Course Title: Statistical Inference

Part A: Course Overview

Course Title: Statistical Inference

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2155

City Campus

Undergraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

 Sem 1 2006,
Sem 1 2007,
Sem 1 2008,
Sem 1 2009,
Sem 1 2010,
Sem 1 2011,
Sem 1 2012,
Sem 1 2013,
Sem 1 2014,
Sem 1 2015,
Sem 2 2016

MATH2155

City Campus

Undergraduate

171H School of Science

Face-to-Face

 Sem 2 2017,
Sem 2 2018,
Sem 2 2019,
Sem 2 2020,
Sem 1 2022,
Sem 1 2023,
Sem 2 2024,
Sem 2 2025

Course Coordinator: Professor Irene Hudson

Course Coordinator Phone: +61 3 9925 3224

Course Coordinator Email: irene.hudson@rmit.edu.au


Pre-requisite Courses and Assumed Knowledge and Capabilities

Recommended Prior Study

You should have satisfactorily completed following course/s before you commence this course.

If you have completed prior studies at RMIT or another institution that developed the skills and knowledge covered in the above course/s you may be eligible to apply for credit transfer.

Alternatively, if you have prior relevant work experience that developed the skills and knowledge covered in the above course/s you may be eligible for recognition of prior learning.

Please follow the link for further information on how to apply for credit for prior study or experience.


Course Description

This course deals with fundamental concepts and techniques of statistical inference including estimation and tests of simple and composite hypotheses. A brief revision will also be given of some basic topics in probability theory as well as single and multiple random variables. The impact that statistics has made and will continue to make in virtually all fields of scientific and other human endeavours is considered.

During this course you will develop a deeper understanding of the basis underlying modern statistical inference and equip yourself with a statistical tool kit which will enable you to apply your knowledge and skills to real world tasks.


Objectives/Learning Outcomes/Capability Development

Program Learning Outcomes

This course contributes to the program learning outcomes for the following program(s):

BP350 - Bachelor of Science (Statistics Major)

PLO 1 Apply a broad and coherent knowledge of scientific theories, principles, concepts and practice in one or more scientific disciplines.
PLO 2 Analyse and critically examine scientific evidence using methods, technical skills, tools and emerging technologies in a range of scientific activities.
PLO 3 Analyse and apply principles of scientific inquiry and critical evaluation to address real-world scientific challenges and inform evidence based decision making.

BP083P23 - Bachelor of Applied Mathematics and Statistics (Statistics Major)

PLO 1 Apply a broad and coherent knowledge of mathematical and statistical theories, principles, concepts and practices with multi-disciplinary collaboration.
PLO 2 Analyse and critically examine the validity of mathematical and statistical arguments and evidence using methods, technical skills, tools and computational technologies.
PLO 3 Formulate and model real world problems using principles of mathematical and statistical inquiry to inform evidence-based decision making.
PLO 5 Work ethically and independently, with integrity and accountability to develop professional agility for future careers.

BP083P20 - Bachelor of Science (Applied Mathematics and Statistics)

PLO 1 Personal and Professional Awareness
PLO 2 Knowledge and Technical Competence
PLO 6 Information Literacy

BP245 - Bachelor of Science (Statistics)
BH101AMS - Bachelor of Science (Dean's Scholar, Applied Mathematics and Statistics) (Honours)

PLO1 Personal and Professional Awareness
PLO2 Knowledge and Technical Competence
PLO3 Problem Solving
PLO6 Information Literacy

For more information on the program learning outcomes for your program, please see the program guide.


Upon successful completion of the course, you will be able to:

  1. Apply various discrete and continuous univariate and multivariate probability distributions in modelling statistical processes.
  2. Elucidate the concepts of sampling distribution and how to apply them.
  3. Estimate unknown parameters of a given probability distribution using standard and non-standard estimation techniques.
  4. Conceptually map the theoretical basis of tests of simple and composite hypotheses.


Overview of Learning Activities

Key concepts in estimation and hypothesis testing will be explained with relevant examples in lectures, tutorials and online notes. The assignments and tutorials will also test and consolidate your understanding of the topics covered in lectures. You will also have the opportunity to discuss your progress with teaching staff.

You will be actively engaged in a range of learning activities such as lectorials, tutorials, practicals, laboratories, seminars, project work, class discussion, individual and group activities. Delivery may be face to face, online or a mix of both.

You are encouraged to be proactive and self-directed in your learning, asking questions of your lecturer and/or peers and seeking out information as required, especially from the numerous sources available through the RMIT library, and through links and material specific to this course that is available through myRMIT Studies Course.


Overview of Learning Resources

RMIT will provide you with resources and tools for learning in this course through myRMIT Studies Course.

Weekly learning resources are set up and available in Canvas.

A list of recommended learning resources will be provided by your lecturer, which may include books, journal articles and web resources. You will also be expected to seek further resources relevant to the focus of your own learning.

There are services available to support your learning through the University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services. For further information, please visit the Library page on the RMIT University website and the myRMIT student portal.


Overview of Assessment

Assessment Tasks:

Assessment Task 1: Real-world application problems
Weighting 30%
This assessment task supports CLOs 1, 2, 3, 4.

Assessment Task 2: Applications of probability theory
Weighting 35%
This assessment task supports CLOs 1, 2, 3, 4.

Assessment Task 3: In-class study-based authentic assessment (final test)
Weighting 35%
This assessment task supports CLOs 1, 2, 3, 4.

If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.