Course content will be delivered by formal lectures supported by daily classes. All topics will be explained during lectures accompanied by examples demonstrating the material. A comprehensive course pack will be provided and daily exercise sets will be distributed to provide an opportunity to practise problems. Solutions to exercises will be discussed and distributed in the classes. Supplementary materials will be accessible via the course’s virtual learning environment to facilitate additional self-study.
The course provides a precise and accurate treatment of probability, distribution theory and statistical inference. As such, there will be a strong emphasis on mathematical statistics as important discrete and continuous probability distributions are covered. Properties of these distributions will be investigated followed by a thorough overview of parameter estimation techniques.
Application of this theory to the construction and performance of statistical tests follows, leading to multiple linear regression which is widely used in much economic and statistical modelling.
In summary, the main objectives of this course are:
• To provide a solid understanding of distribution theory which can be drawn upon when developing appropriate statistical tests. Useful properties of some important distributions will be reviewed as well as parameter estimation techniques for various probability distributions.
• To facilitate a comprehensive understanding of the main branches of statistical inference, and to develop the ability to formulate the hypothesis of interest, derive the necessary tools to test this hypothesis and interpret the results.
• To introduce the fundamental concepts of statistical modelling, with an emphasis on linear regression models with multiple explanatory variables.
Collectively, these topics provide a solid training in statistical analysis. As such, this course would be of value to those intending to pursue further study in statistics, econometrics and/or empirical economics. Indeed, the quantitative skills developed by the course are readily applicable to all fields involving real data analysis.
As stand-alone resources will be provided, there will be no need to rely on a particular text. There are several good texts at the right level for this course which can be used in support of the course materials, including:
Freedman, D., Pisani, R. and R. Purves (2007) Statistics, Norton, 4th edition.
Larsen, R.J. and M.J. Marx (2011) An Introduction to Mathematical Statistics and Its Applications, Pearson Education, 5th edition.
*A more detailed reading list will be supplied prior to the start of the programme
**Course content, faculty and dates may be subject to change without prior notice