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Linear Model Theory : Exercises and Solutions
This book contains 296 exercises and solutions covering a wide variety of topics in linear model theory, including generalized inverses, estimability, best linear unbiased estimation and prediction, ANOVA, confidence intervals, simultaneous confidence intervals, hypothesis testing, and variance component estimation.The models covered include the Gauss-Markov and Aitken models, mixed and random effects models, and the general mixed linear model.Given its content, the book will be useful for students and instructors alike.Readers can also consult the companion textbook Linear Model Theory - With Examples and Exercises by the same author for the theory behind the exercises.
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DIY Reciprocating Linear Actuator Telescopic Linear Actuator Kit Metal Gear Reduction Motor Speed
DIY Reciprocating Linear Actuator Telescopic Linear Actuator Kit Metal Gear Reduction Motor Speed
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Foundations of Linear and Generalized Linear Models
A valuable overview of the most important ideas and results in statistical modeling Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models.The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications,and examples with crafted models to elucidate key ideas and promote practical modelbuilding. The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about the effects of explanatory variables.Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data.Focusing on the theoretical underpinnings of these models, Foundations ofLinear and Generalized Linear Models also features: An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methodsAn overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high dimensional problemsNumerous examples that use R software for all text data analysesMore than 400 exercises for readers to practice and extend the theory, methods, and data analysisA supplementary website with datasets for the examples and exercises An invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, Foundations of Linear and Generalized Linear Models is also an excellent reference for practicing statisticians and biostatisticians, as well as anyone who is interested in learning about the most important statistical models for analyzing data.
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Telescopic Linear Actuator Kit with New Stand DC 24V Stroke Adjustable Cycling Reciprocating Linear
Telescopic Linear Actuator Kit with New Stand DC 24V Stroke Adjustable Cycling Reciprocating Linear
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VEVOR Linear Rail 2PCS 500mm SBR16 Linear Slide Rail 4PCS SBR16UU Bearing Block CNC Kit Linear Rails and Bearings Kit CNC Rails Linear Rail Set for CNC Machines Automated Machines and Equipments
VEVOR Linear Rail 2PCS 500mm SBR16 Linear Slide Rail 4PCS SBR16UU Bearing Block CNC Kit Linear Rails and Bearings Kit CNC Rails Linear Rail Set for CNC Machines Automated Machines and EquipmentsSBR16 Linear Rail KitsHigh-quality MaterialHigh Hardness & RigidityComplete Sliding UnitEasy To AssembleWidely UsedRail shaft length: 500mm/19.7inch,Rail Shafts: 2 pieces SBR16,Type: Linear slide rail set,Rail shaft diameter: 16mm/0.63inch,Performance: Low noise,Bearing Blocks: 4 pieces SBR16UU
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Linear Algebra
Linear Algebra is intended for a one-term course at the junior or senior level.It begins with an exposition of the basic theory of vector spaces and proceeds to explain the fundamental structure theorems for linear maps, including eigenvectors and eigenvalues, quadric and hermitian forms, diagonalization of symmetric, hermitian, and unitary linear maps and matrices, triangulation, and Jordan canonical form.The book also includes a useful chapter on convex sets and the finite-dimensional Krein-Milman theorem.The presentation is aimed at the student who has already had some exposure to the elementary theory of matrices, determinants, and linear maps.However, the book is logically self-contained. In this new edition, many parts of the book have been rewritten and reorganized, and new exercises have been added.
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Extending the Linear Model with R : Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition
Start Analyzing a Wide Range of Problems Since the publication of the bestselling, highly recommended first edition, R has considerably expanded both in popularity and in the number of packages available.Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition takes advantage of the greater functionality now available in R and substantially revises and adds several topics. New to the Second EditionExpanded coverage of binary and binomial responses, including proportion responses, quasibinomial and beta regression, and applied considerations regarding these models New sections on Poisson models with dispersion, zero inflated count models, linear discriminant analysis, and sandwich and robust estimation for generalized linear models (GLMs) Revised chapters on random effects and repeated measures that reflect changes in the lme4 package and show how to perform hypothesis testing for the models using other methodsNew chapter on the Bayesian analysis of mixed effect models that illustrates the use of STAN and presents the approximation method of INLA Revised chapter on generalized linear mixed models to reflect the much richer choice of fitting software now availableUpdated coverage of splines and confidence bands in the chapter on nonparametric regressionNew material on random forests for regression and classification Revamped R code throughout, particularly the many plots using the ggplot2 packageRevised and expanded exercises with solutions now includedDemonstrates the Interplay of Theory and PracticeThis textbook continues to cover a range of techniques that grow from the linear regression model.It presents three extensions to the linear framework: GLMs, mixed effect models, and nonparametric regression models.The book explains data analysis using real examples and includes all the R commands necessary to reproduce the analyses.
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Linear Systems
This textbook provides a mathematical introduction to linear systems, with a focus on the continuous-time models that arise in engineering applications such as electrical circuits and signal processing.The book introduces linear systems via block diagrams and the theory of the Laplace transform, using basic complex analysis. The book mainly covers linear systems with finite-dimensional state spaces.Graphical methods such as Nyquist plots and Bode plots are presented alongside computational tools such as MATLAB.Multiple-input multiple-output (MIMO) systems, which arise in modern telecommunication devices, are discussed in detail.The book also introduces orthogonal polynomials with important examples in signal processing and wireless communication, such as Telatar’s model for multiple antenna transmission.One of the later chapters introduces infinite-dimensional Hilbert space as a state space, with the canonical model of a linear system.The final chapter covers modern applications to signal processing, Whittaker’s sampling theorem for band-limited functions, and Shannon’s wavelet. Based on courses given for many years to upper undergraduate mathematics students, the book provides a systematic, mathematical account of linear systems theory, and as such will also be useful for students and researchers in engineering.The prerequisites are basic linear algebra and complex analysis.
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