Course Title: Introduction to Probability and Statistics

Part A: Course Overview

Course Title: Introduction to Probability and Statistics

Credit Points: 12.00

Important Information:

 

 


Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2200

City Campus

Undergraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

 Sem 1 2010,
Sem 1 2011,
Sem 1 2012,
Sem 1 2013,
Sem 1 2014,
Sem 1 2015,
Sem 1 2016

MATH2200

City Campus

Undergraduate

171H School of Science

Face-to-Face

 Sem 1 2017,
Sem 1 2018,
Sem 1 2019,
Sem 1 2020,
Sem 1 2021,
Sem 1 2022,
Sem 1 2023

Course Coordinator: Mahshid Sadeghpour

Course Coordinator Phone: +61 3 9925

Course Coordinator Email: mahshid.sadeghpour@rmit.edu.au

Course Coordinator Availability: By appointment, by email


Pre-requisite Courses and Assumed Knowledge and Capabilities

None


Course Description

This course provides a broad introduction to statistical techniques and data analysis using statistical packages. It is aimed at students who need a basic background in statistics and its application.  Topics areas include: summarising univariate and bi-variate data, fitting the regression line to bivariate data, using computer packages to analyse univariate and bivariate data, discrete and continuous random variables, binomial and normal distributions and generating random data using statistical packages.


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:

Personal and professional awareness

  • The ability to contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions
  • The ability to reflect on experience and improve your own future practice
  • The ability to apply the principles of lifelong learning to any new challenge.

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 will be able to:

  1. Construct appropriate graphical displays of data (stem and leaf plots, boxplots, etc) and explain the role of such displays in data analysis;
  2. Assess the nature of random variables and probability distributions (including binomial, Poisson, normal ) through direct calculation and computer simulation;
  3.  Perform basic statistical inference tasks using software (estimation and confidence intervals) and interpret the results.
  4. Discriminate between univariate and bivariate data and fit a regression line to bivariate data.
  5. Select and use appropriate computer packages to analyse univariate and bivariate data, discrete and continuous random variables, binomial and normal distributions, and generate random data.
  6. Specify the calculations involved in such tasks and be cognisant of assumptions necessary for the validity of results (residual analysis, normality tests).


Overview of Learning Activities

  •  Review recorded lecture material and attend  sessions where syllabus material will be explained, and the subject will be illustrated with demonstrations and examples. 
  • Completion of tutorial questions and laboratory projects designed to give further practice in the application of theory and procedures and to provide feedback on your progress and understanding; 
  • Completion of assessment tasks consisting of numerical and other problems requiring an integrated understanding of the subject matter; 
  • Private study, working through the course as presented in classes and learning materials, and gaining practice at solving conceptual and numerical problems. 
  • Assessment through a mixture of group case-based practical assessments, other practical assessments, and in-class discipline-based assessments.. 

If you are experiencing difficulty in understanding lecture material you may seek help from your lecturer or tutors. 

 


Overview of Learning Resources

You will be able to access course information and learning materials through Canvas. Lists of relevant reference texts, and resources in the library will be provided. The library guide is available online at http://rmit.libguides.com/mathstats. You will also use online computer software within the School during discipline-based and case-based assessments. 


Overview of Assessment

Note that: 

 ☒This course has no hurdle requirements. 

 

Assessment Task 1: Group case-based practical assessment (reports and presentations)  

Weighting 40% 

This assessment supports CLO 1-6 

 

Assessment Task 2:  Individual-based practical assessments  

Weighting 30% 

These assessments support CLO 1-6 

 

Assessment Task 3: Timed assessment after teaching period

Weighting 30%

This assessment supports CLO 1-6