Statistics

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%).

This course is part of the LM degree program and is intended for advanced level student. Enrollment is by consent of instructor. This course provides an overview of the basic tools used by health economists for their empirical investigations, the linear regression model for the analysis of cross-sectional data, and under what conditions the estimated relationship has a causal interpretation. Drawing on critical discussion about some micro-economic applications, the student receives specific data to practice at the computer and learn the basic skills to perform empirical work using the software STATA. At the end of the course, the student is able to understand scientific articles using the linear regression model and is also able to perform their own analysis with this tool. The course discusses topics including an introduction to econometric methods, data, and STATA; simple and multiple regression models (advanced); and a variety of data issues.