• Class Number 2498
  • Term Code 2930
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
  • COURSE CONVENER
    • Abhinav Mehta
  • LECTURER
    • Abhinav Mehta
  • Class Dates
  • Class Start Date 25/02/2019
  • Class End Date 31/05/2019
  • Census Date 31/03/2019
  • Last Date to Enrol 04/03/2019
SELT Survey Results

STAT2008/STAT6038 is a course in applied statistics that studies the use of linear regression techniques for examining relationships between variables. 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 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. Identify outlying and influential data points.
  6. Carry out model selection in a multiple linear regression modelling context.

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 Resources

Required Texts

The required textbook for this year is available both in print and ebook version. The print version can be bought from the Harry Hartog Bookstore on campus. The ebook can be bought from the publisher’s website (link provided below). In order 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


Details of the textbook:

This textbook is a customised version created specifically for this course. It is meant to include content which is covered in the course as well as reduce the cost of purchase to students.

  • Linear Regression 1e (customised print edition) ISBN:9781307350692
  • Linear Regression 1e (ebook version). Can be bought here.

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 the Hancock library.


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 ANU Co-op 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

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

Students are expected to check the Wattle site for announcements about this course, e.g. changes to timetables or notifications of cancellations.

Co- Teaching

STAT2008 shares the same lecture content and assignments with STAT4038 & 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.

Class Schedule

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
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. Assignment 1
7 Model interpretation continued (discussion of causality)
8 ANOVA for multiple regression. Sequential sum of squares.
9 Hypothesis testing, confidence intervals and prediction for multiple regression.
10 Model diagnostics. Outlier detection. Types of residuals. Influence diagnostics. Multicollinearity. Assignment 2
11 Model selection and criteria for comparing models.
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


 

 

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 Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Wattle Quiz 5 % 25/03/2019 29/03/2019 2, 3
Assignment 1 (Simple Linear Regression) 15 % 05/04/2019 26/04/2019 1-3
Assignment 2 (Multiple Regression) 20 % 17/05/2019 03/06/2019 1-5
Final Examination 60 % 06/06/2019 04/07/2019 1-6

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. Students may choose not to submit assessment items through Turnitin. In this instance you will be required to submit, alongside the assessment item itself, hard copies of all references included in the assessment item.

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.

Assessment Task 1

Value: 5 %
Due Date: 25/03/2019
Return of Assessment: 29/03/2019
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 a short window in that week, more details will be provided during the lectures and on the wattle page. This assessment is not redeemable.

Assessment Task 2

Value: 15 %
Due Date: 05/04/2019
Return of Assessment: 26/04/2019
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.

Assessment Task 3

Value: 20 %
Due Date: 17/05/2019
Return of Assessment: 03/06/2019
Learning Outcomes: 1-5

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 to be completed individually and not redeemable.

Assessment Task 4

Value: 60 %
Due Date: 06/06/2019
Return of Assessment: 04/07/2019
Learning Outcomes: 1-6

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/.

Academic Integrity

Academic integrity is a core part of our culture as a community of scholars. At its heart, academic integrity is about behaving ethically. This means that all members of the community commit to honest and responsible scholarly practice and to upholding these values with respect and fairness. The Australian National University commits to embedding the values of academic integrity in our teaching and learning. We ensure that all members of our community 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 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 University has policies and procedures in place to promote academic integrity and manage academic misconduct. Visit the following Academic honesty & plagiarism website for more information about academic integrity and what the ANU considers academic misconduct. The ANU offers a number of services to assist students with their assignments, examinations, and other learning activities. The Academic Skills and Learning Centre offers a number of workshops and seminars that you may find useful for your studies.

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) as 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. The Course Convener may grant extensions 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.
In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
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.

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).

Abhinav Mehta
02 6125 1081
U4356489@anu.edu.au

Research Interests


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

Abhinav Mehta

Tuesday 15:00 16:00
Tuesday 15:00 16:00
Wednesday 15:00 16:00
Abhinav Mehta
6125 1081
Abhinav.Mehta@anu.edu.au

Research Interests


Abhinav Mehta

Tuesday 15:00 16:00
Tuesday 15:00 16:00
Wednesday 15:00 16:00

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