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Fixed Effects Regression Models

This book demonstrates how to estimate and interpret fixed-effects models in a variety of different modeling contexts.;

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An R Companion to Linear Statistical Models

programming language. These examples cover methods used for linear regression and designed experiments with up to two fixed-effects factors, including;

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An R Companion to Linear Statistical Models

programming language. These examples cover methods used for linear regression and designed experiments with up to two fixed-effects factors, including;

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Methods And Applications Of Linear Models

focus on fixed effects models, Methods and Applications of Linear Models: Regression and the Analysis of Variance, Third Edition also features;

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Interaction Effects in Logistic Regression

This book provides an introduction to the analysis of interaction effects in logistic regression by focusing on the interpretation of the;

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Mathematical Statistics

following: analysis of variance (ANOVA) for models with fixed, random, and mixed effects; regression analysis is also first presented for linear;

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Regression Models as a Tool in Medical Research

While regression models have become standard tools in medical research, understanding how to properly apply the models and interpret the;

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Regression Models As A Tool In Medical Research

While regression models have become standard tools in medical research, understanding how to properly apply the models and interpret the;

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Nonparametric Statistical Methods Using R

, procedures for regression models, computation for general fixed-effects ANOVA and ANCOVA models, and time-to-event analyses. The last two chapters;

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Nonparametric Statistical Methods Using R

two-sample problems, procedures for regression models, computation for general fixed-effects ANOVA and ANCOVA models, and time-to-event;

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Regression Models for Categorical Dependent Variables Using Stata, Third Edition

Regression Models for Categorical Dependent Variables Using Stata, Third Edition shows how to use Stata to fit and interpret regression;

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A First Course in Linear Model Theory

. Revised or expanded topics include linear fixed, random and mixed effects models, generalized linear models, Bayesian and hierarchical linear;

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Linear Model Methodology

contemporary topics in linear models, including the adequacy of Satterthwaite's approximation, unbalanced fixed- and mixed-effects models;

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Contextual Analysis

regression coefficients as fixed parameters or as random variables.;

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Dynamic Regression Models for Survival Data

This book studies and applies modern flexible regression models for survival data with a special focus on extensions of the Cox model and;

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Dynamic Regression Models for Survival Data

This book studies and applies modern flexible regression models for survival data with a special focus on extensions of the Cox model and;

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Methods and Applications of Longitudinal Data Analysis

trends over timelinear mixed regression models with both fixed and random effectscovariance pattern models on correlated errors;

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Analysis Techniques for non-Experimental Data

matching, instrumental variables, the regression discontinuity design, fixed effects models, and trajectory analysis. The book starts with a short;

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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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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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Extending The Linear Model With R

Nonparametric Regression Models, Second Edition takes advantage of the greater functionality now available in R and substantially revises and adds;

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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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Logistic Regression

In this text, author Scott Menard provides coverage of not only the basic logistic regression model but also advanced topics found in no;

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App Panel Data Analysis For Economic & S

over time. It covers fixed and random effects models, models for change scores and event history models. All statistical methods are explained in an;

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Nonparametric Regression Methods For Longitudinal Data Analysis

nonparametric regression techniques for longitudinal data analysis and systematically investigates the incorporation of mixed-effects modeling;

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Smoothing Splines

smoothing spline ANOVA, extended and generalized smoothing spline ANOVA, vector spline, nonparametric nonlinear regression, semiparametric;

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Smoothing Splines

smoothing spline ANOVA, extended and generalized smoothing spline ANOVA, vector spline, nonparametric nonlinear regression, semiparametric;

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