Statistics

The course studies the basic theory of stochastic processes and research methods through and economics and management perspective. Topics include financial engineering theory, conditional mathematical expectation, martingale, Poisson process, update process, Markov chain, Brownian motion, stochastic integral, and stochastic financial model.

This course introduces students to quantitative text analysis, reviews selected methods falling within this category of approaches, and illustrates their implementation in the statistical programming language R. It covers the origins of quantitative approaches to studying text and how they complement traditional, qualitative methodologies. Using recent peer-reviewed publications, the course explores how these methodological approaches can be used to answer sociological questions and, in hands-on lab session, students implement selected techniques in R.

This course introduces students to basic probability theory and statistical inference. Topics include basic concepts of probability, conditional probability, independence, random variables, joint and marginal distributions, mean and variance, some common probability distributions, sampling distributions, estimation, and hypothesis testing based on a normal population.

This course introduces the fundamental statistical concepts and techniques to make informed decision for a variety of real-world business and economic problems. The course objective includes collection, visualization, summarization, and analysis of statistical data to draw statistical inferences. It discusses numerical summaries of qualitative and quantitative variables, random sampling theory, central limit theorem, the normal distribution, one/two sample hypothesis testing, confidence intervals and goodness-of-fit method. Applications of Software such as MINITAB and R are also demonstrated.

This course examines probability, the concept of random variables, special distributions including the Binomial, Hypergeometric, Poisson, Normal, Geometric and Gamma and statistical estimation. This course will investigate univariate techniques in data analysis and for the most common statistical distributions that are used to model patterns of variability. Students will learn the method of moments and maximum likelihood techniques for fitting statistical distributions to data.