekly exercise; Assignment 1|
|8||ANOVA for multiple regression. Sequential sum of squares.||Weekly exercise|
|9||Qualitative covariates in multiple regression.||Weekly exercise|
|10||Model diagnostics. Outlier detection. Types of residuals. Influence diagnostics. Multicollinearity.||Weekly exercise|
|11||Model selection and criteria for comparing models.||Weekly exercise; Assignment 2|
|12||Revision for Final Examination.|
1. Log on to Wattle, and go to the course site
2. Click on the link ‘Tutorial enrolment’
3. On the right of the screen, click on the tab ‘Become Member of . . . .’ for the tutorial class you wish to enter
4. Confirm your choice
If you need to change your enrolment, you will be able to do so by clicking on the tab ‘Leave group. . . .’ and then re-enrol in another group. You will not be able to enrol in groups that have reached their maximum number. Please note that enrolment in ISIS must be finalised for you to have access to Wattle.
|Assessment task||Value||Due Date||Return of assessment||Learning Outcomes|
|Wattle Quiz||5 %||30/08/2020||30/08/2020||2-3|
|Assignment 1 (Simple Linear Regression)||15 %||22/09/2020||09/10/2020||1-3|
|Assignment 2 (Multiple Regression)||20 %||20/10/2020||01/11/2020||1-5|
|Weekly Exercise||10 %||25/10/2020||01/11/2020||1-6|
|Final Examination||50 %||*||*||1-6|
* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details
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:
- Student Assessment (Coursework) Policy and Procedure
- Special Assessment Consideration Policy and General Information
- Student Surveys and Evaluations
- Deferred Examinations
- Student Complaint Resolution Policy and Procedure
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.
Any student identified, either during the current semester or in retrospect, as having used ghostwriting services will be investigated under the University’s Academic Misconduct Rule.
Assessment Task 1
Learning Outcomes: 2-3
A short quiz will be made available on Wattle for you to complete in week 5. The quiz will be available for a short window in that week. The due date is August 30, 2020. More details will be provided during the lectures in week 4 and on the wattle page. This assessment is redeemable.
Assessment Task 2
Learning Outcomes: 1-3
Assignment 1 (Simple Linear Regression)
The due date is September 22, 2020. Detailed assignment specifications will be handed out at least two weeks prior to the due dates. Assignments are compulsory and will involve using R to analyse data from a case study, then organising and editing the R output and preparing a written report on your analyses. This assessment is to be completed individually and not redeemable.
Assessment Task 3
Learning Outcomes: 1-5
Assignment 2 (Multiple Regression)
The due date is October 20, 2020. Detailed assignment specifications will be handed out at least two weeks prior to the due dates. Assignments are compulsory and will involve using R to analyse data from a case study, then organising and editing the R output and preparing a written report on your analyses. This assessment is to be completed individually and not redeemable.
Assessment Task 4
Learning Outcomes: 1-6
Students are required to attempt one question each week. The question will be posted at the start of each week. The due date is every Sunday. Written answers are submitted through Wattle. This assessment is to be completed individually and not redeemable.
Assessment Task 5
Learning Outcomes: 1-6
This is a compulsory piece of assessment which is a written exam to be held during the end of the semester examination period. Permitted materials and other conditions for the Final Examination will be discussed with students no later than week 10. The outcome will be advised on Wattle. The Final Examination will be centrally timetabled and the details released via http : //timetable.anu.edu.au/. Further information about the examination will be provided in class and on Wattle prior to the end of week 12.
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.
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.
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 of assignments without an extension is penalised at the rate of 5% of the possible marks available per working day or part thereof. Late submission of assignments 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.
Late submission is not accepted for Weekly Exercise.
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.
Assignments 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 after the due date.
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).
- ANU Health, safety & wellbeing for medical services, counselling, mental health and spiritual support
- ANU Diversity and inclusion for students with a disability or ongoing or chronic illness
- ANU Dean of Students for confidential, impartial advice and help to resolve problems between students and the academic or administrative areas of the University
- ANU Academic Skills and Learning Centre supports you make your own decisions about how you learn and manage your workload.
- ANU Counselling Centre promotes, supports and enhances mental health and wellbeing within the University student community.
- ANUSA supports and represents undergraduate and ANU College students
- PARSA supports and represents postgraduate and research students
Functional data analysis
Dr Yuan Gao
Dr Yuan Gao