• Class Number 3607
• Term Code 3030
• Class Info
• Unit Value 6 units
• Mode of Delivery In Person
• COURSE CONVENER
• Dr Anton Westveld
• LECTURER
• Dr Anton Westveld
• Lucy Hu
• Class Dates
• Class Start Date 24/02/2020
• Class End Date 05/06/2020
• Census Date 08/05/2020
• Last Date to Enrol 02/03/2020
SELT Survey Results

Regression Modelling for Actuarial Studies (STAT6014)

This is a course in applied statistics that studies the use of regression techniques for examining relationships between variables. Ordinary linear models and generalised linear models are covered. The course emphasizes 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 these 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;
6. Select and discuss a useful multiple linear regression model from a number of competing models; and
7. Define and describe the features of a Generalised Linear Model (GLM), fit GLM models, assess and refine the models based on diagnostic measures, and interpret model output.

## Research-Led Teaching

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 also need access to a scientific calculator for the Final Examination.

## Required Texts

This year's textbook:

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

In order to reduce the cost of purchase, we also made a customised version which can be bought from the Harry Hartog Bookstore on campus:

• Linear Regression 1e (customised print edition) ISBN:9781307350692

• Linear Regression 1e (ebook version). 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.

Course notes, tutorial material and other required resources will be provided through 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.

• Faraway, Julian J. (2016) Extending the Linear Models with R, 2nd Edn, CRC/Chapman & Hall.

There are electronic copies available for loan from the library.

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

Students will be given feedback in the following forms in this course:

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

Co-Teaching

STAT6014 shares the same lecture content and assignments with STAT2008 and STAT2014 cohorts, however these cohorts may have separate tutorials and different assessments. Students in 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.

## 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. GLM Introduction, Exponential Family, Maximum Likelihood Estimator. Assignment 1
7 Model interpretation continued (discussion of causality). Binary Logistic Regression and Model Diagnostics
8 ANOVA for multiple regression. Sequential sum of squares. Binomial Logistic Regression, Dummy Variable
9 Hypothesis testing, confidence intervals and prediction for multiple regression. Poisson Log-linear Regression
10 Model diagnostics. Outlier detection. Types of residuals. Influence diagnostics. Multicollinearity. Model Diagnostics for Binomial Logistic Regression and Poisson Log-linear Regression Assignment 2
11 Model selection and criteria for comparing models. Gamma Regression and Model Diagnostics.
12 Revision for Final Examination.

## Tutorial Registration

Tutorial signup for this course will be done via the Wattle website. Detailed information about signup times will be provided on Wattle or during your first lecture. When tutorials are available for enrolment, follow these steps:

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

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 Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Wattle Quiz 5 % 23/03/2020 27/03/2020 2-3
Assignment 1 (Simple Linear Regression) 15 % 27/03/2020 27/04/2020 1-3
Assignment 2 (Multiple Regression) 20 % 15/05/2020 01/06/2020 1-5, 7
Final Examination 60 % 04/06/2020 02/07/2020 1-7

* 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.

Value: 5 %
Due Date: 23/03/2020
Return of Assessment: 27/03/2020
Learning Outcomes: 2-3

Wattle Quiz

A short quiz will be made available on Wattle for you to complete in week 5. The quiz will be available for three days, and you will have one hour after accessing the quiz to complete it. It must be completed by 23:59 on the due date. More details will be provided during the lectures and on the wattle page. This assessment is not redeemable. The quiz will be automatically marked by Wattle and grades given the day after the quiz finishes.

Value: 15 %
Due Date: 27/03/2020
Return of Assessment: 27/04/2020
Learning Outcomes: 1-3

Assignment 1 (Simple Linear Regression)

Detailed assignment specifications will be handed out at least three 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. The assessment will be submitted in Wattle using TurnitIn by 23:59 on the due date and marks/feedback will be given on the 'Return of Assessment Date'.

Value: 20 %
Due Date: 15/05/2020
Return of Assessment: 01/06/2020
Learning Outcomes: 1-5, 7

Assignment 2 (Multiple Regression)

Detailed assignment specifications will be handed out at least three 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 not redeemable. The assessment will be submitted in Wattle using TurnitIn by 23:59 on the due date and marks/feedback will be given on the 'Return of Assessment Date'.

Value: 60 %
Due Date: 04/06/2020
Return of Assessment: 02/07/2020
Learning Outcomes: 1-7

Final Examination

This a compulsory piece of assessment which is a three hour written exam to be held during the end of semester examination period. Permitted materials and other

conditions for the Final Examination will be discussed with students and the outcome 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 during Week 11.

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 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.
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 Anton Westveld 6125 9045 anton.westveld@anu.edu.au

### Research Interests

My research interests include Bayesian methodology and theory, network and relational data, game theoretic data, inference (uncertainty quantification) for agent based stochastic computer simulation models, and statistical causality.

### Dr Anton Westveld

 By Appointment

## Instructor

 Dr Anton Westveld 6125 5122 anton.westveld@anu.edu.au

### Dr Anton Westveld

 By Appointment

## Instructor

 Lucy Hu 6125 0836 yunxi.hu@anu.edu.au