Program Overview
Overview
Mathematics and Statistics provide powerful quantitative tools to solve problems and make informed decisions in a very diverse range of real-life applications. This unit builds on the calculus that you learnt in Mathematics 1A and introduces you to mathematical statistics. Mathematics 1B gives you a foundational knowledge of the theory of multivariable calculus, differential equations and mathematical statistics that will underpin examples of applications in this unit and in other areas that you will study.
Unit Details and Rules
- Academic unit: Mathematics and Statistics Academic Operations
- Credit points: 6
- Prerequisites: None
- Corequisites: None
- Prohibitions: MATH1905 or MATH1903 or MATH1907 or MATH1923 or MATH1933 or MATH1972 or MATH1962 or MATH1003 or MATH1023 or MATH1005 or MATH1015
- Assumed knowledge: Knowledge of complex numbers and methods of differential and integral calculus including integration by partial fractions and integration by parts as for example in MATH1021 or MATH1921 or MATH1931 or MATH1061 or HSC Mathematics Extension 2
- Available to study abroad and exchange students: No
Teaching Staff
- Coordinator: Brad Roberts
- Lecturer(s): Fernando Viera, Sean Skinner
Assessment
The census date for this unit availability is 24 January 2025
- Type: Supervised exam
- Description: Final exam, Multiple choice and written calculations and explanations
- Weight: 60%
- Due: February exam weeks
- Length: 2 hours
- Outcomes assessed: LO1 LO2 LO3 LO4 LO6 LO7 LO8
- Type: Small test
- Description: Webwork and R quizzes 3-10, Weekly online quizzes
- Weight: 6%
- Due: Multiple weeks
- Length: 60 minutes per quiz
- Outcomes assessed: LO1 LO3 LO4 LO6 LO7 LO8
- Type: Participation
- Description: Tutorials and labs, Participation in tutorials and labs
- Weight: 2%
- Due: Progressive
- Length: 2x50 minutes per teaching day
- Outcomes assessed: LO1 LO2 LO9
- Type: Small test
- Description: Webwork and R quizzes 1-2, #earlyfeedbacktask
- Weight: 2%
- Due: Week 01, Due date: 19 Jan 2025 at 23:59, Closing date: 19 Jan 2025
- Length: 60 minutes per quiz
- Outcomes assessed: LO1
- Type: Short release assignment
- Description: Assignment 1, written calculations, computational data analysis
- Weight: 5%
- Due: Week 02, Due date: 23 Jan 2025 at 23:59, Closing date: 30 Jan 2025
- Length: 2-4 pages and an R markdown report
- Outcomes assessed: LO1 LO2 LO3 LO5 LO8 LO9
- Type: Short release assignment
- Description: Assignment 2, written mathematical problem solving, computational data analysis
- Weight: 10%
- Due: Week 04, Due date: 04 Feb 2025 at 23:59, Closing date: 11 Feb 2025
- Length: 6-8 pages and an R markdown report
- Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO9
- Type: Tutorial quiz
- Description: Quiz, Multiple choice or written answers
- Weight: 15%
- Due: Week 05, Due date: 12 Feb 2025 at 13:00, Closing date: 12 Feb 2025
- Length: 40 minutes
- Outcomes assessed: LO1 LO3 LO4 LO5 LO6 LO7 LO8
Early Feedback Task
This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the Canvas site and your result will be recorded in your Marks page.
Assessment Summary
- Assignments: There are two short release assignments. Each must be submitted electronically, as one single typeset or scanned PDF file only, via Canvas by the deadline.
- Quiz: One quiz will be held on campus. The quiz is 40 minutes and has to be submitted during your timetabled computer lab on the due date.
- Webwork Quizzes and R Quizzes: There are ten weekly online quizzes (through Canvas and equally weighted) and the marks for the best eight count.
- Tutorial/Lab Participation: This is a satisfactory
on-satisfactory mark assessing whether or not you participate in class activities during the tutorials and labs starting on the second teaching day. - Final Exam: 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.
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 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.
Academic Integrity
The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.
Weekly Schedule
WK | Topic | Learning activity | Learning outcomes ---|---|---|--- Week 01 | Introduction to mathematical modelling and differential equations; graphical summaries | Lecture (3 hr) | LO1 LO2 LO3 LO5 LO8 LO9 Week 02 | Linear differential equations; linear models | Lecture and tutorial (5 hr) | LO1 LO2 LO4 LO8 LO9 Week 03 | Curves and surfaces in 3D; understanding chance and variability | Lecture and tutorial (5 hr) | LO1 LO2 LO3 LO4 LO5 LO6 Week 04 | Directional derivatives, chain rule; test for a proportion | Lecture and tutorial (5 hr) | LO1 LO2 LO4 LO5 LO6 LO7 LO9 Week 05 | Optimisation of functions of two variables; chi-sqared tests and p-values | Lecture and tutorial (5 hr) | LO1 LO2 LO4 LO5 LO7 LO9
Learning Outcomes
At the completion of this unit, you should be able to:
- LO1: apply mathematical logic and statistical thinking to solve problems
- LO2: express mathematical and statistical ideas and arguments coherently in written form
- LO3: identify appropriate methods to describe, summarise and visualise a given data set
- LO4: identify and apply appropriate methods of inference for a variety of data types
- LO5: apply statistical software such as R to analyse example sets of data
- LO6: express surfaces and curves in three dimensions as equations in Cartesian coordinates and interpret functions of two variables as surfaces in three-dimensional Cartesian space
- LO7: calculate partial derivatives of functions of several variables and use these to find directional derivatives and gradient vectors and to interpret the physical and geometric significance of these quantities
- LO8: create differential equations models and use a variety of techniques to solve these differential equations and interpret their solutions in terms of the original problem
- LO9: apply concepts of mathematical statistics and calculus to a variety of contexts and applications
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.
- 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.
- 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.
No changes have been made since this unit was last offered.
Additional Information
- Lectures: Lectures are face-to-face and streamed live with online access from Canvas.
- Tutorials: Tutorials are small classes in which you are expected to work through questions from the tutorial sheet in small groups on the white board.
- Labs: Labs are small classes in which you are expected to work through questions from the tutorial sheet.
- Tutorial and exercise sheets: The question sheets for a given week will be available on the MATH1062 Canvas page.
- Ed Discussion forum:
- Science student portal
- Mathematics and Statistics student portal
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 19 Dec 2024.