Statistics

The implementation of sound quantitative actuarial models is a vital task to assess risk in insurance, finance, and other industries and professions. This course provides a self-contained introduction to both theoretical and practical implementation of various quantitative modelling techniques applicable to finance and insurance. The course combines diverse quantitative disciplines, from probability to statistics, from actuarial science to quantitative finance. Students are able to apply the acquired knowledge to evaluate various insurance products.

This course uses statistical models to address scientific questions. While exploring the application of statistical methods, the course covers three key themes—regression modelling for continuous, binomial, and count data including ANOVA; multivariate analysis including cluster and principal component analysis; and the design of research studies.

This series of two courses covers many of the popular approaches for a variety of statistical problems. There is heavy emphasis on the implementation of these methods on real-world data sets in the popular statistical software package R. Part I gives a broad overview of the common problems as well as their most popular approaches. Topics include linear regression model and its extensions, classification methods, resampling methods, regularization and model selection, principal components and clustering methods.

This is an introductory course on statistics and how it can help us answer the kind of questions that arise when we want to better understand the world. We will use real-world examples from the social and natural sciences to establish the foundations of probability and distribution theory, and introduce important statistical skills, from descriptive statistics to sampling and inference. In addition to these examples, students conduct interactive experiments in the classroom to demonstrate the use of key techniques.

This course covers the theory, models, and analysis of probability and basic statistics and their applications with emphasis on electrical and computer engineering problems. The main topics are: Experiments, Model, and Probabilities, Random Variables, Random Variables and Expected Value, Random Vectors, Sums of Random Variables, Parameter Estimation Using the Sample Mean, and Hypothesis Testing. Text: R.D. Yates and D.J. Goodman, PROBABILITY AND STOCHASTIC PROCESSES. Assessment: midterm exam (35%), final exam (35%), homework and problems (25%), participation (5%).