Part A: Course Overview

Course Title: Statistical Methodologies

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2201

City Campus

Undergraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

 Sem 2 2010,
Sem 2 2011,
Sem 2 2012,
Sem 2 2013,
Sem 2 2014,
Sem 2 2015,
Sem 2 2016

MATH2201

City Campus

Undergraduate

171H School of Science

Face-to-Face

 Sem 2 2017,
Sem 2 2018,
Sem 2 2019,
Sem 2 2020,
Sem 2 2021,
Sem 2 2022,
Sem 2 2023,
Sem 2 2024,
Sem 2 2025

Course Coordinator: Dr Alice Johnstone

Course Coordinator Phone: -

Course Coordinator Email: alice.johnstone@rmit.edu.au

Course Coordinator Availability: Email for appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

Recommended Prior Study

You should have satisfactorily completed or received credit for the 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 extends the probability and statistics material covered in MATH 2200 Introduction to Probability and Statistics and ONPS2700 Data for Scientific World. In the laboratory sessions, extensive use will be made of appropriate computer software for problem solving. Topics areas include: confidence intervals and hypothesis testing for proportions and the mean; review of sampling distributions; central limit theorem; two sample hypothesis test (z and t-tests); inference and confidence intervals for the difference between two populations’ means and proportions; one way analysis of variance; goodness of fit test; simple linear regression and inference for regression; and basic non-parametric hypothesis tests and confidence intervals.

The course aims to provide the theoretical foundations of statistical analysis. It will focus on developing your abilities in critical analysis and decision making. The course is an introductory level course.


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.
PLO 6 Collaborate and contribute within diverse, multi-disciplinary teams, with commitment to diversity, equity and globally inclusive perspectives and practices including First Nations knowledges and input.

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 6 Collaborate and contribute within diverse, multi-disciplinary teams, with commitment to diversity, equity and globally inclusive perspectives and practices including First Nations knowledges.

BH101AMS - Bachelor of Science (Dean's Scholar, Applied Mathematics and Statistics) (Honours)
BH119 - Bachelor of Analytics (Honours)
BP083P10 - Bachelor of Science (Mathematics)
BP083P20 - Bachelor of Science (Applied Mathematics and Statistics)
BP245 - Bachelor of Science (Statistics)

PLO 2 Knowledge and Technical Competence
PLO 3 Problem Solving

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


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

  1. Identify and apply the appropriate statistical methods to analyse discrete and continuous data.
  2. Demonstrate statistical analysis of data using a range of techniques, including one and two-sample tests, non-parametric tests, one-way analysis of variance, and simple linear regression.
  3. Apply basic statistical inference tasks using statistical software.
  4. Clarify the fundamental concepts and assumptions involving different types of hypothesis tests.
  5. Present findings of and interpret statistical analysis results to develop suitable solutions for statistical problems.


Overview of Learning Activities

Learning activities will be presented in a variety of modes. They include:

  • Lectorials: Students will attend interactive sessions where the material will be presented and explained using illustrative examples;
  • Computer labs: Students will actively engage with the subject matter by using the statistical tool to apply the statistical theories and procedures they have learned;
  • Assessments: Students will complete a range of assessments, which will be available online as well as in face-to-face settings, facilitating a thorough evaluation of their knowledge.

Supplementary study is essential to reinforce the concepts covered in class and attain proficiency in solving both conceptual and numerical problems.


Overview of Learning Resources

You can gain access to course information and learning material online. Pre-recorded video lectures, lab exercises, class notes, reading material and post-lecture practice will also be available online while access to computer labs and relevant software will be provided. A Library Guide is available at: http://rmit.libguides.com/mathstats


Overview of Assessment

Practical Assessments

Assessment Task 1: Practical Assessments
Weight 30%
This assessment task supports CLOs 1, 2, 3, 4, & 5

Assessment Task 2: Project
Weight 30%
This assessment task supports CLOs 1, 2, 3, 4 & 5

Assessment Task 3: In-Class Assessment
Weight 40%
This assessment supports CLOs 1, 2, 3, 4, & 5

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.