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Data Analysis Using Regression and Multilevel/Hierarchical Models

"Data Analysis Using Regression and MultilevelHierarchical Models is a comprehensive manual for the applied researcher who wants to;

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Data Analysis Using Regression & Multi

Data Analysis Using Regression and MultilevelHierarchical Models, first published in 2007, is a comprehensive manual for the applied;

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Multilevel Analysis

for all models for hierarchical or nested data. This book presents two types of multilevel models: the multilevel regression model; and a;

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Modeling Multilevel Data in Traffic Safety

flexibilities and effectiveness of the Bayesian hierarchical approach in modelling multilevel structure of traffic safety data.;

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Multilevel Modeling of Social Problems

of multilevel data structures enables researchers to combine contextual and longitudinal analyses appropriately. But researchers working on social;

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Multilevel Modeling of Social Problems

of multilevel data structures enables researchers to combine contextual and longitudinal analyses appropriately. But researchers working on social;

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Categorical Data Analysis and Multilevel Modeling Using R

Categorical Data Analysis and Multilevel Modeling Using R provides a practical guide to regression techniques for analyzing binary, ordinal;

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Data Analysis Using Hierarchical Generalized Linear Models with R

within the field and, using data examples, illustrates how to analyse various kinds of data using R. It provides a likelihood approach to;

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Data Analysis Using Hierarchical Generalized Linear Models with R

within the field and, using data examples, illustrates how to analyse various kinds of data using R. It provides a likelihood approach to;

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Data Analysis

analysis of variance. This approach also provides an integrated introduction to multilevel or hierarchical linear models and logistic regression;

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Data Analysis

analysis of variance. This approach also provides an integrated introduction to multilevel or hierarchical linear models and logistic regression;

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Multilevel Analysis

multilevel models for ordinal and count data (Ch. 7) and another on multilevel survival analysis (Ch. 8). Thoroughly updated chapters on multilevel;

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Statistical Methods for Categorical Data Analysis

This book provides a comprehensive introduction to methods and models for categorical data analysis and their applications in social;

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An Introduction to Generalized Linear Models

focusing on methods for analyzing particular kinds of data. It covers normal, Poisson, and binomial distributions; linear regression models;

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Logistic Regression

regression that shows readers how to examine both direct and indirect effects using logistic regression analysis Applies logistic regression;

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Regression Analysis and Linear Models

As a part of statistical modeling, regression analysis is the process and technique of understanding the relationship between dependent and;

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Introducing Multilevel Modeling

This is the first accessible and practical guide to using multilevel models in social research. Multilevel approaches are becoming;

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Introducing Multilevel Modeling

This is the first accessible and practical guide to using multilevel models in social research. Multilevel approaches are becoming;

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Applied Ordinal Logistic Regression Using Stata: From Single-Level to Multilevel Modeling

Provides a unified framework for both single-level and multilevel modeling of ordinal categorical data;

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Principles And Practice Of Structural Equation Modelling

). Reviewed are fundamental statistical concepts--such as correlation, regressions, data preparation and screening, path analysis, and confirmatory;

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Bayesian Hierarchical Models

and data analysis, but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal;

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Bayesian Hierarchical Models

data analysis, but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal;

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Learning Predictive Analytics with R

* Understand how statistical distributions can help retrieve information from data using correlations, linear regression, and multilevel regression;

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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists

regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data;

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Univariate and Multivariate General Linear Models

. Extensions of these concepts include GLMs with heteroscedastic errors that encompass weighted least squares regression and categorical data analysis;

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Univariate and Multivariate General Linear Models

. Extensions of these concepts include GLMs with heteroscedastic errors that encompass weighted least squares regression and categorical data analysis;

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Bayesian Analysis with Python

alternative models. * Predict continuous target outcomes using regression analysis or assign classes using logistic and softmax regression. * Learn;

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