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Kutner, Nachtsheim, Neter, Wasserman, Applied Linear Regression Models, 4/e (ALRM4e) is the long established leading authoritative text and;
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, quasibinomial and beta regression, and applied considerations regarding these models New sections on Poisson models with dispersion, zero inflated;
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methods Applied Linear Regression, Fourth Edition is an excellent textbook for upper-undergraduate and graduate-level students, as well as an;
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of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear;
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approaches to understanding and working with models and theory of linear regression. The Third Edition provides readers with the necessary;
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. One is a two-term sequence focusing on regression followed by ANOVA/Experimental design. Applied Linear Statistical Models serves that market;
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Master the fundamentals of regression without learning calculus with this one-stop resource The newly and thoroughly revised 3rd;
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calculus and statistical inference. In this substantially revised and updated new edition the overview on regression models has been extended;
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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;
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and Applications with SAS, Second Edition presents analyses of simple and complex models, both univariate and multivariate, that employ data;
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and Applications with SAS, Second Edition presents analyses of simple and complex models, both univariate and multivariate, that employ data;
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a basic knowledge of elementary statistics, Applied Regression Analysis, Third Edition focuses on the fitting and checking of both linear;
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. Linear Models examines the subject from a mean model perspective, defining simple and easy-to-learn rules for building mean models, regression;
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Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Models, Third Edition provides a cohesive;
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generalized linear mixed models nonlinear mixed models Professionals and students with a background in two-way ANOVA and regression and a basic;
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This textbook for a second course in basic statistics for undergraduates or first-year graduate students introduces linear regression;
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Thoroughly updated throughout, A First Course in Linear Model Theory, Second Edition is an intermediate-level statistics text that fills an;
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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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Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied;
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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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. Many practitioners work with data that fail the assumptions of the common linear regression models, necessitating more advanced modeling;
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performance of well-known asymptotic tests for regression models. This book uses the linear regression model as a framework for introducing simulation;
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performance of well-known asymptotic tests for regression models. This book uses the linear regression model as a framework for introducing simulation;
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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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assess part of a multiple linear regression model with the zero function, to compare two regression models, and to evaluate more than two;
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assess part of a multiple linear regression model with the zero function, to compare two regression models, and to evaluate more than two;
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applicable for both beginning and advanced students of applied statistics. Generalized linear models (GLMs) are powerful tools in applied;
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