This course will teach you how to design studies to produce statistically valid conclusions. Topics covered in the course include: overview of validity and bias, selection bias, information bias, and confounding bias.

This course will explain the theory of generalized linear models (GLM), outline the algorithms used for GLM estimation, and explain how to determine which algorithm to use for a given data analysis.

This course will teach you a number of advanced topics in optimization: how to formulate and solve network flow problems; how to model and solve optimization problems; how to deal with multiple objectives in optimization problems, and techniques for handling optimization problems.

Learn how to understand and work with data using Excel.

This course will teach you the statistical basis for analyzing multiple-choice survey or test data – item response theory (IRT).

This course will teach you the basic statistical principles in the design and analysis of randomized controlled trials.

This course will teach you the fundamental concepts and theory of Structural Equation Modeling, including model specification, model identification, model estimation, model testing, and model modification.

This course will teach you the equivalent of a semester course in introductory statistics.

This course will teach you the basic theory of linear and non-linear mixed effects models, hierarchical linear models, algorithms used for estimation, primarily for models involving normally distributed errors, and examples of data analysis.

This course will teach you key multivariate procedures such as multivariate analysis of variance (MANOVA), principal components, factor analysis, and classification.

This course will teach you how multiple linear regression models are derived, assumptions in the models, how to test whether data meets assumptions, and develop strategies for building and understanding useful models.

This course will teach you modeling technique making decisions in the presence of risk or uncertainty, including risk analysis using Monte Carlo simulation, queuing theory for problems involving waiting lines, and decision trees for analyzing problems with multiple discrete decision alternatives.

This course will teach you how to make sample size determinations for various statistical tests and for confidence intervals, as needed for experimental studies such as comparison studies, as well as for other types of experiments.

This course will teach you spatial statistical analysis methods to address problems in which spatial location. This course will explain and give examples of the analysis that can be conducted in a geographic information system such as ArcGIS or Mapinfo.

This course will teach you how to extract data from a relational database using SQL and merge data into a single file in R so that you can perform statistical operations.