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This course aims to provide post graduate students with no or limited knowledge of statistics, the fundamental statistical concepts, methods and data analysis skills necessary to undertake quantitative research and interpret subsequent results. The course is aimed at developing statistical thinking and literacy for students from diverse disciplines and is a pre-requisite for most high-level statistics courses.

This course focuses primarily on the graphical and numerical methods of data analysis including descriptive and inferential statistics for post-graduate students. Emphasis is on the understanding of the concepts and principles associated with statistical methods and skills required for the analysis of real-life for quantitative studies. Core components of the course include the use of a popular statistical software package (SPSS) and the development of problem-solving skills relevant to many disciplines. However, the mathematical underpinnings of the methods are not covered in detail in this course; other statistics courses cover this aspect.

On Completion of this course students should be able to:

1. Recognise types of data and describe them using appropriate graphical and numerical analyses including exploring relationships in data;
2. Distinguish between different methods of data generation, collection and analysis;
3. Select and implement appropriate statistical methods to analyse quantitative data to answer an underlying research question;
4. Prepare, manage, format and analyse data using a popular statistical computer package in order to apply appropriate statistical inferencing procedure;
5. Apply professional skills to effectively present and communicate results of statistical analyses including interpretation and justification of statistical decisions to a diverse scholarly audience.