MATHS 2103  Probability & Statistics II
North Terrace Campus  Semester 1  2021

General Course Information
Course Details
Course Code MATHS 2103 Course Probability & Statistics II Coordinating Unit School of Mathematical Sciences Term Semester 1 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 3.5 hours per week Available for Study Abroad and Exchange Y Prerequisites MATHS 1012 or MATHS 1004 Course Description Probability theory is the branch of mathematics that deals with modelling uncertainty. It is important because of its direct application in areas such as genetics, finance and telecommunications. It also forms the fundamental basis for many other areas in the mathematical sciences including statistics, modern optimisation methods and risk modelling. This course provides an introduction to probability theory, random variables and Markov processes.
Topics covered are: probability axioms, conditional probability; Bayes' theorem; discrete random variables, moments, bounding probabilities, probability generating functions, standard discrete distributions; continuous random variables, uniform, normal, Cauchy, exponential, gamma and chisquare distributions, transformations, the Poisson process; bivariate distributions, marginal and conditional distributions, independence, covariance and correlation, linear combinations of two random variables, bivariate normal distribution; sequences of independent random variables, the weak law of large numbers, the central limit theorem; definition and properties of a Markov chain and probability transition matrices; methods for solving equilibrium equations, absorbing Markov chains.Course Staff
Course Coordinator: Dr Andrew Black
Course Timetable
The full timetable of all activities for this course can be accessed from Course Planner.

Learning Outcomes
Course Learning Outcomes
Students who successfully complete this course should be able to demonstrate understanding of :
1 basic probability axioms and rules and the moments of discrete and continous random variables as well as be familiar with common named discrete and continous random variables. 2 how to derive the probability density function of transformations of random variables and use these techniques to generate data from various distributions. 3 how to calculate probabilities, and derive the marginal and conditional distributions of bivariate random variables. 4 discrete time Markov chains and methods of finding the equilibrium probability distributions. 5 how to calculate probabilities of absorption and expected hitting times for discrete time Markov chains with absorbing states. 6 how to translate realworld problems into probability models. 7 how to read and annotate an outline of a proof and be able to write a logical proof of a statement.
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Deep discipline knowledge
 informed and infused by cutting edge research, scaffolded throughout their program of studies
 acquired from personal interaction with research active educators, from year 1
 accredited or validated against national or international standards (for relevant programs)
1,2,3,4,5,6,7 Critical thinking and problem solving
 steeped in research methods and rigor
 based on empirical evidence and the scientific approach to knowledge development
 demonstrated through appropriate and relevant assessment
5,6,7 Teamwork and communication skills
 developed from, with, and via the SGDE
 honed through assessment and practice throughout the program of studies
 encouraged and valued in all aspects of learning
4,5 Career and leadership readiness
 technology savvy
 professional and, where relevant, fully accredited
 forward thinking and well informed
 tested and validated by work based experiences
6 Selfawareness and emotional intelligence
 a capacity for selfreflection and a willingness to engage in selfappraisal
 open to objective and constructive feedback from supervisors and peers
 able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
4,5 
Learning Resources
Required Resources
Access to the internet.Recommended Resources
There are many good books on probability and statistics in the Barr Smith Library, with the following texts being recommended for this course.
1. Probability and Random Processes, by Grimmett and Stirzaker, 3rd edition, Oxford, 2001
2. Mathematical Statistics with Applications, by Wackerly, Mendenhall and Schaeffer, Duxbury, 2008.
3. Introduction to Stochastic Models, by Roe Goodman, 2nd edition, Dover, 2006.
4. Introduction to Probability Models, by Sheldon Ross, Academic Press, 2010.
5. Mathematical Statistics and Data Analysis, by John Rice, Duxbury Press, 2006.
For other texts on probability and statistics, try browsing books with call numbers beginning with 519.2.Online Learning
This course uses MyUni exclusively for providing electronic resources, such as topic videos, lecture notes, assignment papers, and sample solutions. Students should make appropriate use of these resources. 
Learning & Teaching Activities
Learning & Teaching Modes
This course relies on topic videos as the primary delivery mechanism for the material. Tutorials are the primary direct contact hours, during which students will both reinforce and employ the understanding obtained through lectures. Weekly quizzes provide regular opportunities for students to gauge their progress and understanding.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Workload hours Videos and study 88 Tutorials 11 33 Assignments 5 25 Online quizzes 12 12 Total 158 Learning Activities Summary
Topics Schedule Week 1 Discrete random variables Probability mass function, expectation and variance. Bernoulli distribution, Geometric distribution, Binomial distribution. Derivation of mean and variance. Week 2 Discrete random variables Sampling with and without replacement. Hypergeometric distribution and Poisson distribution. Derivation of the Poisson distribution as limiting form of Binomial. Derivation of mean and variance. Week 3 Discrete random variables Bounding probabilities, tail sum formula, Markov’s inequality and Chebyshev’s inequality. Probability generating functions and moment generating functions. Week 4 Continuous random variables Probability density function, cumulative distribution function, expectation, mean and variance. Moment generating functions and uniqueness theorem. Chebyshev’s inequality. Week 5 Continuous random variables The uniform distribution on (a, b), the normal distribution. Mean and variance of the normal distribution. The Cauchy distribution. The exponential distribution, moments, memoryless property, hazard function. Week 6 Continuous random variables Gamma distribution, moments, Chisquare distribution. Point processes, the Poisson process, derivation of the Poisson and exponential distributions. Week 7 Transformation of random variables
and bivariate distributionsCumulative distribution function method for finding the distribution of a function of random variable. The transformation rule. Discrete bivariate distributions, marginal and conditional distributions, the trinomial distribution and multinomial distribution. Week 8 Bivariate distributions Continuous bivariate distributions, marginal and conditional distributions, independence of random variables. Covariance and correlation. Mean and variance of linear combination of two random variables. The joint Moment generating function (MGF) and MGF of the sum. Week 9 Bivariate distributions and
independent random variablesThe bivariate normal distribution, marginal and conditional distributions, conditional expectation and variance, joint MGF and marginal MGF. Linear combinations of independent random variables. Means and variances. Sequences of independent random variables and the weak law of large numbers. The central limit theorem, normal approximation to the binomial distribution. Week 10 Discrete time Markov chains Definition of a Markov chain and probability transition matrices. Equilibrium behaviour of Markov chains: computer demonstration and ergodic, limiting and stationary interpretations. Week 11 Discrete time Markov chains Methods for solving Equilibrium Equations using probability generating functions and partial balance. Week 12 Discrete time Markov chains Definition of absorbing Markov chains, structural results, hitting probabilities and expected hitting times. Review.
Each tutorial, starting in week 2, will cover material from the previous week.Small Group Discovery Experience
NA 
Assessment
The University's policy on Assessment for Coursework Programs is based on the following four principles:
 Assessment must encourage and reinforce learning.
 Assessment must enable robust and fair judgements about student performance.
 Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
 Assessment must maintain academic standards.
Assessment Summary
Assessment task Task type When due Weighting Learning outcomes Examination Summative Examination period 50% All Assignments Formative and summative Weeks 3,5,7,9 and 11 20% All Midsemester test Formative and summative Week 7 20% All Online quizzes Formative and summative Weeks 112 10% All
Assessment Related Requirements
An aggregate score of 50% is required in order to pass this course.Assessment Detail
Assessment task Set Due Weighting Assignment 1 Week 2 Week 3 4% Assignment 2 Week 4 Week 5 4% Assignment 3 Week 6 Week 7 4% Assignment 4 Week 8 Week 9 4% Assignment 5 Week 10 Week 11 4% Submission
Assignments are submited electronically through MyUni. Late assignments will not be accepted. Assignments will be returned within two weeks. Students may be excused from an assignment for medical or compassionate reasons. In such cases, documentation is required and the lecturer must be notified as soon as possible before the fact.Course Grading
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 149 Fail P 5064 Pass C 6574 Credit D 7584 Distinction HD 85100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.

Student Feedback
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including ongoing engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

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