Course Title: Introduction to Statistics

Part A: Course Overview

Course Title: Introduction to Statistics

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


145H Mathematical & Geospatial Sciences


Sem 1 2007,
Sem 1 2008,
Sem 1 2009,
Sem 1 2010,
Sem 1 2011,
Sem 1 2012,
Sem 1 2013,
Sem 1 2014,
Sem 2 2014,
Sem 1 2015,
Sem 2 2015,
Sem 1 2016,
Sem 2 2016


City Campus


171H School of Science


Sem 1 2017,
Sem 2 2017

Course Coordinator: Dr Anil Dolgun

Course Coordinator Phone: +61 3 9925 2526

Course Coordinator Email:

Course Coordinator Location: Building 8, Level 9, Room 23

Course Coordinator Availability: By appointment

Pre-requisite Courses and Assumed Knowledge and Capabilities

A working knowledge of basic mathematics and familiarity with computers.

Course Description

This course will introduce you to fundamental statistical concepts and modern statistical practice. You will study statistical data investigations, summary statistics, data visualisation and probability as a measure for uncertainty. You will then build upon these topics and learn about  sampling, sampling distributions and confidence intervals as the basis for statistical inference. The course will finish with a series of modules looking at common hypothesis testing methods for different types of data. There is an emphasis on conceptual understanding, interpretation of statistical output and the use of statistical technology, namely R, for statistical computation.

Objectives/Learning Outcomes/Capability Development

 This course contributes to the following Program Learning Outcomes for MC004 Master of Statistics and Operations Research and MC242 Master of Analytics:

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

  • an understanding of appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.


  • the ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
  • an understanding of the balance between 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. Elucidate the concept of variation and identify and pose statistical questions requiring investigation
  2. Plan a statistical data investigation including identifying variables and measures and proposing a method of data collection that will answer the question posed.
  3. Collect, manage and store statistical data ready for analysis.
  4. Apply fundamental statistical methods to explore, analyse and visualise data and test statistical hypotheses
  5. Interpret statistical analysis and draw conclusions in context and in the presence of uncertainty
  6. Use the free and powerful statistical package R for statistical computing and reproducible analysis. 

Overview of Learning Activities

This course runs both online and face-to-face. You are required to have regular and reliable access to a high speed internet connection. The delivery of this course is a little different to what you might expect in a normal lecture. Online course content and materials replace traditional lectures and labs. Class time is used for demonstrations, discussions, and working collaboratively with other students on activities, problems and investigations.

Online exercises and staggered assignments throughout the semester are provided to consolidate learning and prepare for the final exam. The course emphasises conceptual understanding, interpretation of statistical output and the use of statistical technology, namely R, for statistical computation.

Students will stay in communication and actively participate in course discussions outside of class time through an online learning community.

Overview of Learning Resources

There are no prescribed texts for this course. All course content, notes and learning materials will be available through the course website.

Students are highly recommended to bring along a portable computing device to class, preferably a laptop, with WiFi access to the RMIT University network. Those bringing tablets are encouraged to bring a portable keyboard and mouse. Students without portable computing devices may be able to use a limited number of room computers if available or share with other students. All course materials and learning activities will be available online. Students unable to bring a portable computing device to class will be able to work through material in their own time.

This course will use the statistical software package R and the RStudio integrated development environment. R and RStudio are free. Students will require R and RStudio installed on their personal computing device. Student will be notified of ways to access this software on campus computers and through online services.

Overview of Assessment

This course has no hurdle requirements.

Assessment Tasks


Assessment Task 1:  Assignments  

Assignments staggered throughout the semester.

Weighting 40%

This assessment task supports CLOs 1, 2, 3, 4, 5 and 6.


Formative Assessment Task 2:Module Exercises

Course module exercises that aim to develop student understanding and practice the application of statistics using technology.

Weighting 10%

This assessment task supports CLO 4, 5 and 6.


Assessment Task 3:  Final Examination

A two hour final examination during the exam period.

Weighting 50%

This assessment task supports CLOs 1, 2, 4 and 5