Course Catalogue

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This course introduces students to key concepts in the statistical sciences including data visualisation, parameter estimation, statistical inference and analysis using statistical software.

On-campus Lectures: 15 x 1 hour lectures

On-campus Labs: 10 x 2 hour labs (several times available)

Drop-in help rooms: 20 x 1 hour optional sessions

Required: Mathematics 1 at grade D or better.

Strongly recommended: Statistics 1Y and Statistics 1Z

Statistics 2R: Probability 1

Mathematics 2A

Mathematics 2B

End-of-course examination (55%); coursework (45%).

Details about online assessment will be included in the course handbook.

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

■ Reassessment opportunities within the semester the course runs will only be available for the problem sheet components of continuous assessment.

■ Reassessment opportunities after the award of the course grade will, generally, not be available for any continual assessment component of these courses.

■ There will be a reassessment opportunity for the degree exam of each course in the following August diet.

The aims of this course are:

■ to introduce students to key formal concepts used in statistics such as sampling distributions and point and interval estimation

■ to equip students to apply statistical methods to solve problems from a wide range of disciplines and real life scenarios

■ to train students to communicate results of statistical analysis in clear, non-technical language

■ to introduce students to statistical analysis software

■ to promote an interest in statistical science and data analysis and encourage students to study more advanced courses.

■ to introduce students to the science of collecting, organizing, summarizing, analyzing, and presenting data

By the end of this course students will be able to:

■ recognise different types of data structures and summarize data using appropriate graphical and numerical methods

■ manipulate and analyse data with appropriate methods using statistical software

■ describe sampling methods and derive sampling distributions of statistics such as sample mean

■ define point and interval estimates and implement point and interval estimation techniques such as maximum likelihood estimation

■ generate, interpret and communicate the output of statistical software related to methods covered in the course

Minimum requirement as  in code of assessment