Unit convenor and teaching staff 
Unit convenor and teaching staff
Lecturer in charge
Jun Ma
Contact via 98508548
Room 526, 12 Wally's Walk (E7A)
Lecturer
Hassan Doosti
Contact via 98504884
Room 534, 12 Wally's Walk (E7A)


Credit points 
Credit points
3

Prerequisites 
Prerequisites
6cp at 200 level including (STAT272 or STAT273 or STAT278)

Corequisites 
Corequisites

Cobadged status 
Cobadged status

Unit description 
Unit description
This unit develops basic computerintensive statistical methods. Many of them find applications in scientific research and industry. Topics include: MonteCarlo simulation; bootstrapping; regression computations which include collinearity diagnostic and models selection using crossvalidation; alternatives to least squares; ridge regression, weighted least squares and logistic regression; maximum likelihood computations using iterative methods, such as NewtonRaphson and Fisher scoring; and applications of the maximum likelihood method.

Information about important academic dates including deadlines for withdrawing from units are available at http://students.mq.edu.au/student_admin/enrolmentguide/academicdates/
Name  Weighting  Hurdle  Due 

Assignment 1  13%  No  Week 6 Tue (5/9/2017) 
Assignment 2  13%  No  Week 12 Tue (31/10/2017) 
Group presentation  14%  No  Week 13 lecture 
Final exam  60%  No  University Exam Period 
Due: Week 6 Tue (5/9/2017)
Weighting: 13%
Assignment 1 will be available on the unit webpage in week 3 and due in week 6. Assignments may be handwritten or wordprocessed, and submitted in person to Dr Hassan Doosti during the week 6 lecture on Tuesday. Late submissions without approval will be penalized at a rate of 5% of earned mark per day, up to maximum of 50%. This implies that submissions will not be accepted more than 10 days late. Assignments must be each student’s own work.
This Assessment Task relates to the following Learning Outcomes:
Due: Week 12 Tue (31/10/2017)
Weighting: 13%
Assignment 2 will be available on the unit webpage in week 9 and due in the week 12 lecture. Assignments may be handwritten or wordprocessed, and submitted in person to A/Prof Jun Ma during the 12 lecture. For policy on later submission and other issues please see the Assignment 1 description.
This Assessment Task relates to the following Learning Outcomes:
Due: Week 13 lecture
Weighting: 14%
There will be group presentations during the week 13 lecture. Details on topics and groups, as well as marking criteria, will be announced later. Basically, each group will be given a paper to read (for 3 weeks) and present it to other students and lecturers. Other groups and lecturers will mark the presenting group, and an weighted average mark will be the final mark for this group students.
Due: University Exam Period
Weighting: 60%
This is a written exam and it is to be scheduled in the university exam period. This examination involves conceptual questions, simple calculation questions and simple programming questions. For example, it may ask students to write a bootstrapping program to compute bootstrap confidence intervals. For this exam, students are allowed to bring into the exam room TWO A4 paper notes written/typed on both sides; photocopies are not allowed. Only nonprogrammable calculators that do not have text retrieval capacity are allowed. Note that students who apply for Supplementary Exams must make themselves available over the supplementary exam period (11/12/17 15/12/17).
This Assessment Task relates to the following Learning Outcomes:
Classes
You are required to attend a 3hour lecture each week at Tuesday 10 – 1pm in E4B 208 (FBE Computer Lab)
You are also required to attend an 1hour tutorial each week at Tuesday 4  5pm in E4B 214 (FBE Computer Lab).
Prescribed texts
There is no prescribed textbook for this unit. Students should obtain lecture overheads from iLearn prior to the lecture. The following are recommended reading books for this unit
Wendy L. Martinez, Angel R. Martinez, "Computational Statistics Handbook with MATLAB", Third Edition (2015 by Chapman and Hall/CRC).
Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, "An Introduction to Statistical Learning with Applications in R", (2013 by Springer).
Robert I. Jennrich, "An Introduction to Computational Statistics – regression analysis", (QA278.2.J46 1995).
J.H. Maindonald, "Statistical Computation", (QA276.4.M25)
James E. Gentle, "Elements of Computational Statistics", (QA276.4.G455)
Kleijnen, Jack. Simulation: a statistical perspective. (QA76.9.C65.K5913)
Draper and Smith. Applied Regression Analysis. 2nd edition. (QA278.2.D7)
Computing packages used
We primarily use the software package MATLAB in this Unit. MATLAB is becoming increasingly important for training students in scientific computations. More information about MATLAB can be found at the web site "https://au.mathworks.com/products/matlabonline.html" and students are entitled to one year free license.
We use Winbugs when teaching Bayesian methods.
Unit webpage
Unit webpage is located on Moodle at https://ilearn.mq.edu.au. You can access the material on Moodle only if you are enrolled in the unit. All lecturing materials are available at this webpage.
Teaching and Learning Strategy
The unit is taught in traditional mode:
Examination
If you notify the University of your disruption to studies for your final examination, you must make yourself available for the supplementary examination. If you are not available at that time, there is no guarantee an additional examination time will be offered.
Software
We are using MATLAB (or R) and WinBUGS in teaching this unit. R and WinBUGS are free software and are widely used nowadays by statisticians. More information about R can be found at http://www.rproject.org/, and WinBUGS at “http://www.mrcbsu.cam.ac.uk/bugs/”.
Week 
Topic 
1 
Introduction and Review 1. Introduction to statistical computing. 2. Some common probability distributions and their random number generation. 3. Quick introduction to MATLAB. 
2 
Regression Computation: Linear regression 1. Linear regression model. 2. Leastsquares criterion and normal equations. 3. Matrices expression of linear regression. 
3 
Regression Computation: Linear regression 1. Solving linear system of equations. 2. Properties of the estimator. 3. Residual analysis. 4. Regression Diagnostics. 
4 
Regression Computation: Linear regression 1. Transformation and variance stabilizing. 2. BoxCox transformation 3. Weighted regression. 
5 
Regression Computation: Linear regression 1. Ridge regression. 2. Computer assisted model building: (1) Stepwise. (2) Best subset. (3) Crossvalidation. (4) Cp and PRESS. 
6 
Regression Computation: Linear regression 1. Model selection using Lasso. 2. Examples. 
7&8 
Monte Carlo Simulation 1. Introduction to Monte Carlo simulations. 2. Examples Bootstrapping 1. Introduction to Bootstrapping method. 2. Examples. 3. Further discussions. 
8&9 
Nonlinear regression and neural networks 1. Nonlinear regression. 2. Least squares estimation. 3. GaussNewton algorithm. 4. Neural network modelling 
10&11 
Maximum Likelihood (ML) Estimation 1. Introduction to maximum likelihood estimation. 2. An example in medical imaging. 3. Algorithms for ML computing. 
11&12 
Bayesian methods 1. Introduction. 2. Prior and posterior distributions. 3. Generate random number from the posterior distributions. 4. Bayesian computations using WinBugs. 
13 
Students group presentation 
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Assessment Policy http://mq.edu.au/policy/docs/assessment/policy_2016.html
Grade Appeal Policy http://mq.edu.au/policy/docs/gradeappeal/policy.html
Complaint Management Procedure for Students and Members of the Public http://www.mq.edu.au/policy/docs/complaint_management/procedure.html
Disruption to Studies Policy (in effect until Dec 4th, 2017): http://www.mq.edu.au/policy/docs/disruption_studies/policy.html
Special Consideration Policy (in effect from Dec 4th, 2017): https://staff.mq.edu.au/work/strategyplanningandgovernance/universitypoliciesandprocedures/policies/specialconsideration
In addition, a number of other policies can be found in the Learning and Teaching Category of Policy Central.
Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/support/student_conduct/
Results shown in iLearn, or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit ask.mq.edu.au.
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