# Course Title: Basic Statistical Methodologies

## Part A: Course Overview

Course Title: Basic Statistical Methodologies

Credit Points: 12.00

Important Information:

Please note that this course may have compulsory in-person attendance requirements for some teaching activities.

To participate in any RMIT course in-person activities or assessment, you will need to comply with RMIT vaccination requirements which are applicable during the duration of the course. This RMIT requirement includes being vaccinated against COVID-19 or holding a valid medical exemption.

Please read this RMIT Enrolment Procedure as it has important information regarding COVID vaccination and your study at RMIT.

Please read the Student website for additional requirements of in-person attendance: Coming to campus - COVID protocols for students.

Please check your Canvas course shell closer to when the course starts to see if this course requires mandatory in-person attendance. The delivery method of the course might have to change quickly in response to changes in the local state/national directive regarding in-person course attendance.

## Terms

### Teaching Period(s)

MATH2201

City Campus

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

171H School of Science

Face-to-Face

Sem 2 2017,
Sem 2 2018,
Sem 2 2019,
Sem 2 2020,
Sem 2 2021

Course Coordinator: Dr Sevvandi Kandanaarachchi

Course Coordinator Phone: -

Course Coordinator Email: sevvandi.kandanaarachchi@rmit.edu.au

Course Coordinator Location: 15.04.13

Course Coordinator Availability: by appointment, by email

Pre-requisite Courses and Assumed Knowledge and Capabilities

MATH2200 Introduction to Probability and Statistics 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. 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; 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 Program Learning Outcomes in various applied science programs. In particular it promotes knowledge, skills and their application in the following domains:

Knowledge and technical competence:

• use the appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.

Problem-solving:

• synthesise and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
• balance the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.

Course Learning Outcomes (CLOs)

On completion of this course you should be able to:

1. Perform basic statistical inference tasks involving various forms of hypothesis test for one and two samples using statistical software.
2. Perform one way analysis of variance and explain the assumptions involved in the technique.
3. Apply and justify basic concepts and methods of correlation and simple linear regression and use computer software for relevant calculations.
4. Perform a chi-squared test for association and accuracy of fit.
5. Analyse statistical problems under conditions of uncertainty and devise appropriate responses.

Overview of Learning Activities

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

• Attendance at lectorials and labs where the material will be presented and explained with examples;
• Completion of practice questions and lab sheets to provide further practice in the application of the theory and procedures and to provide feedback on your progress;
• Completion of individual and group assignments requiring an integrated understanding of the subject material;

Private study to consolidate the material presented in class and gain proficiency in solving conceptual and numerical problems.

Overview of Learning Resources

You can gain access to course information and learning material online. Recorded video lectures, class notes and reference material 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: Introductory Practical Timed Assessments
Weight 30%
This assessment task supports CLOs 1, 2, 3, 4, & 5

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