STATS 125 : Probability and its Applications

Science

2020 Semester One (1203) (15 POINTS)

Course Prescription

Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including: finance and economics; biology; telecommunications, networks; games, gambling and risk.

Course Overview

The course concentrates on probability models and their applications in a variety of fields. Probability models are used in disciplines as varied as commerce and biology (e.g. calculating the probability that a share price will exceed a certain level or the probability that a population will become extinct). Probability underpins both statistics and (stochastic) operations research. The course will cover probability, conditional probability, Bayes' theorem, discrete distributions, expectation and variance, joint and conditional discrete distributions, definition and examples of Markov chains, random walks, hitting probabilities and times, equilibrium distributions. Illustrations will be drawn from a wide variety of applications including finance and economics, genetics, bioinformatics and other areas of biology, telecommunication networks, games, gambling and risk, and forensic science.

Course Requirements

Corequisite: MATHS 108 or 110 or 120 or 130 Restriction: STATS 210

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Graduate Profile: Bachelor of Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Demonstrate the use of probability rules, discrete distributions, joint distributions and Markov chains to solve problems (Capability 1 and 3)
  2. Translate information given in words into correct probability statements (Capability 1 and 3)
  3. Select an appropriate discrete probability model to solve a problem (Capability 1 and 3)
  4. Produce and clearly explain mathematical calculations and reasoning (Capability 1, 3 and 4)
  5. Interpret their solution to a probability problem with reference to the context of the problem (Capability 1 and 4)
  6. Recognise and specify the limitations of a simple probability model in a given context (Capability 1 and 2)
  7. Differentiate between reasonable and unreasonable solutions to probability problems (Capability 1, 2 and 3)

Assessments

Assessment Type Percentage Classification
Assignments 18% Individual Coursework
Tutorials 4% Individual Coursework
Quizzes 5% Individual Coursework
Discussions 3% Group Coursework
Test 20% Individual Test
Final Exam 50% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6 7
Assignments
Tutorials
Quizzes
Discussions
Test
Final Exam

Minimum of 45% in the final exam required to pass.

Plussage applies to test and exam:  the best mark of test 20%, exam 50% or test 10%, exam 60% 

Tuākana

Statistics Tuākana tutors are available to help with this course.

Key Topics

The course covers four broad areas (roughly one quarter each):  Probability, Discrete distributions, Joint and conditional distributions, and Markov chains.

Learning Resources

The course book contains all the notes and most examples covered in class plus additional practice exercises.  The Course book is available free of charge from the Science Student's resource centre.  
It is assumed that students have access to a scientific (or graphics) calculator.

Special Requirements

Note that attendance at tutorials is required for the tutorial and discussion components of course work.

Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, you can expect 3 hours of lectures, a 1-hour tutorial, 3 hours of reading and thinking about the content and 3 hours of work on assignments, quizzes and/or test preparation.

Digital Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

A digital copy of the coursebook (as printed with gaps to complete in lectures)  and a filled-in version are both available on Canvas. 

Copyright

The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website at http://disability.auckland.ac.nz

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page: https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter (https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html).

Disclaimer

Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

Published on 11/01/2020 03:23 p.m.