• Class Number 5648
• Term Code 2940
• Class Info
• Unit Value 6 units
• Mode of Delivery In Person
• COURSE CONVENER
• Dr Haijun Gong
• LECTURER
• Dr Haijun Gong
• Class Dates
• Class Start Date 27/05/2019
• Class End Date 26/07/2019
• Census Date 14/06/2019
• Last Date to Enrol 31/05/2019
SELT Survey Results

Applied Statistics (STAT7001)

Statistics 3008/7001 (Applied Statistics) is a course designed for senior undergraduate and research students who need to design experiments and carry out statistical analysis of their data. Emphasis will be placed on the development of statistical concepts and statistical computing, rather than mathematical details. The content covered will be motivated by problem-solving in many diverse areas of application. The topics covered will include regression modelling with emphasis on model formulation, understanding the implication of model assumptions, diagnostic methods for model checking and interpretation, logistic regression for binary variables and binomial counts, log-linear regression for Poisson counts, and exploratory tools for summarising multivariate responses.

## Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

1. Demonstrate a deep understanding and usage of the statistical computing package R.
2. Fit simple and multiple linear regression models and demonstrate model parameters.
3. Explain in detail the relationships between a response variable and a covariate or covariates.
4. Evaluate and Improve simple and multiple linear regression models based on - Perform diagnostic measures.
5. Perform model selection in a multiple linear regression modelling context.
6. Perform logistic and Poisson log-linear regression models.
7. Demonstrate multivariate analyses techniques and the bootstrap.

## Research-Led Teaching

This course builds upon the foundation in statistical thinking and evidence-based logic that you have acquired from previous statistics courses. The course contents and activities are designed to help you to learn to apply and hone this foundation to achieve the learning outcomes, and to prepare yourself for the remainder of your academic program and life in the workforce. Course contents and activities heavily involve statistical computing with R interfaced through R Studio. Additional materials (e.g. new reports, journal publications) will be discussed as examples and case studies in which research questions relevant to the course are tackled.

## Required Resources

You must make your own arrangements to have access to computing equipment and R and RStudio; these are available as standard on ANU campus computers. More instructions about the software can be found on Wattle.

The following book is not compulsory.

Recommended Text

"The Statistical Sleuth", 3rd Edition, by Ramsey/Schafer. This book is available in the University bookstore, and is also available for borrowing on short-term loan from the ANU library during the period of the course.

Other recommended resources will be listed on the Wattle course page.

## Staff Feedback

Feedback from the teaching staff will aim to facilitate the learner's ongoing self assessment of his/her progress in achieving the learning objectives of the course. There are several ways to get feedback from the teaching staff:

(a) to benefit all the students, teaching staff strongly suggests the learners post course-related question publicly on the discussion forum of Wattle so that the teaching staff can reply those questions publicly.

(b) those students who have private things to discuss can email the teaching staff for consultation.

(c) students can also make an appointment by email to schedule a face-to-face consultation on campus or online consultation by Zoom or Skype.

Due to most students being off campus during the non-intensive period of the course, no formal consultation time has been set - however, the above options give students the opportunity to arrange consultation when they see necessary. You will be able to arrange consultation as appropriate during the intensive week as well - further information will be provided on Wattle.

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 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

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.

## Class Schedule

Week/Session Summary of Activities Assessment
1 Pre- intensive period May 27 – Jun 23 Students watch recorded video and self-study additional learning materials published on Wattle. In this period, we will discuss the following topics: Introduction and getting to know R; Review of fundamentals linear algebra and statistics; Simple linear regression; Multiple linear regression; Model Diagnostics for linear regression; Bias-Variance Decomposition; Variable Selection Assignment 1 (10%): posted on May 27, due on June 9. Assignment 2 (10%): posted on June 9, due on June 23. Late submissions will receive 0 marks. Note: 11:59pm Canberra time
2 Intensive Jun 24 – 28 In the intensive period, we will discuss the following topics: Logistic regression; Nonlinear regression and robust regression; Generalized linear model; Log-linear regression; Bootstrap; Principal Component Analysis, etc. A combination of lectures and tutorials will be held during the intensive week. Refer to Wattle for further information one week before the intensive week. Assignment 3 (20%): posted on June 24, due on July 14 (after Intensive period). Late submissions will receive 0 marks. Note: 11:59pm Canberra time
3 Post- intensive June 29 –July 26 In this period, we will discuss some beyond standard regression methods. Students watch recorded video and self-study additional learning materials published on Wattle. Assignment 3 will be due on July 14. Individual term project report (60%) is due on July 26 by 11:59pm Canberra time. Late submission will receive 0 marks.

## Tutorial Registration

This course runs in intensive mode, with attendance required for the intensive period June 24-28. No sign up is required. Further information is on Wattle.

## Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Assignment 1 10 % 09/06/2019 23/06/2019 1-4
Assignment 2 10 % 23/06/2019 07/07/2019 1, 4-5
Assignment 3 20 % 14/07/2019 28/07/2019 1, 6-7
Term Project Report 60 % 26/07/2019 09/08/2019 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 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.

Value: 10 %
Due Date: 09/06/2019
Return of Assessment: 23/06/2019
Learning Outcomes: 1-4

Assignment 1

Students are expected to complete the assignment individually. This assignment is designed to cover the topics about simple linear regression and multiple regression. Detailed instructions will be announced on Wattle.

Value: 10 %
Due Date: 23/06/2019
Return of Assessment: 07/07/2019
Learning Outcomes: 1, 4-5

Assignment 2

Students are expected to complete the assignment individually. This assignment is designed to cover the topics about variable selection. Detailed instructions will be announced on Wattle.

Value: 20 %
Due Date: 14/07/2019
Return of Assessment: 28/07/2019
Learning Outcomes: 1, 6-7

Assignment 3

Students are expected to complete the assignment individually. This assignment is designed to cover the topics discussed in the intensive week. Detailed instructions will be announced on Wattle.

Value: 60 %
Due Date: 26/07/2019
Return of Assessment: 09/08/2019
Learning Outcomes: 1-7

Term Project Report

Students are expected to apply at least three regression methods learned from the class to analyze some real-world data and submit one report individually. The project report should be at least 5 pages, including the introduction, methods, results, conclusion/discussion, and reference. Detailed instructions will be announced on Wattle.

## 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.

Each assignment must be submitted as a single electronic file using Turnitin within the course Wattle site. Each of these assessments will require both statistical computing via R Studio and mathematical derivations (either handwritten or typeset using R Studio). If submitting handwritten mathematical derivations, ensure that your handwriting is legible, and scan the derivations (e.g., by using your smartphone camera) to be incorporated into your single electronic file. Prior to submission, you should practice using the Turnitin system here. Students can upload draft versions to the designated Turnitin web link on the Wattle course page, and change those drafts every 24 hours up until the due date.

Submissions outside of the designated Turnitin web link and/or after the due date will be ignored.

## Hardcopy Submission

Assessment submission for this course will be online (see above).

## 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, a mark of 0 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 should be available via Turnitin within 14 days after the due date. Written feedback will be published on Wattle.

## 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

Resubmission of assessments is not allowed under any circumstance.

## Privacy Notice

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 Haijun Gong 6125 0753 haijun.gong@anu.edu.au

### Research Interests

Bayesian Statistics, Machine learning, Model Checking, Bioinformatics

### Dr Haijun Gong

 Monday 00:00 00:00

## Instructor

 Dr Haijun Gong 6125 0753 haijun.gong@anu.edu.au

### Dr Haijun Gong

 Monday 00:00 00:00