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, 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 confidence bands enable more intuitive and detailed inference of regression analysis than the standard inferential methods;
Vergelijkbare producten zoals 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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Providing a modern treatment of regression analysis, linear models and closely related methods, this book introduces students to one of the;
Vergelijkbare producten zoals Applied Regression Analysis and Generalized 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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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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focusing on methods for analyzing particular kinds of data. It covers normal, Poisson, and binomial distributions; linear regression models;
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prediction. The authors discuss the application of statistical theories and methodologies to various linear models such as the linear regression;
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count models, linear discriminant analysis, and sandwich and robust estimation for generalized linear models (GLMs) Revised chapters on random;
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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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nominal and ordinal regression, survival analysis, non-linear associations and longitudinal analysis Connects Bayesian analysis and MCMC;
Vergelijkbare producten zoals An Introduction to Generalized Linear Models
nominal and ordinal regression, survival analysis, non-linear associations and longitudinal analysis Connects Bayesian analysis and MCMC;
Vergelijkbare producten zoals An Introduction to Generalized Linear Models
This text introduces the fundamental linear regression models used in quantitative research. It covers both the theory and application;
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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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. Extensions of these concepts include GLMs with heteroscedastic errors that encompass weighted least squares regression and categorical data analysis;
Vergelijkbare producten zoals 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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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 provides theoretical foundations and guides practical implementation;
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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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regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data;
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and exposition by means of examples. Sufficient theoretical foundations are provided so that applications of regression analysis can be;
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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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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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This book introduces several topics related to linear model theory, including: multivariate linear models, discriminant analysis, principal;
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models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to;
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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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, regression analysis, response surface methodology, variance components analysis, and more, making it necessary to understand the theory behind linear;
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