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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

/wl/mybook.html Most of the book focuses on normal-error linear regression models. The author presents simultaneous confidence bands for a;

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Simultaneous Inference in Regression

/wl/mybook.html Most of the book focuses on normal-error linear regression models. The author presents simultaneous confidence bands for a;

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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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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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Confidence Intervals in Generalized Regression Models

a number of different regression models, from very simple to more complex ones. It covers the general linear model (GLM), nonlinear;

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Confidence Intervals in Generalized Regression Models

a number of different regression models, from very simple to more complex ones. It covers the general linear model (GLM), nonlinear;

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Extending The Linear Model With R

Demonstrates the Interplay of Theory and Practice This textbook continues to cover a range of techniques that grow from the linear regression model;

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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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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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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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Applied Linear Models with SAS

This textbook for a second course in basic statistics for undergraduates or first-year graduate students introduces linear regression;

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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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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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Nonparametric Regression and Spline Smoothing

in partially linear models; practical aspects, problems and methods for confidence intervals and bands; local polynomial regression; and form and;

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Nonparametric Regression and Spline Smoothing

in partially linear models; practical aspects, problems and methods for confidence intervals and bands; local polynomial regression; and form and;

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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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Bootstrap Tests For Regression Models

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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Bootstrap Tests for Regression Models

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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Linear Models

. 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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Multilevel and Longitudinal Modeling Using Stata, Volume I

part provides a review of the methods of linear regression. The second part provides an in-depth coverage of the two-level models, the simplest;

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An Introduction to Generalized Linear Models

distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming;

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Univariate and Multivariate General Linear Models

analyzes double multivariate linear, growth curve, seeming unrelated regression (SUR), restricted GMANOVA, and hierarchical linear models. New;

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An Introduction to Generalized Linear Models

distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming;

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Univariate and Multivariate General Linear Models

analyzes double multivariate linear, growth curve, seeming unrelated regression (SUR), restricted GMANOVA, and hierarchical linear models. New;

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Regression Diagnostics

of regression models: the normal linear regression model (LM), in which the response variable is quantitative and assumed to have a normal distribution;

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