STATS 4106 - Mathematical Statistics - Honours
North Terrace Campus - Semester 1 - 2022
General Course Information
Course Code STATS 4106 Course Mathematical Statistics - Honours 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 Prerequisites (MATHS 1012 and STATS 2107) or (MATHS 2201 or MATHS 2106 and MATHS 2202 or MATHS 2107) Assumed Knowledge STATS 2107 Restrictions Honours students only 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 Cramer-Rao lower bound, exponential families, sufficient statistics, the Rao-Blackwell theorem, efficiency, consistency, maximum likelihood estimators, large sample properties; tests of hypotheses, most powerful tests, the Neyman-Pearson lemma, likelihood ratio, score and Wald tests, large sample properties.
Course Coordinator: Dr Melissa Humphries
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesOn 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)
Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
Attribute 2: Creative and critical thinking, and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.
Attribute 4: Professionalism and leadership readiness
Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.
Attribute 8: Self-awareness and emotional intelligence
Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.
Required ResourcesA set of lecture notes will be provided.
Recommended ResourcesRecommended 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.
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 LearningMyUni will be used for distributing lecture notes and assignments, as well as communicating with students.
Learning & Teaching Activities
Learning & Teaching ModesThis 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.
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 5 18
Assessments 14 48
Learning Activities SummaryLecture outline
1-3: Review of probability, random variables, density and mass functions, expectation, mean, variance
4-6: Standard probability distributions (statistical models) and their properties
6-7: Exponential families of distributions; distribution and expectation of a function of a random variable
8-11: Joint distributions, covariance, correlation, independence of random variables, distributions of functions of jointly distributed random variables, conditional distributions, conditional means and variances
12-14: Sums of independent random variables, transformations of two or more jointly distributed random variables
14-15: Random vectors, the multivariate normal distribution and properties
16-19: Modes of convergence, laws of large numbers, central limit theorem, Jensen's inequality
20-22: Random samples, the chi-square, t, and F distributions and their roles in normal sampling, basic concepts of statistical inference, the likelihood principle, sufficient statistics
23-25: Basic concepts of estimation; method of moments, maximum likelhood, large sample properties (consistency, asymptotic normality), mean square eror, Rao-Blackwell theorem
26-27: Fisher information, the Cramer-Rao inequality, confidence intervals and properties
28-30: Hypothesis testing, types of errors, p-value, power, Neyman-Pearson 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
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.
Component Weighting Objectives Assignments 30% all Quizzes 10% all Mid-semester Test 30% all End-of-semester Test 30% all
There are three assignments in this course (each contribute 10% of final grade).
There are also 10 quizzes througout the course (1% each).
There will be a mid-semester test (30%) and an end of semester test (30% of final grade), times and dates TBA.
Assessment Related RequirementsAn aggregate score of at least 50% is required to pass the course.
Assessment DetailFive equally weighted (10% each) assigments, due at the end of weeks 3, 5, 7, 9, 12. The assignments will be distributed on Monday of weeks 2, 4, 6, 8, 11.
Submission1. All written assignments are to be submitted online via MyUni.
2. Late assignments will not be accepted unless an extension has been arranged prior to the due date.
Grades for your performance in this course will be awarded in accordance with the following scheme:
M11 (Honours Mark Scheme) Grade Grade reflects following criteria for allocation of grade Reported on Official Transcript Fail A mark between 1-49 F Third Class A mark between 50-59 3 Second Class Div B A mark between 60-69 2B Second Class Div A A mark between 70-79 2A First Class A mark between 80-100 1 Result Pending An interim result RP Continuing Continuing CN
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.
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 on-going 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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