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Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as bootstrapping and missing data. Updated throughout, this Third Edition includes new chapters on mixed-effects models for hierarchical and longitudinal data. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material in optional sections and chapters throughout the
Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as
*This book eases students into GLMs and motivates the need for GLMs by starting with regression.* A practical working knowledge of good applied statistical practice is developed through the use of these real data sets and numerous case studies*. Each example in the text is cross-referenced with the
This bestseller is known for its modern, practical approach to regression-analysis methods that students will find applicable to real-life problems. APPLIED REGRESSION ANALYSIS AND MULTIVARIABLE METHODS highlights the role of the computer in contemporary statistics with numerous printouts and
This book introduces researchers and students to the concepts and generalized linear models for analyzing quantitative random variables that have one or more bounds. Examples of bounded variables include the percentage of a population eligible to vote (bounded from 0 to 100), or reaction time in
Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing
The text explains the model-based method of intelligence analysis that represents the analyst's mental models of a subject as well as the analyst's reasoning process. It includes dynamic simulations and interactive analytic games, case studies that illustrate a wide range of intelligence problems,
Handbook and reference guide for students and practitioners of statistical regression-based analyses in R Handbook of Regression Analysis with Applications in R, Second Edition is a comprehensive and up-to-date guide to conducting complex regressions in the R statistical programming language. The
This comprehensive text introduces regression, the general linear model, structural equation modeling, the hierarchical linear model, growth curve models, panel data, and event history models, and includes discussion of published implementations of each technique showing how it was used to address
1 A Brief Introduction.- 2 Selected Matrix Algebra Topics and Results.- 3 Generalized Inverses and Solutions to Sytems of Linear Equations.- 4 Moments of a Random Vector and of Linear and Quadratic Forms in a Random Vector.- 5 Types of Linear Models.- 6 Estimability.- 7 Least Squares Estimation for
Linear algebra permeates mathematics, as well as physics and engineering. This book treats diagonalization as a central tool in solving complicated problems in these subjects by reducing coupled linear evolution problems to a sequence of simpler decoupled
Part I: Existing Models and Algorithms for Image Texture Image Texture, Texture Features, and Image Texture Classification and Segmentation Texture Features and Image Texture Models Algorithms for Image Texture Classification Dimensionality Reduction and Sparse Representation Part II: The K-Views
Explores the theoretical and practical aspects of detailed econometric theories and applications within economics, political science, public policy, business, and finance. The book features coverage on a broad range of topics, including cointegration, machine learning, and time series
Highly recommended by the Journal of Official Statistics, The American Statistician, and other journals, Applied Survey Data Analysis, Second Edition provides an up-to-date overview of state-of-the-art approaches to the analysis of complex sample survey data. Building on the wealth of material on
What are the models used in phylogenetic analysis and what exactly is involved in Bayesian evolutionary analysis using Markov chain Monte Carlo (MCMC) methods? How can you choose and apply these models, which parameterisations and priors make sense, and how can you diagnose Bayesian MCMC when
Reflecting the latest practices, trends, and developments from the field, SYSTEMS ANALYSIS AND DESIGN IN A CHANGING WORLD, 7E equips you with a solid foundation of the most important principles of systems analysis, systems design, project management, and new software development approaches. Using
Introduction.- Novel Mathematical Modeling and Stability Analysis of Linear Uncertain Systems Subject to Actuator Saturations.- Commuting Matrices, Equilibrium Points for Control Systems with Single Saturated Input.- Stability and Closed Trajectory for 2nd Order Control Systems with Single
Finite element analysis has been widely applied to study biomedical problems. This book aims to simulate some common medical problems using finite element advanced technologies, which establish a base for medical researchers to conduct further investigations. This book consists of four main parts:
This groundbreaking textbook combines straightforward explanations with a wealth of practical examples to offer an innovative approach to teaching linear algebra. Requiring no prior knowledge of the subject, it covers the aspects of linear algebra - vectors, matrices, and least squares - that are
A concise treatment of modern econometrics and statistics, including underlying ideas from linear algebra, probability theory, and computer programming.This book offers a cogent and concise treatment of econometric theory and methods along with the underlying ideas from statistics, probability
1. Introduction.- 2. Linear Programming.- 3. Nonlinear Programming.-4. Discrete Optimization.- 5. Optimization Under Uncertainty.- 6. Multiobjective Optimization.- 7.Optimal Control and Dynamic Optimization.-
Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian data analysis, as material is explained clearly with concrete examples. Included are step-by-step instructions on how to carry out Bayesian data analyses in the