In the digital age it is becoming ever easier, and more important, to collect data to make predictions and inform future decisions in science and society, varying from analysing communities and trends on the internet, to patients’ responses to new drugs, to the impact of global events on the stock market, to population sizes of endangered species, and climate change.
Many professions require skills in extracting useful information from data and to manage and present data accurately. You’ll learn the core methods and principles of probability theory and statistics, and gain skills in applying these methods to analyse sample data and draw inferences or generalisations. You’ll learn how to estimate the value of an unknown parameter or to construct a confidence interval within which the parameter is likely to lie from sample data, and do hypothesis testing. To support your learning and illustrate the methods, the statistical computing package R is used throughout the module.
Lecture 60, PC 4, Revision 4
Problem sheets worth 30%.
Examination (2-hours) worth 70%.
Reassessment Method: Like-for-like Including composite form of reassessment for failed coursework components – single replacement problem sheet.
On successfully completing the module, students will be able to:
1) Critically appraise the underlying concepts and core principles of probability and statistics.
2) Apply and interpret the core principles of probability and statistics within the context of statistical analysis.
3) Present, evaluate and interpret quantitative data accurately.
4) Reason with, and evaluate arguments, using the core concepts and basic principles of probability and statistics.
5) Communicate their work and knowledge in the context of the subject accurately and using sound arguments.
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