Unit outline_

# 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.

### Unit details and rules

Academic unit Mathematics and Statistics Academic Operations 3 None None MATH1015 or MATH1905 or STAT1021 or ECMT1010 or ENVX1001 or ENVX1002 or BUSS1020 or DATA1001 or DATA1901 HSC Mathematics Advanced or equivalent Yes

### Teaching staff

Coordinator Clara Grazian, clara.grazian@sydney.edu.au Clara Grazian

## Assessment

Type Description Weight Due Length
Supervised exam

Exam
Multiple choice questions and extended answer questions.
50% Formal exam period 1.5 hours
Outcomes assessed:
Assignment Project 1
See Canvas
20% Week 08
Due date: 22 Sep 2023 at 23:59

Closing date: 03 Oct 2023
word limit 1450
Outcomes assessed:
Assignment Project 2
See Canvas
20% Week 11
Due date: 20 Oct 2023 at 23:59

Closing date: 30 Oct 2023
word limit 1050
Outcomes assessed:
Small test Revision Quiz
Multiple Choice Revision Questions
8% Weekly Weekly Revision Quiz
Outcomes assessed:
Participation Labs
Participation in lab classes
2% Weekly 1 hour/week
Outcomes assessed:
= group assignment

### Assessment summary

• Revision quizzes: Each week 3 revision quizzes are posted on Monday and they are due by the following Sunday. The best 10 out of 12 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.
• Final Examination: The final exam for this unit is compulsory and must be attempted. Failure to attempt the final exam will result in an AF grade for the course. Further information about the exam will be made available at a later date on Canvas. If a second replacement exam is required, this exam may be delivered via an alternative assessment method, such as a viva voce (oral exam). The alternative assessment will meet the same learning outcomes as the original exam. The format of the alternative assessment will be determined by the unit coordinator.
• Lab Participation: This is a satisfactory/non-satisfactory mark assessing whether or not you participate in class activities during the labs. It is 0.25 marks per lab class up to 8 labs (there are 12 labs).

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.

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 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

• Lecture attendance: You are expected to attend lectures. If you do not attend lectures you should at least follow the lecture recordings available through Canvas.
• Lab attendance: Labs (one per week) start in Week 1. You should attend the lab given on your personal timetable. Attendance at tutorials and participation will be recorded to determine the participation mark. Your attendance will not be recorded unless you attend the lab in which you are enrolled. We strongly recommend you attend labs regularly to keep up with the material and to engage with the lab questions.

### 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.

There are no required readings. However, 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

## Responding to student feedback

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

• Lectures: Lectures are face-to-face and streamed live with online access from Canvas.
• Labs: Labs are small classes in which you are expected to work through questions from the tutorial sheet.
• Ed Discussion forum: https://edstem.org

### 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.