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Statistical inference involves using data from a sample to draw conclusions about a wider population. Given a partly specified statistical model, in which at least one parameter is unknown, and some observations for which the model is valid, it is possible to draw inferences about the unknown parameters and hence about the population from which the sample is drawn. As such, inference
underpins all aspects of statistics. However, inference can take different forms. It may be adequate to provide a point estimate of a parameter, i.e. a single number. More usually, an interval is required, giving a measure of precision. It may also be necessary to test a pre-specified hypothesis about the parameter(s). These forms of inference can all be considered as special cases of the use of a decision function.

There are a number of different philosophies about how these inferences should be drawn, ranging from that which says the sample contains all the information available about a parameter (likelihood), through that which says account should be taken of what would happen in repeated sampling (frequentist), to that which allows the sample to modify prior beliefs about a parameter’s value (Bayesian).

This Module aims to explore these approaches to parametric statistical inference, particularly through application of the methods to numerous examples.

Statistical learning and data science provide us with new forms of data and powerful new analysis tools. Advances in AI have allowed huge improvements in our ability to predict, but at the same time, these methods and data sources generate important ethical issues that we must consider. This module will provide students with the tools to appreciate the power of AI but also its limitations, and to be able to understand which AI tools might be suitable for particular tasks.

Statistical learning and data science provide us with new forms of data and powerful new analysis tools. Advances in AI have allowed huge improvements in our ability to predict, but at the same time, these methods and data sources generate important ethical issues that we must consider. This module will provide students with the tools to appreciate the power of AI but also its limitations, and to be able to understand which AI tools might be suitable for particular tasks.

Statistical mechanics links the microscopic properties of physical systems to their macroscopic properties. Thermodynamics, which describes macroscopic properties, can then be derived from statistical mechanics with a few well motivated postulates. It leads to a microscopic interpretation of thermodynamic concepts, such as thermal equilibrium, temperature and entropy. In the course the basic principles of statistical mechanics will be introduced with applications to the physics of matter.

Statistical mechanics links the microscopic properties of physical systems to their macroscopic properties. Thermodynamics, which describes macroscopic properties, can then be derived from statistical mechanics with a few well motivated postulates. It leads to a microscopic interpretation of thermodynamic concepts, such as thermal equilibrium, temperature and entropy. In the course the basic principles of statistical mechanics will be introduced with applications to the physics of matter.

Statistical Methods for Finance is a critical module for you to learn basics for future modules on Econometrics, as well as their final year dissertation. This module covers important topics such as probability, discrete and random variables, Probability distributions, normal distribution, hypothesis testing, graphical analysis, correlation and simple regression. Lectures are followed by in-depth practical examples using tools that show the real world implications.

Statistical Methods for Finance is a critical module for you to learn basics for future modules on Econometrics, as well as their final year dissertation. This module covers important topics such as probability, discrete and random variables, Probability distributions, normal distribution, hypothesis testing, graphical analysis, correlation and simple regression. Lectures are followed by in-depth practical examples using tools that show the real world implications.

The main aim of the module is to provide the students with necessary knowledge of statistics and stochastic processes to carry out simple statistical procedures and to be able to develop simulation and other models widely employed in OR. The model is split into two parts: Statistics and Stochastic Processes.

The main aim of the module is to provide the students with necessary knowledge of statistics and stochastic processes to carry out simple statistical procedures and to be able to develop simulation and other models widely employed in OR. The model is split into two parts: Statistics and Stochastic Processes.

The main aim of the module is to provide the students with necessary knowledge of statistics and stochastic processes to carry out simple statistical procedures and to be able to develop simulation and other models widely employed in OR. The model is split into two parts: Statistics and Stochastic Processes.