• Class Number 4240
• Term Code 3230
• Class Info
• Unit Value 6 units
• Mode of Delivery In Person
• COURSE CONVENER
• Dr Anton Westveld
• LECTURER
• Dr Anton Westveld
• Class Dates
• Class Start Date 21/02/2022
• Class End Date 27/05/2022
• Census Date 31/03/2022
• Last Date to Enrol 28/02/2022
SELT Survey Results

Statistical Inference (STAT3013)

This course introduces students to the basic theory behind the development and assessment of statistical analysis techniques in the areas of point and interval estimation, as well as hypothesis testing.  Topics include:
* Point estimation methods, including method of moments and maximum likelihood, bias and variance, mean-squared error, sufficiency, completeness, exponential families, the Cramer-Rao inequality, the Rao-Blackwell theorem, uniformly minimum variance unbiased estimators, and Bayesian estimation methods.
* Confidence interval construction methods, including likelihood-based intervals, inversion methods, intervals based on pivots, Bayesian credible and highest posterior density regions, and resampling based intervals.
* Hypothesis testing methods, including likelihood ratio tests, the Neymann-Pearson lemma and uniformly most powerful tests, power calculations, Bayesian approaches, and non-parametric approaches.

## Learning Outcomes

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

1. Explain the notion of a parametric model and point estimation of the parameters of those models;
2. Explain and apply approaches to include a measure of accuracy for estimation procedures and our confidence;
3. Assess the plausibility of pre-specified ideas about the parameters of a model by examining the area of hypothesis testing;
4. Explain and apply non-parametric statistics; and,
5. Discuss the computational issues related to the implementation of various statistical inferences.

## Research-Led Teaching

The topic of statistical inference seeks to provide answers to questions of point estimation, interval estimation, and hypothesis testing, based on observable data. Not surprisingly, through the development of the topic of statistical inference, there exist diverse approaches to these problems. Examination and application of these diverse approaches will provide insight into the past and potentially future development of statistical science.

A computer which is able to operate the current versions of R and RStudio.

## Examination Material or equipment

The mid-semester assignment and final exam will be open book. Access to a computer which is able to operate the current versions of R and RStudio will be necessary.

## Required Resources

Required Texts:

• Efron and Hastie. 2016. Computer Age Statistical Inference. Cambridge University Press.

• A set of notes by Professor Stern. These pdfs will be placed on Wattle.

## Staff Feedback

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

• Written comments, both individually as well as to the whole class.

• Verbal comments to the whole class.

## 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

Assessment Requirements

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.

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 and may be either up or down.

Co-teaching

The courses STAT3013, STAT4027, and STAT8027 are co-taught.

## Class Schedule

Week/Session Summary of Activities Assessment
1 Introduction
2 Properties of estimators
3 Properties of estimators Tutorial questions
4 Maximum likelihood and other methods of estimation Tutorial questions
5 Maximum likelihood and other methods of estimation Quiz
6 Hypothesis testing
7 Hypothesis testing Mid-semester Assignment
8 Interval estimation Tutorial questions
9 Bayesian inference & decision theory Tutorial questions
10 Bayesian inference & decision theory Tutorial questions
11 Non-parametric methods
12 Non-parametric methods Project

## Tutorial Registration

Tutorials will be available on campus, live through scheduled Zoom sessions and as pre-recorded videos. information regarding enrolments for these options will be provided on Wattle no later than week one of the semester. Enrolments for these options will be provided on Wattle no later than week one of the semester.

## Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Tutorial Questions 5 % 07/03/2022 29/05/2022 1-5
Quiz 0 % 25/03/2022 01/04/2022 1
Mid-Semester Assignment (48 hours to complete) 20 % 22/04/2022 08/05/2022 1,2,3
Presentation/Project 15 % 27/05/2022 12/06/2022 1-5
Final Exam 60 % 02/06/2022 30/06/2022 1-5

* 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.

## Participation

The lectures will be delivered either on campus (recorded and available via echo360 on Wattle), live and recorded through Zoom, or on occasion as prerecorded videos. Consultations will be live through Zoom. Tutorials will be available on campus, live through scheduled Zoom sessions, and as prerecorded videos. Information regarding enrolments for these options will be provided during O-week, prior to the start of the semester.

## Examination(s)

As further academic integrity control, students may be selected for a 15-minute individual oral examination of their written assessment submissions.

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: 07/03/2022
Return of Assessment: 29/05/2022
Learning Outcomes: 1-5

Tutorial Questions

Before five of the weekly tutorial sessions, at the beginning of those weeks (see the class overview and Wattle for the exact date and time), you will submit your answers to tutorial questions online via Wattle. These will be graded for “performance” (whether you reasonably demonstrated the concepts) and not whether you got the answer correct. Each week the “performance” will be graded as 0 or 100. Students may be asked to present their solutions during tutorial.

Value: 0 %
Due Date: 25/03/2022
Return of Assessment: 01/04/2022
Learning Outcomes: 1

Quiz

There will be an online Wattle quiz which will be available at the beginning of Week 5. The quiz will be available for one week. The results will be made available by the end of Week 6.

Value: 20 %
Due Date: 22/04/2022
Return of Assessment: 08/05/2022
Learning Outcomes: 1,2,3

Mid-Semester Assignment (48 hours to complete)

The mid-semester assignment will cover material from Weeks 1-6 and will be held during Week 7. Specifically the assignment will be posted on Wattle on 2022-04-20 at 11:00 am and will be due by 2022-04-22 at 11:00 am.

Students are expected to complete this assignment individually and fully adhere to the academic integrity principles laid out by ANU. The assignment will require the use of R and RStudio. The assignment will be submitted via Turnitin.

Value: 15 %
Due Date: 27/05/2022
Return of Assessment: 12/06/2022
Learning Outcomes: 1-5

Presentation/Project

In groups of 2-5 (exact size TBD), based on your cohort (STAT3013 or STAT4027/STAT8027), you will read and present an academic paper. In addition, you will have to consider some type of “extension”. This may be by simplifying the problem and considering another estimator and its properties, extending the inferential method, or even considering applying the method to other data sets. Each presentation will last 10-25 minutes (TBD) and each member of the group must speak based on the part of the presentation where they provided the most input (in order to demonstrate individual skills). Students will give their presentations via pre-recorded video. All paper choices and groups must be approved by the lecturer. Please adhere to covid safe practices (required social distancing requirements, e.g. using Zoom for meetings etc.).

Value: 60 %
Due Date: 02/06/2022
Return of Assessment: 30/06/2022
Learning Outcomes: 1-5

Final Exam

The exam will be during the final examination period and will be 3 and 1/2 hours in length (this includes reading and submission time). The exact coverage of the exam will be made known in Week 12 and will be discussed in lecture, as well as posted on Wattle. ANU's Examination Office will set the date and time of the exam. The exam will be open book and will require the use of R and RStudio. The exam will either be submitted via Turnitin or through Wattle's quiz function - this exact approach will be discussed in Week 12. A practice exam with the ability to practice submission of answers will be made available in Week 12. While Proctorio (or similar invigilation approaches) will not be used, students are expected to complete this assignment individually and fully adhere to the academic integrity principles laid out by ANU.

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

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

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 or the project 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

 Dr Anton Westveld +612 6125 5122? anton.westveld@anu.edu.au

### Research Interests

Research interests include Bayesian methodology and theory and statistical methods for interaction/relational data.

### Dr Anton Westveld

 Thursday 11:00 12:00 Thursday 11:00 12:00

## Instructor

 Dr Anton Westveld +612 6125 5122? anton.westveld@anu.edu.au

### Dr Anton Westveld

 Thursday 11:00 12:00 Thursday 11:00 12:00