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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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INTRODUCTION TO LINEAR REGRESSION ANALYSIS A comprehensive and current introduction to the fundamentals of regression analysis;
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This volume presents in detail the fundamental theories of linear regression analysis and diagnosis, as well as the relevant statistical;
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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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INTRODUCTION TO LINEAR REGRESSION ANALYSIS;
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Simultaneous confidence bands enable more intuitive and detailed inference of regression analysis than the standard inferential methods;
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Simultaneous confidence bands enable more intuitive and detailed inference of regression analysis than the standard inferential methods;
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modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world;
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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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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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This book is an introduction to regression analysis, focusing on the practicalities of doing regression analysis on real-life data;
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This book is an introduction to regression analysis, focusing on the practicalities of doing regression analysis on real-life data;
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thoroughly updated book, Methods and Applications of Linear Models: Regression and the Analysis of Variance, Third Edition features innovative;
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and its application to energy-growth consumption problem. This book also discusses about intuitionistic fuzzy weighted linear regression model;
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from oxygen saturation recordings using non-linear regression analysis; non-linearity in bridge structural analysis; non-linear analysis;
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This text introduces the fundamental linear regression models used in quantitative research. It covers both the theory and application;
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introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis.;
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The new edition adds a chapter on multiple linear regression in biomedical research, with sections including the multiple linear;
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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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For a solid foundation of important statistical methods, the concise, single-source text unites linear regression with analysis;
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For a solid foundation of important statistical methods, the concise, single-source text unites linear regression with analysis;
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, extensions of the simple linear model to multiple regression and its assumptions, stepwise logit regression, and canonical and discriminant analysis.;
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Robust Regression: Analysis and Applications characterizes robust estimators in terms of how much they weigh. Each observation discusses;
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this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival;
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this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival;
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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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