- Class Number 2972
- Term Code 3130
- Class Info
- Unit Value 6 units
- Mode of Delivery In Person
- Dr Xuan Liang
- Dr Xuan Liang
- Class Dates
- Class Start Date 22/02/2021
- Class End Date 28/05/2021
- Census Date 31/03/2021
- Last Date to Enrol 01/03/2021
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.
Additional Course Costs
Students will need a non-programmable scientific calculator.
Examination Material or equipment
Examination material and condition will be notified to all students via wattle and the examinations office.
Applied Linear Regression Models (4th Edition): by Michael H. Kutner, Christopher J. Nachtsheim, John Neter. ISBN: 9780073014661
The ebook can be found on ANU library https://library.anu.edu.au/record=b6852478. The ANU Library has been requested to make hard copies of this book available as a 2 hour or 2 day loan.
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.
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 . (Freely available from http://moderndive.com )
Students will be given feedback (through both verbal and written comments) in the following forms in this course:
• To the whole class during lectures.
• Within tutorials.
• Individually during consultation hours.
Students will also be given online quiz feedback on Wattle and written comments in the marked assignments.
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.
Your final mark for the course will be based on the raw marks allocated for each of your assessment items. However, your final mark may not be the same number as produced by that formula, as marks may be scaled. Any scaling applied will preserve the rank order of raw marks (i.e. if your raw mark exceeds that of another student, then your scaled mark will exceed the scaled mark of that student), and may be either up or down.
In assignments and exams, students must appropriately reference any results, words or ideas that they take from another source which is not their own. A guide can be found at https://academicskills.anu.edu.au/resources/handouts/referencing-basics.
STAT4038 shares the same lecture content and assignments with STAT2008, STAT2014, STAT6014 and STAT6038, however these cohorts may have separate tutorials and different assessments. The different cohorts of students will also be treated separately in grading and any scaling that is applied.
|Week/Session||Summary of Activities||Assessment|
|1||Introduction. Getting started with R. Simple Linear Regression (revision). Parameter interpretation/estimation.||No tutorials in week 1|
|2||Properties of least squares estimators. ANOVA|
|3||Hypothesis testing and interval estimation in a SLR context. Prediction intervals.|
|4||Regression diagnostics (residual plots). Outliers and influential observations.|
|5||Scale transformations. Matrix approach to linear regression.||Wattle Quiz|
|6||Introduction to Multiple Regression. Model interpretation and estimation.||Release of Assignment 1 via Wattle|
|7||Model interpretation continued||Submission of Assignment 1 via Wattle|
|8||ANOVA for multiple regression. Sequential sum of squares.|
|9||Qualitative covariates in multiple regression.||Release of Assignment 2 via Wattle|
|10||Model diagnostics. Outlier detection. Types of residuals. Influence diagnostics. Multicollinearity.|
|11||Model selection and criteria for comparing models.||Submission of Assignment 2 via Wattle|
|12||Course review||There will be a final exam during the university examination period. More information and instructions regarding final exams will be provided no later than week 10.|
Tutorials will be available on campus, live through scheduled Zoom sessions, and as pre-recorded videos. Information regarding enrolments for these options will be provided om Wattle during O-week, prior to the start of the semester.
|Assessment task||Value||Due Date||Return of assessment||Learning Outcomes|
|Wattle Quiz||5 %||26/03/2021||26/03/2021||2-3|
|Assignment 1||15 %||20/04/2021||30/04/2021||1-3|
|Assignment 2||15 %||18/05/2021||28/05/2021||1-5|
|Final Examination||65 %||03/06/2021||01/07/2021||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 Academic Integrity . 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.
Lectures will be pre-recorded. Consultations will be live through Zoom. Tutorials will be available on campus, live through scheduled Zoom sessions, and as pre-recorded videos. Information regarding enrolments for these options will be provided on Wattle during O-week, prior to the start of the semester.
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. Centrally scheduled examinations through Examinations, Graduations & Prizes will be timetabled prior to the examination period. Please check ANU Timetabling for further information.
Assessment Task 1
Learning Outcomes: 2-3
This quiz is worth 5% of the final raw score and is redeemable. The quiz will be designed to cover materials from Week 1 to Week 4 and be available for a short window in week 5. The notification about access to the quiz will be announced in week 4 during lectures and on Wattle. Under no circumstances will the students be able to attempt the quiz outside of the allocated time period.
Assessment Task 2
Learning Outcomes: 1-3
The students are expected to complete this assignment individually. This assignment is designed to cover materials about Simple Linear Regression. It is worth 15% of the final raw score and is redeemable. The assignment and further details will be made available in week 6 during lectures and on Wattle. It will be due on Tuesday in Week 7 after the teaching break. It will involve using R to analyse data from a case study, then organizing and editing the R output and preparing a written report on your analyses, as well as some proofs.
Assessment Task 3
Learning Outcomes: 1-5
The students are expected to complete this assignment individually. This assignment is designed to cover materials about Multiple Regression. It is worth 15% of the final raw score and is redeemable. The assignment and further details will be made available in week 9 during lectures and on Wattle. It will be due on Tuesday in Week 11. It will involve using R to analyse data from a case study, then organizing and editing the R output and preparing a written report on your analyses, as well as some proofs.
Assessment Task 4
Learning Outcomes: 1-6
This final exam will be based on all the materials covered throughout the duration of the semester. The final examination is a compulsory piece of assessment and worth 65% of the final raw score. Students will be provided with further details regarding the exam no later than week 10. June 3rd is the earliest date the exam can be held.
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.
There is no hardcopy submission in the course.
No submission of assessment tasks without an extension after the due date will be permitted. If an assessment task is not submitted by the due date, a mark of 0 will be awarded.
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.
The marked 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
It will not be possible for assignments to be resubmitted.
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
Spatial modeling, panel data, nonparametric and semiparametric modeling, network data modeling, environmental statistics.
Dr Xuan Liang
Dr Xuan Liang