Course Title: Statistical Methodologies

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

Course Coordinator: Professor Irene Hudson

Course Coordinator Phone: +61 3 9925

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


Pre-requisite Courses and Assumed Knowledge and Capabilities

MATH2200 Introduction to Probability and Statistics or ONPS2700 Data for Scientific World or its equivalent would be an advantage.


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

This course contributes to the following Program Learning Outcomes for BP083 Bachelor of Applied Mathematics and Statistics:


PLO1: Apply a broad and coherent knowledge of mathematical and statistical theories, principles, concepts and practices with multi-disciplinary collaboration.  
PLO2: Analyse and critically examine the validity of mathematical and statistical arguments and evidence using methods, technical skills, tools and computational technologies. 
PLO3: Formulate and model real world problems using principles of mathematical and statistical inquiry to inform evidence-based decision making.  
PLO4: Critically evaluate and communicate technical and non-technical mathematical and statistical knowledge to diverse audiences utilising a variety of formats employing culturally safe practices.  
PLO5: Work ethically and independently, with integrity and accountability to develop professional agility for future careers.  


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

Assessment Task 2: Timed Knowledge Quizzes
Weight 30%
This assessment task supports CLOs 1, 2, 3, & 4

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