ECMT1010: Semester 2, 2022

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Units ECMT1010 Semester 2 2022 [Normal day]

Unit outline_

ECMT1010: Introduction to Economic Statistics

Semester 2, 2022 [Normal day] - Camperdown/Darlington, Sydney

Overview

This unit emphasises understanding the use of computing technology for data description and statistical inference. Both classical and modern statistical techniques such as bootstrapping will be introduced. Students will develop an appreciation for both the usefulness and limitations of modern and classical theories in statistical inference. Computer software (e.g., Excel, StatKey) will be used for analysing real datasets.

Unit details and rules

Academic unit	Economics
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	ECMT1011 or ECMT1012 or ECMT1013 or MATH1015 or MATH1005 or MATH1905 or STAT1021 or ECOF1010 or BUSS1020 or ENVX1001 or DATA1001
Assumed knowledge ?	Students enrolled in this unit have an assumed knowledge equal to or exceeding 70 or higher in HSC Mathematics (or equivalent), or 35 or higher in HSC Mathematics Extension 1 (or equivalent), or 35 or higher in HSC Mathematics Extension 2 (or equivalent)
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Timothy Fisher, tim.fisher@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Record+)	Final exam Multiple-choice and short-answer questions	40%	Formal exam period	2 hours
Outcomes assessed:
Online task	Online quizzes Multiple-response questions	15%	Multiple weeks	Varies
Outcomes assessed:
In-semester test (Record+)	In-semester test Multiple-choice and short-answer questions	30%	Week 07 Due date: 12 Sep 2022 at 10:00	1.5 hours
Outcomes assessed:
Online task	Assignment Short-answer questions	15%	Week 13 Due date: 04 Nov 2022 at 18:00	Varies
Outcomes assessed:
= Type B final exam ? = Type B in-semester 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
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

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.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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	Introduction	Lecture (2 hr)
Week 01	Week 1 workshop	Workshop (2 hr)
Week 02	Collecting data	Lecture (2 hr)
Week 02	Week 2 workshop	Workshop (2 hr)
Week 03	Describing data	Lecture (2 hr)
Week 03	Week 3 workshop	Workshop (2 hr)
Week 04	Sampling distribution, confidence intervals, bootstrapping	Lecture (2 hr)
Week 04	Week 4 workshop	Workshop (2 hr)
Week 05	Bootstrap confidence intervals	Lecture (2 hr)
Week 05	Week 5 workshop	Workshop (2 hr)
Week 06	Introduction to hypothesis tests	Lecture (2 hr)
Week 06	Week 6 workshop	Workshop (2 hr)
Week 07	In-semester test	Individual study (1.5 hr)
Week 08	Hypothesis testing, the normal distribution	Lecture (2 hr)
Week 08	Week 8 workshop	Workshop (2 hr)
Week 09	Approximating with a distribution, inference for means and proportions (one population)	Lecture (2 hr)
Week 09	Week 9 workshop	Workshop (2 hr)
Week 10	Inference for means and proportions (two populations)	Lecture (2 hr)
Week 10	Week 10 workshop	Workshop (2 hr)
Week 11	Inference for regression	Lecture (2 hr)
Week 11	Week 11 workshop	Workshop (2 hr)
Week 12	Probability basics, Bayes’ rule	Lecture (2 hr)
Week 12	Week 12 workshop	Workshop (2 hr)
Week 13	Random variables, probability functions, binomial probabilities	Lecture (2 hr)
Week 13	Week 13 workshop	Workshop (2 hr)

Attendance and class requirements

Attendance: According to Faculty Board Resolutions, students in the Faculty of Arts and Social Sciences are expected to attend 90% of their classes. If you attend less than 50% of classes, regardless of the reasons, you may be referred to the Examiner’s Board. The Examiner’s Board will decide whether you should pass or fail the unit of study if your attendance falls below this threshold.
Lecture recording: Most lectures (in recording-equipped venues) will be recorded and may be made available to students on the LMS. However, you should not rely on lecture recording to substitute your classroom learning experience.
Preparation: Students should commit to spend approximately three hours’ preparation time (reading, studying, homework, essays, etc.) for every hour of scheduled instruction.

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.

Required readings

All readings for this unit can be accessed on the Library link available on Canvas.

Robin H. Lock, Patti Frazer Lock, Kari Lock Morgan, Eric F. Lock, Dennis F. Lock, Statistics: Unlocking the power of data, 2nd Edition, Wiley.

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. describe data using simple statistics and visual tools
LO2. understand the concept of statistical inference as a systematic approach to infer the population parameters using sample data
LO3. demonstrate proficiency in the use of software for data analysis
LO4. appreciate the importance of correct and responsible use of data
LO5. implement appropriate statistical inference procedures in various applications in economics and social sciences and effectively interpret the result to a wide audience.

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.

Workshop questions have been updated.

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

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

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