Course Title: Basic Statistical Methodologies

Part A: Course Overview

Course Title: Basic 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

Course Coordinator: Dr Andrew Stacey

Course Coordinator Phone: +61 3 9925 2280

Course Coordinator Email: andrew.stacey@rmit.edu.au

Course Coordinator Location: 8.9.24


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.


 

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. Describe and apply 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 lectures where the material will be presented and explained with examples;
  • Completion of practice classes and laboratory tutorials to provide further practice in the application of the theory and procedures and to provide feedback on your progress;
  • Completion of assignments/tests 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. 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

 

Assessment Tasks:

 

Early Assessment Task: Laboratory Exercise Sheets

Weighting 35%

This assessment task supports CLOs  1, 2, 3, 4 & 5

Assessment Task 2:  Mid-Semester Test

Weighting 15%

This assessment task supports CLOs 1, 2, 3, 4 & 5

 

Assessment 3: Final Exam

Weighting 50% 

This assessment supports CLOs 1, 2, 3, 4 & 5