### Bayesian Statistics in R

This course will teach you how to specify and run Bayesian modeling procedures using regression models for continuous, count and categorical data Using R and the associated R package JAGS.

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Bayesian Statistics in R

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Bootstrap Methods

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Categorical Data Analysis

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Generalized Linear Models

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Introduction to Bayesian Hierarchical and Multi-level Models

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Introduction to Bayesian Statistics

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Introduction to MCMC and Bayesian Regression via rstan

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Introduction to Resampling Methods

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Introduction to Structural Equation Modeling (SEM)

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Matrix Algebra

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Maximum Likelihood Estimation

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Mixed and Hierarchical Linear Models

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Modeling Count Data

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Modeling in R

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Multivariate Statistics

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Principal Components and Factor Analysis

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Regression Analysis

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Sample Size and Power Determination

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Spatial Statistics for GIS Using R

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Statistical and Machine Learning Methods for Analyzing Clusters and Detecting Anomalies

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Structural Equation Modeling (SEM) Using R

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Survival Analysis

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This course will teach you how to specify and run Bayesian modeling procedures using regression models for continuous, count and categorical data Using R and the associated R package JAGS.

This course will teach you the basic theory and application of the bootstrap family of procedures with the emphasis on applications.

This course will teach you the analysis of contingency table data. Topics include tests for independence, comparing proportions as well as chi-square, exact methods, and treatment of ordered data. Both 2-way and 3-way tables are covered.

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 how to extend the Bayesian modeling framework to cover hierarchical models and to add flexibility to standard Bayesian modeling problems.

This course will teach you the basic ideas of Bayesian Statistics: how to perform Bayesian analysis for a binomial proportion, a normal mean, the difference between normal means, the difference between proportions, and for a simple linear regression model.

In this course, students learn how to apply Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling using R and rstan.

The course introduces the basic concepts and methods of resampling methods including bootstrap procedures and permutation with little or no complex theory or confusing notation.

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 basics of vector and matrix algebra and operations necessary to understand multivariate statistical methods, including the notions of the matrix inverse, generalized inverse and eigenvalues and eigenvectors.

This course will teach you the derivation of maximum likelihood estimates and their properties.

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 regression models for count data, models with a response or dependent variable data in the form of a count or rate, Poisson regression, the foundation for modeling counts, and extensions and modifications to the basic model.

This course will show you how to use R to create statistical models and use them to analyze data.

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

In this course, you will learn how to make decisions in building a factor analysis model – including what model to use, the number of factors to retain, and the rotation method to use.

This course will teach you how multiple linear regression models are derived, the use software to implement them, what assumptions underlie the models, how to test whether your data meet those assumptions and what can be done when those assumptions are not met, and develop strategies for building and understanding useful models.

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 use various cluster analysis methods to identify possible clusters in multivariate data. Methods discussed include hierarchical clustering, k-means clustering, two-step clustering, and normal mixture models for continuous variables.

Structural Equation Modeling (SEM) allows you to go beyond simple single-outcome models, and deal with multiple outcomes and multi-directional causation. You will learn how to

This course will teach you the various methods used for modeling and evaluating survival data or time-to event data.

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