• Class Number 3992
• Term Code 3330
• Class Info
• Unit Value 6 units
• Mode of Delivery In Person
• COURSE CONVENER
• Abhinav Mehta
• LECTURER
• Abhinav Mehta
• Class Dates
• Class Start Date 20/02/2023
• Class End Date 26/05/2023
• Census Date 31/03/2023
• Last Date to Enrol 27/02/2023
SELT Survey Results

Regression Modelling (STAT2008)

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 working knowledge of the R statistical computing language, particularly the graphical capabilities;
2. Fit simple linear regression models and interpret model parameters;
3. Summarise and analyse relationships between a response variable and a covariate;
4. Summarise and analyse relationships between a response variable and several covariates;
5. Assess and refine simple and multiple linear regression models based on diagnostic measures, including identifying outlying and influential data points; and,
6. Explore model selection in a multiple linear regression modelling context.

## Research-Led Teaching

The material covered in this course covers established principles in actuarial work and academia.

Students will need a non-programmable scientific calculator.

## Examination Material or equipment

The final exam will be conducted remotely via Wattle with limited timeframes and using the Proctorio or other online invigilation software which will be advised no later than Week 10 of the semester.

## Required Texts

Applied Linear Regression Models (4th Edition): by Michael H. Kutner, Christopher J. Nachtsheim, John Neter. ISBN: 9780073014661

The ebook can be found in the ANU library here.

## 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).

## Staff Feedback

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.

## 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). Feedback can also be provided to Course Conveners and teachers via the Student Experience of Learning & Teaching (SELT) feedback program. SELT surveys are confidential and also provide the Colleges and ANU Executive with opportunities to recognise excellent teaching, and opportunities for improvement.

## 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

Assessment Requirements

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.

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.

Referencing Requirements

In assignments and exams, students must appropriately reference any results, words, or ideas that they take from another source that is not their own. A guide can be found at https://academicskills.anu.edu.au/resources/handouts/referencing-basics.

Co-Teaching

STAT2008 shares the same lecture content and assignments as STAT2014 and STAT6014, however, these cohorts may have separate tutorials and different assessments. Students in STAT2014 and STAT6014 also have some additional lecture content that STAT2008 students are not required to take. The different cohorts of students will also be treated separately in grading and any scaling that is applied. Contact the convener of the Bachelor of Actuarial Studies if you are unsure whether to enrol in STAT2008, STAT2014 or STAT6014.

## Class Schedule

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. Submission of Assignment 1 via Wattle
7 Model interpretation continued.
8 ANOVA for multiple regression. Sequential sum of squares.
9 Qualitative covariates in multiple regression.
10 Model diagnostics. Outlier detection. Types of residuals. Influence diagnostics. Multicollinearity. Submission of Assignment 2 via Wattle
11 Model selection and criteria for comparing models.
12 Course review.

## Tutorial Registration

Tutorials will be available on campus, live through scheduled Zoom sessions and as pre-recorded videos. ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage. https://www.anu.edu.au/students/program-administration/timetabling].

## Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Wattle Quiz 5 % 20/03/2023 24/03/2023 2,3
Assignment 1 15 % 30/03/2023 21/04/2023 1,2,3
Assignment 2 15 % 11/05/2023 26/05/2023 1,2,3,4,5
Final Examination 65 % 01/06/2023 29/06/2023 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 Integrity 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 Skills 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.

## Participation

Course content delivery will take the form of weekly on-campus lectures (recorded and available via echo360 on Wattle), weekly online (Zoom) workshops (recorded) and weekly tutorials, delivered in a hybrid format (on campus, live through scheduled Zoom sessions and as pre-recorded videos). Weekly consultations with the lecturer and the tutor(s) will be conducted over Zoom.

## Examination(s)

Centrally scheduled examinations through Examinations, Graduations & Prizes will be timetabled prior to the examination period. Please check ANU Timetabling for further information. 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.

Value: 5 %
Due Date: 20/03/2023
Return of Assessment: 24/03/2023
Learning Outcomes: 2,3

Wattle Quiz

The students are to complete this quiz individually. The quiz will be made available on Wattle for you to complete in week 5. The quiz will be available for a short window that week with more details provided during the lectures and on the Wattle page. This quiz is worth 5% of the final raw score and is not redeemable.

Value: 15 %
Due Date: 30/03/2023
Return of Assessment: 21/04/2023
Learning Outcomes: 1,2,3

Assignment 1

The students are 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 not redeemable. The assignment and further details will be made available in week 4 on Wattle. It will be due at COB on Thursday in Week 6. It will involve using R to analyse data from a case study, then organize and edit the R output and prepare a written report on your analyses, as well as some proofs.

Value: 15 %
Due Date: 11/05/2023
Return of Assessment: 26/05/2023
Learning Outcomes: 1,2,3,4,5

Assignment 2

The students are 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 not redeemable. The assignment and further details will be made available in week 8 on Wattle. It will be due at COB on Thursday in Week 10. It will involve using R to analyse data from a case study, then organize and edit the R output and prepare a written report on your analyses, as well as some proofs.

Value: 65 %
Due Date: 01/06/2023
Return of Assessment: 29/06/2023
Learning Outcomes: 1,2,3,4,5,6

Final Examination

The students are to complete this assessment individually. The final exam will be held during the exam period with details to be advised no later than teaching week 10 of the semester. This is a compulsory piece of assessment which will be a 4-hour written exam to be held during the end of the semester examination period. Examination materials and conditions will be notified to all students via Wattle no later than week 10 of the semester. Centrally scheduled examinations through Examinations, Graduations & Prizes will be timetabled prior to the examination period. Please check ANU Timetabling for further information.

Academic integrity is a core part of the ANU culture as a community of scholars. The University’s students are an integral part of that community. The academic integrity principle commits all students to engage in academic work in ways that are consistent with, and actively support, academic integrity, and to uphold this commitment by behaving honestly, responsibly and ethically, and with respect and fairness, in scholarly practice.

The University expects all staff and students to be familiar with the academic integrity principle, the Academic Integrity Rule 2021, the Policy: Student Academic Integrity and Procedure: Student Academic Integrity, and to uphold high standards of academic integrity to ensure the quality and value of our qualifications.

The Academic Integrity Rule 2021 is a legal document that the University uses to promote academic integrity, and manage breaches of the academic integrity principle. The Policy and Procedure support the Rule by outlining overarching principles, responsibilities and processes. The Academic Integrity Rule 2021 commences on 1 December 2021 and applies to courses commencing on or after that date, as well as to research conduct occurring on or after that date. Prior to this, the Academic Misconduct Rule 2015 applies.

The University commits to assisting all students to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. All coursework students must complete the online Academic Integrity Module (Epigeum), and Higher Degree Research (HDR) students are required to complete research integrity training. The Academic Integrity website provides information about services available to assist students with their assignments, examinations and other learning activities, as well as understanding and upholding academic integrity.

## 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

There is no hardcopy submission in the course.

## Late Submission

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 then a penalty of 5% per working day will be applied to your marked assignment up to a maximum of 10 working days after the due date. Any submissions after this period will be marked 0.

## Referencing Requirements

The Academic Skills website has information to assist you with your writing and assessments. The website includes information about Academic Integrity including referencing requirements for different disciplines. There is also information on Plagiarism and different ways to use source material.

## Returning Assignments

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.

## 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

 Abhinav Mehta 02 6125 1081 Abhinav.Mehta@anu.edu.au

### Research Interests

Bio-Statistics, Crime Statistics, Survival Analysis, Longitudinal Data Analysis

### Abhinav Mehta

 Tuesday 14:00 16:00 Tuesday 14:00 16:00

## Instructor

 Abhinav Mehta 02 6125 1081 Abhinav.Mehta@anu.edu.au

### Abhinav Mehta

 Tuesday 14:00 16:00 Tuesday 14:00 16:00