Unit of study_

# MATH1005: Statistical Thinking with Data

## 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 you with the foundational statistical thinking to become a critical consumer of data. You 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.

### Details

Academic unit Mathematics and Statistics Academic Operations MATH1005 Statistical Thinking with Data Semester 2, 2021 Normal day Remote 3

### Enrolment rules

 Prohibitions ? MATH1015 or MATH1905 or STAT1021 or ECMT1010 or ENVX1001 or ENVX1002 or BUSS1020 or DATA1001 or DATA1901 None None HSC Mathematics Advanced or equivalent. Yes

### Teaching staff and contact details

Coordinator John Ormerod, john.ormerod@sydney.edu.au Andy Tran MATH1005@sydney.edu.au Mathematics and Statistics Student Services Office, Carslaw 520

## Assessment

Type Description Weight Due Length
Final exam (Record+) Exam
Multiple Choice and Extended Answer Questions
50% Formal exam period 1.5 hours
Outcomes assessed:
Assignment Project 1
See Canvas
20% Week 08
Due date: 08 Oct 2021 at 23:59

Closing date: 22 Oct 2021
Project
Outcomes assessed:
Assignment Project 2
See Canvas
20% Week 12
Due date: 05 Nov 2021 at 23:59

Closing date: 19 Nov 2021
Project
Outcomes assessed:
Assignment Revision Quiz
Multiple Choice Revision Questions
10% Weekly Weekly Revision Quiz
Outcomes assessed:
= group assignment
= Type B final exam
• Weekly quizzes: The best 10 out of 13 quizzes count. The better mark principle will be used for the weekly quizzes so do not submit an application for Special Consideration or Special Arrangements if you miss a quiz. The better mark principle means that the total quiz mark counts if and only if it is better than or equal to your exam mark. If your quiz mark is less than your exam mark, the exam mark will be used for that portion of your assessment instead.
• Projects: Details about the projects and instructions for submission will be given on Canvas. Penalties apply for late submission. A mark of zero will be awarded for all submissions more than 10 days past the original due date. Further extensions past this time will not be permitted.
• Examination: Details will be provided later.

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.

### 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.

### Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

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.

## Weekly schedule

WK Topic Learning activity Learning outcomes
Week 01 Design of Experiments: Controlled experiments + Observational studies Lecture (2 hr)
Lab 1 Computer laboratory (1 hr)
Week 02 Data & Graphical Summaries: Qualitative + Quantitative data Lecture (2 hr)
Lab 2 Computer laboratory (1 hr)
Week 03 Data & Graphical Summaries: Centre + Spread Lecture (2 hr)
Lab 3 Computer laboratory (1 hr)
Week 04 Numerical Summaries: Normal Model + Reproducible Reports Lecture (2 hr)
Lab 4 Computer laboratory (1 hr)
Week 05 Linear Model: Scatter Plot & Correlation + Regression line Lecture (2 hr)
Lab 5 Computer laboratory (1 hr)
Week 06 Understanding Chance: Chance & Simulations Lecture (2 hr)
Lab 6 Computer laboratory (1 hr)
Week 07 Chance Variability: Law of averages and sums + The Box Model Lecture (2 hr)
Lab 7 Computer laboratory (1 hr)
Week 08 Chance Variability: Normal approximation Lecture (2 hr)
Lab 8 Computer laboratory (1 hr)
Week 09 Sample Surveys: The Box Model for Sample Surveys & Simulations Lecture (2 hr)
Lab 9 Computer laboratory (1 hr)
Week 10 Testing: Hypothesis testing & Z Test Lecture (2 hr)
Lab 10 Computer laboratory (1 hr)
Week 11 Testing: One and Two Sample T Tests Lecture (2 hr)
Lab 11 Computer laboratory (1 hr)
Week 12 Testing: Other Tests Lecture (2 hr)
Lab 12 Computer laboratory (1 hr)
Week 13 Revision Lecture (2 hr)

### Attendance and class requirements

Due to the exceptional circumstances caused by the COVID-19 pandemic, attendance requirements for this unit of study have been amended. Where online tutorials/workshops/virtual laboratories have been scheduled, students should make every effort to attend and participate at the scheduled time. Penalties will not be applied if technical issues, etc. prevent attendance at a specific online class.

### 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 3 credit point unit, this equates to roughly 60-75 hours of student effort in total.

We will loosely follow Statistics, Freedman, Pisani, and Purves (2007). Examples of how to get access to the text book:

## 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.

At the completion of this unit, you 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 oral reproducible reports

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

GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

## Closing the loop

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

### 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.

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