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 is 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

Unit code MATH1005 Mathematics and Statistics Academic Operations 3 MATH1015 or MATH1905 or STAT1021 or ECMT1010 or ENVX1001 or ENVX1002 or BUSS1020 or DATA1001 or DATA1901 None None HSC Mathematics. Students who have not completed HSC Mathematics (or equivalent) are strongly advised to take the Mathematics Bridging Course (offered in February). Yes

### Teaching staff

Coordinator Munir Hiabu, munir.hiabu@sydney.edu.au Munir Hiabu

## Assessment

Type Description Weight Due Length
Final exam Exam
Multiple Choice and Extended Answer Questions
65% Formal exam period 1.5 hours
Outcomes assessed:
Assignment Projects
See Canvas
25% Multiple weeks 2 x Data Projects
Outcomes assessed:
Assignment Revision Quiz
See Canvas
10% Weekly Weekly Revision Quiz
Outcomes assessed:

### 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.

### 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.

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 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: R & Qualitative data Lecture (2 hr)
Lab 2 Computer laboratory (1 hr)
Week 03 Data & Graphical Summaries: R & Quantitative data Lecture (2 hr)
Lab 3 Computer laboratory (1 hr)
Week 04 Numerical Summaries: Centre + Spread 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 (Project Work) Computer laboratory (1 hr)
Week 07 Chance Variability: Law of averages and sums + The Box Model Lecture (2 hr)
Lab 7 (Project Presentation) Computer laboratory (1 hr)
Week 08 Chance Variability: Normal curve + 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)
Testing: Hypothesis testing & Simulations Lecture (2 hr)
Week 10 Lab 10 Computer laboratory (1 hr)
Week 11 Testing: Z and T Tests Lecture (2 hr)
Lab 11 Computer laboratory (1 hr)
Week 12 Test for a Relationship: 2 Sample T Test Lecture (2 hr)
Lab 12 Computer laboratory (1 hr)
Week 13 Revision Lecture (2 hr)
Lab 13 Computer laboratory (1 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. In that case, students should discuss the problem with the coordinator, and attend another session, if available.

### 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.

Statistics, Freedman, Pisani, and Purves (2007). All students should have access to the text book, which is available in 3 forms:

## 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.

Reduced the number of assignments from 3 to 2. Added additional weekly tutorial quizzes.