STAT2911: Semester 1, 2021

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Units STAT2911 Semester 1 2021 [Normal day]

Unit of study_

STAT2911: Probability and Statistical Models (Adv)

Semester 1, 2021 [Normal day] - Remote

Overview

This unit is essentially an advanced version of STAT2011, with an emphasis on the mathematical techniques used to manipulate random variables and probability models. Common distributions including the Poisson, normal, beta and gamma families as well as the bivariate normal are introduced. Moment generating functions and convolution methods are used to understand the behaviour of sums of random variables. The method of moments and maximum likelihood techniques for fitting statistical distributions to data will be explored. The notions of conditional expectation and prediction will be covered as will be distributions related to the normal: chi^2, t and F. The unit has weekly computer classes where you will learn to use a statistical computing package to perform simulations and carry out computer intensive estimation techniques like the bootstrap method.

Unit details and rules

Unit code	STAT2911
Academic unit	Mathematics and Statistics Academic Operations
Credit points	6
Prohibitions ?	STAT2011
Prerequisites ?	(MATH1X21 or MATH1931 or MATH1X01 or MATH1906 or MATH1011) and a mark of 65 or greater in (DATA1X01 or MATH10X5 or MATH1905 or STAT1021 or ECMT1010 or BUSS1020)
Corequisites ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Uri Keich, uri.keich@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Record+)	Final exam Final exam	70%	Formal exam period	2 hours
Outcomes assessed:
Online task	Quiz 1 Online Quiz	5%	Week 05 Due date: 31 Mar 2021 at 12:00	50 mins
Outcomes assessed:
Assignment	Assignment 1 Answers with calculations	2.5%	Week 06 Due date: 16 Apr 2021 at 23:59	n/a
Outcomes assessed:
Online task	Quiz 2 Online Quiz	5%	Week 11 Due date: 20 May 2021 at 12:00	50 mins
Outcomes assessed:
Assignment	Assignment 2 Answers with calculations	2.5%	Week 12 Due date: 28 May 2021 at 23:59	n/a
Outcomes assessed:
Small test	Computer exam Computer test	10%	Week 13	40 mins
Outcomes assessed:
Assignment	Computer work Weekly lab assignments submitted at the end of the week.	4%	Weekly	n/a
Outcomes assessed:
Assignment	Weekly tutorial problems Weekly tutorial problems submitted prior to the tutorial	1%	Weekly	N/A
Outcomes assessed:
= Type B final exam ?

Assessment summary

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100	Representing complete or close to complete mastery of the material.
Distinction	75 - 84	Representing excellence, but substantially less than complete mastery.
Credit	65 - 74	Representing a creditable performance that goes beyond routine knowledge and understanding, but less than excellence.
Pass	50 - 64	Representing at least routine knowledge and understanding over a spectrum of topics and important ideas and concepts in the course.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Weekly schedule

WK	Topic	Learning activity
Week 01	1. Introduction; 2. Sample spaces and the algebra sets; 3. The probability function; 4. Conditional probability	Lecture and tutorial (5 hr)
Week 02	1. Independence; 2. Combinatorics	Lecture and tutorial (5 hr)
Week 03	1. Combinatorial probability; 2. Taking a second look at the statistics; 3. Introduction; 4. Binomial probability function; 5. The hypergeometric distribution; 6. Discrete random variables	Lecture and tutorial (5 hr)
Week 04	1. Continuous random variables; 2. Expected values	Lecture and tutorial (5 hr)
Week 05	1. Variance; 2. Joint densities	Lecture and tutorial (5 hr)
Week 06	1. Joint densities; 2. Transforming and combining random variables; 3. Further properties of the mean and variance	Lecture and tutorial (5 hr)
Week 07	1. Order statistics; 2. Conditional densities	Lecture and tutorial (5 hr)
Week 08	1. Moment-generating functions; 2. The Poisson distribution; 3. The Normal distribution	Lecture and tutorial (5 hr)
Week 09	1. The Normal distribution; 2. The Geometric distribution; 3. The negative binomial distribution	Lecture and tutorial (5 hr)
Week 10	1. The Gamma distribution; 2. Estimating parameters	Lecture and tutorial (5 hr)
Week 11	1. Estimating parameters; 2. Interval estimation	Lecture and tutorial (5 hr)
Week 12	1. Interval estimation; 2. Properties of estimation	Lecture and tutorial (5 hr)
Week 13	Revision	Lecture and tutorial (5 hr)
Weekly	Computer lab	Computer laboratory (1 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. construct appropriate statistical models involving random variables for a range of modelling scenarios. Compute (or approximate with a computer if necessary) numerical characteristics of random variables in these models such as probabilities, expectations and variances
LO2. fit such models in outcome 1. to data (as appropriate) by estimating any unknown parameters
LO3. compute appropriate (both theoretically and computationally derived) measures of uncertainty for any parameter estimates
LO4. assess the goodness of fit (as appropriate) of a fitted model
LO5. apply certain mathematical results (e.g. inequalities, limiting results) to problems relating to statistical estimation theory
LO6. prove certain mathematical results (e.g. inequalities, limiting results) used in the course.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

No changes have been made since this unit was last offered

Additional information

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

General Laboratory Safety Rules

No eating or drinking is allowed in any laboratory under any circumstances
A laboratory coat and closed-toe shoes are mandatory
Follow safety instructions in your manual and posted in laboratories
In case of fire, follow instructions posted outside the laboratory door
First aid kits, eye wash and fire extinguishers are located in or immediately outside each laboratory
As a precautionary measure, it is recommended that you have a current tetanus immunisation. This can be obtained from University Health Service: unihealth.usyd.edu.au/

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

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