Course Catalogue

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This course further develops key concepts in the statistical sciences including hypothesis testing, linear modelling and the analysis of data using statistical software.

On-campus Lectures: 20 x 1 hour lectures

On-campus Labs: 10 x 1.5 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

Statistics 2S: Statistical Methods, Models and Computing 1

Mathematics 2A

Mathematics 2B

Statistics 2X: Probability 2

End-of-course examination (60%); coursework (40%).

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 hypothesis testing and statistical modelling

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

■ to develop the ability of students to communicate results of statistical analysis in clear, non-technical language

■ to further develop students' statistical analysis software skills

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

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

■ formulate hypothesis tests using terms such as null and alternative hypotheses and carry out a range of common hypothesis tests

■ formulate linear models in algebraic and vector-matrix notations, derive estimators for model parameters and interpret model parameters

■ build linear models, assess the assumptions and suitability of fitted linear models and select between different models

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

Minimum requirement as in code of assessment