Program Overview

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Overview

In a data-rich world, global citizens need to problem solve with data, and evidence-based decision-making is essential in every field of research and work. This unit equips students with the foundational statistical thinking to become a critical consumer of data. Students will learn to think analytically about data and to evaluate the validity and accuracy of any conclusions drawn. Focusing on statistical literacy, the unit covers foundational statistical concepts, including the design of experiments, exploratory data analysis, sampling, and tests of significance.

Unit Details and Rules

• Academic unit: Mathematics and Statistics Academic Operations
• Credit points: 3
• Prerequisites: None
• Corequisites: None
• Prohibitions: MATH1015 or MATH1905 or STAT1021 or ECMT1010 or ENVX1001 or ENVX1002 or BUSS1020 or DATA1001 or DATA1901
• Assumed knowledge: HSC Mathematics. Students who have not completed HSC Mathematics (or equivalent) are strongly advised to take the Mathematics Bridging Course (offered in February).
• Available to study abroad and exchange students: Yes

Teaching Staff

• Coordinator: Munir Hiabu
• Lecturer(s): Parinaz Ezzati

Assessment

• Type: Final exam (Open book)
  • Description: Exam Multiple Choice and Extended Answer Questions
  • Weight: 50%
  • Due: Formal exam period
  • Length: 1.5 hours
  • Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9
• Type: Assignment
  • Description: Project 1 See Canvas
  • Weight: 20%
  • Due: Week 06
  • Length: Project
  • Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10
• Type: Assignment
  • Description: Project 2 See Canvas
  • Weight: 20%
  • Due: Week 11
  • Length: Project
  • Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9
• Type: Assignment
  • Description: Revision Quiz Multiple Choice Revision Questions
  • Weight: 10%
  • Due: Weekly
  • Length: Weekly Revision Quiz
  • Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9

Assessment Summary

The better mark principle will be used for the weekly quizzes so students should not submit an application for Special Consideration or Special Arrangements if they miss a quiz. The better mark principle means that the quiz counts if and only if it is better than or equal to the exam mark. If the quiz mark is less than the exam mark, the exam mark will be used for that portion of the assessment instead.

Assessment Criteria

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

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

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.

Weekly Schedule

Week	Topic	Learning activity	Learning outcomes
Week 01	Design of Experiments: Controlled experiments + Observational studies	Lecture (2 hr)	LO1 LO2
		Lab 1	LO1 LO2
Week 02	Data & Graphical Summaries: R & Qualitative data	Lecture (2 hr)	LO3
		Lab 2	LO3 LO10
Week 03	Data & Graphical Summaries: R & Quantitative data	Lecture (2 hr)	LO3
		Lab 3	LO3 LO10
Week 04	Numerical Summaries: Centre + Spread	Lecture (2 hr)	LO3
		Lab 4	LO3 LO10
Week 05	Linear Model: Scatter Plot & Correlation + Regression line	Lecture (2 hr)	LO5
		Lab 5	LO5
Week 06	Understanding Chance: Chance & Simulations	Lecture (2 hr)	LO6
		Lab 6 (Project Work)	LO6 LO10
Week 07	Chance Variability: Law of averages and sums + The Box Model	Lecture (2 hr)	LO6
		Lab 7 (Project Presentation)	LO6 LO10
Week 08	Chance Variability: Normal curve + Normal approximation	Lecture (2 hr)	LO4
		Lab 8	LO4
Week 09	Sample Surveys: The Box Model for Sample Surveys & Simulations	Lecture (2 hr)	LO1 LO6 LO9
		Lab 9	LO6 LO9
	Testing: Hypothesis testing & Simulations	Lecture (2 hr)	LO6 LO7 LO8
Week 10	Lab 10	Computer laboratory (1 hr)	LO7 LO8
Week 11	Testing: Z and T Tests	Lecture (2 hr)	LO7 LO8
		Lab 11	LO7 LO8
Week 12	Test for a Relationship: 2 Sample T Test	Lecture (2 hr)	LO7 LO8
		Lab 12	LO7 LO8

Learning Outcomes

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

• LO1. articulate the importance of statistics in a data-rich world
• LO2. identify the study design behind a dataset and how the study design affects context-specific outcomes
• LO3. produce, interpret and compare graphical and numerical summaries in R
• LO4. apply the normal approximation to data, with consideration of measurement error
• LO5. model the relationship between 2 variables using linear regression
• LO6. use the box model to describe chance and chance variability, including sample surveys and the central limit theorem
• LO7. given real multivariate data and a problem, formulate an appropriate hypothesis and perform a hypothesis test
• LO8. interpret the p-value, conscious of the various pitfalls associated with testing
• LO9. critique the use of statistics in media and research papers, with attention to confounding and bias
• LO10. perform data exploration in a team, and communicate the findings via oral and written reproducible reports

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 has been designed to equip students 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 ---|--- GQ1 | GQ2 | GQ3 | GQ4 | GQ5 | GQ6 | GQ7 | GQ8 | GQ9

Responding to Student Feedback

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

Weight of projects towards final mark increased. Weight of final exam decreased.

Additional Information

• Science student portal
• Mathematics and Statistics student portal

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.

Disclaimer

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

This unit of study outline was last modified on 15 Dec 2020.

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