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Regression Analysis and Linear Models

, generalized linear model, etc. This book presents the complex subject of regression analysis and linear models, in the most comprehensible and easy to;

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Simultaneous Inference in Regression

Simultaneous confidence bands enable more intuitive and detailed inference of regression analysis than the standard inferential methods;

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Simultaneous Inference in Regression

Simultaneous confidence bands enable more intuitive and detailed inference of regression analysis than the standard inferential methods;

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Applied Regression Analysis and Generalized Linear Models

Providing a modern treatment of regression analysis, linear models and closely related methods, this book introduces students to one of the;

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Methods And Applications Of Linear Models

thoroughly updated book, Methods and Applications of Linear Models: Regression and the Analysis of Variance, Third Edition features innovative;

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Partially Linear Models

In the last ten years, there has been increasing interest and activity in the general area of partially linear regression smoothing;

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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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Advanced Linear Models

prediction. The authors discuss the application of statistical theories and methodologies to various linear models such as the linear regression;

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Extending The Linear Model With R

count models, linear discriminant analysis, and sandwich and robust estimation for generalized linear models (GLMs) Revised chapters on random;

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Generalized Additive Models

This book describes an array of power tools for data analysis that are based on nonparametric regression and smoothing techniques. These;

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An Introduction to Generalized Linear Models

nominal and ordinal regression, survival analysis, non-linear associations and longitudinal analysis Connects Bayesian analysis and MCMC;

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An Introduction to Generalized Linear Models

nominal and ordinal regression, survival analysis, non-linear associations and longitudinal analysis Connects Bayesian analysis and MCMC;

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Linear Regression

This text introduces the fundamental linear regression models used in quantitative research. It covers both the theory and application;

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Logistic Regression Using SAS

If you are a researcher or student with experience in multiple linear regression and want to learn about logistic regression, Paul;

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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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The Coordinate-Free Approach to Linear Models

simpler than the more common matrix approach to linear regression, analysis of variance, and analysis of covariance models in statistics. The book;

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Spatial Regression Analysis Using Eigenvector Spatial Filtering

Spatial Regression Analysis Using Eigenvector Spatial Filtering provides theoretical foundations and guides practical implementation;

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Sensitivity Analysis in Linear Regression

This volume treats linear regression diagnostics as a tool for the application of linear regression models to real-life data. The;

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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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Applied Linear Statistical Models

and exposition by means of examples. Sufficient theoretical foundations are provided so that applications of regression analysis can be;

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Logistic Regression Using SAS

If you are a researcher or student with experience in multiple linear regression and want to learn about logistic regression, Paul;

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Data Analysis Using Regression & Multi

researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety;

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Advanced Linear Modeling

This book introduces several topics related to linear model theory, including: multivariate linear models, discriminant analysis, principal;

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Linear Models in Statistics

models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to;

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Data Analysis Using Regression and Multilevel/Hierarchical Models

perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the;

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Linear Model Methodology

, regression analysis, response surface methodology, variance components analysis, and more, making it necessary to understand the theory behind linear;

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