Teaching

(Description taken from here) Distributions of discrete and random variables, sampling distributions. Covers much of the material on the syllabus for the first actuarial exam.

Instructor: Tatsuki Koyama, Ph.D.

(Description taken from here) This course covers the statistical aspects of study designs, monitoring, and analysis. Emphasis Is on studies of human subjects, i.e., clinical trials. Topics include principles of measurement, selection of endpoints, bias, masking, randomization and balance, blocking, study designs, sample size projections, interim monitoring of accumulating results, flexible and adaptive designs, sequential analysis, analysis principles, data and safety monitoring boards (DSMB), and the ethics of animal and human subject experimentation.

Instructor: Cole Beck, B.S.

(Description taken from here) This course is designed for students who seek to develop skills In statistical computing using the R programming language. STATA for statistical analysis will be introduced briefly. Students will learn to use R for data manipulation, report generating, data presentation, and data tabulation and summarization. Topics will include organization and documentation of data, input and export of datasets, methods of cleaning data, tabulation and graphing of data, programming capabilities, and an introduction to simulations and bootstrapping. Students will also be introduced to LaTex, Markdown, and knitr for report writing.

Instructor: Andrew Spieker, Ph.D.

(Description taken from here) This is the second in a two-course series designed for students who seek to develop skills in modern biostatistical reasoning and data analysis. Students learn modern regression analysis and model-building techniques from an applied perspective. Theoretical principles will be demonstrated with real-world examples from biomedical studies. This course requires substantial statistical computing in software packages Stata and/or R. The course cover regression modeling for continuous outcomes, including simple linear regression, multiple linear regression, and analysis of variance In one-way, two-way, multi-way, and analysis of covariance models. Data types to be modeled Include continuous outcomes (classical regression models), binary outcomes (logistic models), ordinal outcomes (proportional odds models), count outcomes (Poisson/negative binomial models), and time-to-event outcomes (Kaplan-Meier curves, Cox proportional hazard modeling). Incorporated Into the presentation of these models are subtopics such as regression diagnostics, nonparametric regression, splines, data reduction techniques, model validation, parametric bootstrapping, and methods for handling missing data.

Instructor: Hakmook Kang, Ph.D.

(Description taken from here) Students are exposed to the theoretical framework for linear and generalized models. The first half of the semester covers linear models: multivariate normal theory, least squares estimation, limiting chi-square and F-distributions, sums of squares (partial, sequential) and expected sum of squares, weighted least squares, orthogonality, Analysis of Variance (ANOVA). The second half of the semester focuses on generalized linear models: binomial, Poisson, multinomial errors, Introduction to categorical data analysis, conditional likelihoods, quasi-likelihoods, model checking.

Instructors: Megan E. Mocko, M.S. and Maria I. Ripol, M.S.

(Description taken from here) Graphical and numerical descriptive measures. Simple linear regression. Basic probability concepts, random variables, sampling distributions, central limit theorem. Large and small sample confidence Intervals and significance tests for parameters associated with a single population and for comparison of two populations. Use of statistical computer software and computer applets to analyze data and explore new concepts.