STATS 3006  Mathematical Statistics III
North Terrace Campus  Semester 1  2020

General Course Information
Course Details
Course Code STATS 3006 Course Mathematical Statistics III Coordinating Unit School of Mathematical Sciences Term Semester 1 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Available for Study Abroad and Exchange Y Prerequisites (MATHS 1012 and STATS 2107) or (MATHS 2201 and MATHS 2202) or (MATHS 2106 and MATHS 2107) Assumed Knowledge STATS 2107 Course Description Statistical methods used in practice are based on a foundation of statistical theory. One branch of this theory uses the tools of probability to establish important distributional results that are used throughout statistics. Another major branch of statistical theory is statistical inference. It deals with issues such as how do we define a "good" estimator or hypothesis test, how do we recognise one and how do we construct one? This course is concerned with the fundamental theory of random variables and statistical inference.
Topics covered are: calculus of distributions, moments, moment generating functions; multivariate distributions, marginal and conditional distributions, conditional expectation and variance operators, change of variable, multivariate normal distribution, exact distributions arising in statistics; weak convergence, convergence in distribution, weak law of large numbers, central limit theorem; statistical inference, likelihood, score and information; estimation, minimum variance unbiased estimation, the CramerRao lower bound, exponential families, sufficient statistics, the RaoBlackwell theorem, efficiency, consistency, maximum likelihood estimators, large sample properties; tests of hypotheses, most powerful tests, the NeymanPearson lemma, likelihood ratio, score and Wald tests, large sample properties.Course Staff
Course Coordinator: Dr Sharon Lee
Course Timetable
The full timetable of all activities for this course can be accessed from Course Planner.

Learning Outcomes
Course Learning Outcomes
On successful completion of this course students will be able to:
1. demonstrate knowledge of, and properties of, statistical models in common use,
2. understand the basic principles underlying statistical inference (estimation and hypothesis testing),
3. be able to construct tests and estimators, and derive their properties,
4. demonstrate knowledge of applicable large sample theory of estimators and tests.
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 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
1,2,3 Career and leadership readiness
 technology savvy
 professional and, where relevant, fully accredited
 forward thinking and well informed
 tested and validated by work based experiences
1,2,3 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
1,2 
Learning Resources
Required Resources
A set of lecture notes will be provided.Recommended Resources
Recommended textbooks:
Mathematical Statistics with Applications (7th ed.), by D.D. Wackerly, W. Mendenhall, and R.L. Scheaffer, Duxbury Press.
Mathematical Statistics and Data Analysis (3rd ed.), by J.A. Rice, Duxbury Press.
Useful textbooks:
Statistical Inference (2nd ed.), by G. Casella and R. L. Berger, Duxbury Press.
Modern Mathematical Statistics with Applications (2nd ed.), by J.L. Devore and K.N. Berk, Springer.
Online Learning
This course uses MyUni exclusively for providing electronic resources: lecture notes, assignments, solutions, etc. Students are advised to make extensive use of these resources. 
Learning & Teaching Activities
Learning & Teaching Modes
This course relies on lectures as the primary delivery mechanism for the material. Tutorials supplement the lectures by providing exercises and example problems to enhance the understanding obtained through lectures. A sequence of written assignments provides assessment 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 Lectures 30 90 Tutorials 6 18 Assignments 5 48 Total 156 Learning Activities Summary
Lecture outline
13: Review of probability, random variables, density and mass functions, expectation, mean, variance
46: Standard probability distributions (statistical models) and their properties
67: Exponential families of distributions; distribution and expectation of a function of a random variable
811: Joint distributions, covariance, correlation, independence of random variables, distributions of functions of jointly distributed random variables, conditional distributions, conditional means and variances
1214: Sums of independent random variables, transformations of two or more jointly distributed random variables
1415: Random vectors, the multivariate normal distribution and properties
1619: Modes of convergence, laws of large numbers, central limit theorem, Jensen's inequality
2022: Random samples, the chisquare, t, and F distributions and their roles in normal sampling, basic concepts of statistical inference, the likelihood principle, sufficient statistics
2325: Basic concepts of estimation; method of moments, maximum likelhood, large sample properties (consistency, asymptotic normality), mean square eror, RaoBlackwell theorem
2627: Fisher information, the CramerRao inequality, confidence intervals and properties
2830: Hypothesis testing, types of errors, pvalue, power, NeymanPearson lemma, uniformly most powerful tests, likelihood ratio tests, Wald tests, score tests
Tutorial outline: Tutorial material will be integrated into the lecture and assignment material

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
Component Weighting Objective Assessment Assignments 30% all Exam 70% all Assessment Related Requirements
An aggregate score of at least 50% is required to pass the course.Assessment Detail
Five equally weighted (6% each) assigments, due on Friday by 4 pm at the end of weeks 3, 5, 7, 9, 12. The assignments will be distributed on Monday of weeks 2, 4, 6, 8, 10.Submission
1. All written assignments are to be submitted to the designated handin box in the School of Mathematical Sciences with a signed cover sheet attached.
2. Late assignments will not be accepted.
3. Assignments will have a twoweek turnaround time for feedback to students.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.

Student Support
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 Students with a Disability  Alternative academic arrangements
 Reasonable Adjustments to Teaching & Assessment for Students with a Disability Policy

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