Available courses

Aim of Course:

Structural Equation Modeling (SEM) is a modeling technique that allows you to create a deeper understanding of how your data is structured. You will learn how to create structural equation models using the lavaan package in R.

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This course covers the fundamental concepts and theory of Structural Equation Modeling: model specification, model identification, model estimation, model testing, and model modification.

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This course continues the work of Predictive Analytics 1, and introduces you to additional techniques in predictive analytics, also called predictive modeling, the most prevalent form of data mining.

Aim of Course:

This course, the second in a three-course sequence, provides an easy introduction to inference and association through a series of practical applications, based on the resampling/simulation approach.

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This course covers the basic statistical principles in the design and analysis of randomized controlled trials.

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This course extends the Bayesian modeling framework to cover hierarchical models, and to add flexibility to standard Bayesian modeling problems.

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This course will explain meta analysis - the methods that are used to assess multiple statistical studies on the same subject and draw conclusions.

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To provide an easy introduction to ANOVA and multiple linear regression through a series of practical applications.

Aim of Course:

This course, the second in a three-course sequence, provides an easy introduction to inference and association through a series of practical applications, based on the resampling/simulation approach.

Aim of Course:

This course, the second in a three-course sequence, provides an easy introduction to inference and association through a series of practical applications, based on the resampling/simulation approach.