• Class Number 3523
• Term Code 3240
• Class Info
• Unit Value 6 units
• Mode of Delivery In Person
• COURSE CONVENER
• Dr Francis Hui
• LECTURER
• Dr Francis Hui
• Class Dates
• Class Start Date 30/05/2022
• Class End Date 29/07/2022
• Census Date 10/06/2022
• Last Date to Enrol 10/06/2022
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

This course elaborates as well as builds upon the statistical principles to which you have been exposed in introductory statistics course/s. The contents and activities in this

course are designed to help you to build a more rigorous yet data-driven foundation towards a better understanding of linear models, and the workflow behind good statistical model-building using linear regression. Course contents and activities will involve statistical computing with R interfaced through R Studio. Additionally, one or more of the assessment tasks will involve the statistical analysis of datasets using linear models, mimicking real-life applied statistics and placing the student as the researcher and "statistical consultant".

Optional purchase of a non-programmable calculator.

## Examination Material or equipment

The final exam will be a take-home exam. It will be an open book exam, and as such any resource is permitted e.g., you will be able to go online and use probability tables or use statistical software such as R, as appropriate. However, collaboration and collusion is not permitted.

Final details of the take home exam, along with all other assessments, will be made available on the Wattle page no later than the end of the intensive week i.e., by 5pm Canberra time on 1 July.

## Required Resources

There is no required textbook for this class; all lecture notes, required reading, and additional resources will be posted via Wattle.

Recommend textbooks and suggested reading (Note these are not compulsory for the course):

All of the above texts are available from the ANU library.

## Staff Feedback

Feedback from the teaching staff will aim to facilitate the learner's ongoing self assessment of their progress in achieving the learning objectives of the course. To this end, the learner should converse with the teaching staff through Wattle’s discussion forum (preferably) throughout the course, and in-person during the intensive week.

Limited written and verbal comments will also be provided through the grading of assessments tasks. Note that in order to safeguard student privacy, staff members need to

be sure that they are dealing with the right student, therefore course-related messages sent from non-ANU email accounts will generally be ignored.

## 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

Information listed in this course summary is tentative, and students will be made aware of any alterations to all aspects of the course e.g., general announcements, changes in assessment availability and due dates, information regarding the structure of the intensive week, via the course Wattle site.

Course materials sharing is prohibited

Any materials in this class may not be shared online or with anyone outside of the class unless you have the convener's explicit, written permission. This includes but is not limited to lecture videos, assessments, in-class materials, review sheets, and additional problem sets.

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.

Scaling

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.

## Class Schedule

Week/Session Summary of Activities Assessment
1 Pre-intensive period (4 weeks remote; 30 May - 24 June; each consists of 3 x 1-1.5 hour of pre-recorded lectures, and 1 x 2 hour pre-recorded tutorial): C1: Review of basic statistical concepts. Introduction to R (optional) C2: Overview of regression analysis and linear model. C3: Simple linear regression model (SLR) -- model descriptions, assumptions, and model fitting through ordinary least squares (OLS). Properties of OLS estimators. C4: SLR -- Inference and prediction. Analysis of variance. C5: SLR -- Quality of fitted model and basic model diagnostic. C6: Basic matrix and linear algebra. Assignment 1 open and due
2 Intensive period (1 week in hybrid format; 27 June - 1 July; each day consists of 1 x 3.5 hours live (on-campus) lectures + 1 x 2 hours live (on-campus) tutorials. Both tutorials and lectures will be recorded): C7: Multiple linear regression model (MLR) -- model descriptions, assumptions, and model fitting through OLS. C8: MLR -- Properties of OLS estimators. Inference and prediction. C9: MLR -- Models with qualitative variables and interactions; Multicollinearity. C10: MLR -- Variable selection and model selection. C11: MLR -- Quality of fitted models and model diagnostics. C12: A room with a view -- principal component regression, penalized regression, beyond linear models. Wattle quiz open
3 Post-intensive period (4 weeks remote; 7 July - 29 July; there are no classes during this period. But consultation hours (over Zoom) will be increased). Wattle Quiz due Assignment 2 open and due Take home exam open and due

## Tutorial Registration

Tutorial registration will not be required.

## Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Assignment 1 25 % 24/06/2022 01/07/2022 1,2,3
Wattle Quiz 10 % 08/07/2022 15/07/2022 2,3,4
Assignment 2 25 % 22/07/2022 29/07/2022 4,5,6
Final exam 40 % 29/07/2022 * 1,2,3,4,5,6

* 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 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.

## Participation

• During the four week pre-intensive period, there will be 3 x 1 hour of pre-recorded lectures, plus 1 x 2 hour of pre-recorded tutorials per week.
• During the one-week intensive period, the course will operate in a hybrid format: students will have the option (but are not required) to attend on-campus lectures and tutorials in person. Each day of the intensive week will consists of 1 x 3.5 hour live (on-campus) lectures, plus 1 x 2 hour live (on-campus) tutorials. All lectures and tutorials will be streamed live via Zoom, as well as recorded.
• There will be no classes during the post-intensive period.

All assessment tasks will be conducted remotely.

## Examination(s)

The course involves one take-home exam, which is tentatively scheduled to take place in the last week of the course i.e., late post-intensive week 4. You will be required to

sign a declaration, in the form of a cover sheet, as part of the submission of your solutions to the take-home exam.

When completing any assessment of the course, students must act in accordance with the University’s Academic Misconduct Rule. Any student identified, either during the

current semester or in retrospect, as having used ghost writing services, among other activities that constitute poor academic practice and/or academic misconduct, will be

investigated under the University’s Academic Misconduct Rule.

be made available no later than the end of the intensive week i.e., by 5pm Canberra time on 1 July.

Value: 25 %
Due Date: 24/06/2022
Return of Assessment: 01/07/2022
Learning Outcomes: 1,2,3

Assignment 1

The assignment will assess Chapters 1-3 of the course. It must be completed individually, and is not redeemable. This assignment is designed to help you understand the simple linear regression model. This assignment involves both mathematical/conceptual questions (50%) and data analysis in R (50%).

Availability: By 5:00 pm Canberra time, Friday 10th June

Due date: Friday 24 June (i.e., end of pre-intensive week 4) @ 5:00 pm Canberra Time

Value: 25%

Value: 10 %
Due Date: 08/07/2022
Return of Assessment: 15/07/2022
Learning Outcomes: 2,3,4

Wattle Quiz

The quiz (using Wattle quiz tool) is designed to help you (largely) review linear algebra and the matrix formulation of multiple linear regression models, assessing Chapters 4-6 of the course. It must be completed individually, and is not redeemable. Each student will have only one attempt, but the attempt is not timed i.e student can take as much time as needed to complete the quiz.

Availability: From 9:00 am Canberra time, Friday 1st July

Due date: Friday 8 July (i.e., end of post-intensive week 1) @ 5:00 pm Canberra Time, although it is strongly recommended you complete it before this date in light of the timing of other assessments.

Value: 10%

Value: 25 %
Due Date: 22/07/2022
Return of Assessment: 29/07/2022
Learning Outcomes: 4,5,6

Assignment 2

The assignment will assess Chapters 7-12 of the course. It must be completed individually, and is not redeemable. This assignment is designed to help you understand multiple linear regression models. It will contain two main parts: part 1 (50%) involves mathematical/conceptual questions and analyses of model outputs, part 2 (50%) is a mini-project that requires you to analyze real datasets.

Availability: By 5:00 pm Canberra time, Friday 8th July

Due date: Friday 22 July (i.e., end of post-intensive week 3) @ 5:00 pm Canberra Time.

Value: 25%

Value: 40 %
Due Date: 29/07/2022
Learning Outcomes: 1,2,3,4,5,6

Final exam

Details of the final take-home will be announced on Wattle no later than the end of the intensive week i.e., by 5pm Canberra time on 1 July. It must be completed individually, and is not redeemable. The exam will assess all chapters of the course, and will consist of up to four questions, each with multiple parts, and will be at a level similar to that of the Assessment Tasks 1 and 3.

Due date: The take-home exam is tentatively scheduled to take place on Friday 29 July (i.e., post-intensive week 4), or that weekend.

Value: 40%

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.

## Online Submission

You will be required to sign a declaration, in the form of a cover sheet, as part of the submission of your assignment. Please keep a copy of the assignment and signed cover

Each assignment and the final take-home exam must be submitted as a single electronic file, preferably a pdf, to the appropriate activity on the course Wattle site. If submitting

handwritten mathematical derivations, ensure that your handwriting is legible, appropriate working out is shown, and then scan the derivations in e.g., by using your

smartphone camera and Microsoft Lens.

## Hardcopy Submission

Hard copy submissions will not be used in this course.

## Late Submission

No submission of any assessment tasks without an approved extension after the due date is permitted. If an assessment task is submitted after the due date, then unless an

extension has been approved, a mark of zero will be awarded.

## 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

Graded assignments along with feedback should be made available via the relevant activity on the course Wattle site at an appropriate time as indicated above.

## 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

Resubmission of assessments is not allowed under any circumstance.

## 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.
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.

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

 Dr Francis Hui 02 61251976 francis.hui@anu.edu.au

### Research Interests

correlated data analysis; ecological statistics; semiparametric regression; variable selection

### Dr Francis Hui

 Thursday 16:00 18:00 Thursday 16:00 18:00 By Appointment

## Instructor

 Dr Francis Hui 02 6125 1976 francis.hui@anu.edu.au

### Dr Francis Hui

 Thursday 16:00 18:00 Thursday 16:00 18:00 By Appointment