Products related to Linear:
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Linear Regression Analysis
Concise, mathematically clear, and comprehensive treatment of the subject. * Expanded coverage of diagnostics and methods of model fitting. * Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models. * More than 200 problems throughout the book plus outline solutions for the exercises. * This revision has been extensively class-tested.
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Critical Doubt
They met in a war-torn city on the other side of the world and shared an anonymous night of passion. They didn't intend to meet again. Nor did they think they'd be reunited by sinister secrets... Five years later, FBI Agent Savannah Kane is headed to a small town in Georgia for the funeral of her best friend's husband. Going home is fraught with complications, but Savannah never imagined one of those would be Ryker Stone, the stranger she'd shared an unforgettable night with.Haunted by an ambush that took the lives of two men in his unit, Ryker now copes by living a solitary civilian life. Attending the funeral of yet another soldier, this one lost to a senseless accident, he is shocked to run into the beautiful stranger he has never forgotten.When another man in Ryker's former unit dies under suspicious circumstances, it's clear that someone is targeting his team. He's determined to get the truth; Savannah is just as determined to get answers for her friend. Neither wants to work with the other, and as they struggle with trust and attraction, the truth grows murkier...and more dangerous. Will finding answers reveal secrets neither one of them is ready to know? Don't miss this twisting, suspenseful, romantic page-turner by #1 New York Times Bestselling Author Barbara Freethy!
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Questioning Qualitative Inquiry : Critical Essays
Is qualitative research in crisis? In Questioning Qualitative Inquiry Martyn Hammersley raises fundamental questions about the current state of qualitative social research.He examines some of the changes that have taken place within it over the past fifty years, suggesting that the move away from natural science as a model, and towards an appeal to literature and art, involves rejection of key principles that are essential to research of any kind. Hammersley argues that, in important respects, qualitative inquiry has not lived up to the claims originally made on its behalf, and that more recent developments have worsened the situation.Insufficient attention has been given to the problems surrounding leading ideas like thick description, analytic induction, and constructionism.The argument is pursued through discussion of the work of influential writers - such as Clifford, Geertz, Denzin and Lincoln - and by detailed examination of concrete issues, like the value of interview data, the rationales for discourse and conversation analysis, the role of rhetoric in research reports, and the nature of assessment criteria.At a time when qualitative inquiry is coming under renewed challenge in some quarters, the task of addressing the methodological problems it faces has become urgent. These essays on current developments and debates are essential reading for anyone interested in the future of qualitative research.
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Semigroups of Linear Operators : With Applications to Analysis, Probability and Physics
The theory of semigroups of operators is one of the most important themes in modern analysis.Not only does it have great intellectual beauty, but also wide-ranging applications.In this book the author first presents the essential elements of the theory, introducing the notions of semigroup, generator and resolvent, and establishes the key theorems of Hille–Yosida and Lumer–Phillips that give conditions for a linear operator to generate a semigroup.He then presents a mixture of applications and further developments of the theory.This includes a description of how semigroups are used to solve parabolic partial differential equations, applications to Levy and Feller–Markov processes, Koopmanism in relation to dynamical systems, quantum dynamical semigroups, and applications to generalisations of the Riemann–Liouville fractional integral.Along the way the reader encounters several important ideas in modern analysis including Sobolev spaces, pseudo-differential operators and the Nash inequality.
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How does the function Linear Algebra Analysis work?
Linear Algebra Analysis is a mathematical tool that is used to analyze and solve systems of linear equations and study the properties of linear transformations. It involves the use of matrices, vectors, and linear transformations to understand the behavior of linear systems. By performing operations such as matrix multiplication, finding eigenvalues and eigenvectors, and solving systems of linear equations, Linear Algebra Analysis helps in understanding the relationships and patterns within linear systems. This function is widely used in various fields such as engineering, physics, computer science, and economics to model and solve real-world problems.
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Is it linear or non-linear?
The relationship between the variables is non-linear.
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Does the analysis require being good at linear algebra?
No, the analysis does not necessarily require being good at linear algebra. While linear algebra can be helpful in certain aspects of analysis, such as understanding matrix operations or solving systems of equations, it is not a strict requirement. Many analyses can be conducted using statistical software or other tools that automate complex calculations, allowing individuals with varying levels of mathematical proficiency to perform analyses effectively. Ultimately, a solid understanding of the underlying concepts and methodologies being used is more important than specific mathematical skills.
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What is your question about linear equations?
My question about linear equations is how to solve systems of linear equations using different methods such as substitution, elimination, and graphing. I also want to understand how to interpret the solutions of a system of linear equations in terms of the intersection points of the corresponding lines on a graph. Additionally, I am interested in learning about applications of linear equations in real-world scenarios, such as in business, economics, and engineering.
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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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Aegean Linear Script(s) : Rethinking the Relationship Between Linear A and Linear B
When does a continuum become a divide? This book investigates the genetic relationship between Linear A and Linear B, two Bronze Age scripts attested on Crete and Mainland Greece and understood to have developed one out of the other.By using an interdisciplinary methodology, this research integrates linguistic, epigraphic, palaeographic and archaeological evidence, and places the writing practice in its sociohistorical setting.By challenging traditional views, this work calls into question widespread assumptions and interpretative schemes on the relationship between these two scripts, and opens up new perspectives on the ideology associated with the retention, adaptation and transmission of a script, and how identity was negotiated at a moment of closer societal interaction between Cretans and Greek-speaking Mainlanders in the Late Bronze Age.By delving deeper into the structure and inner workings of these two writing systems, this book will make us rethink the relationship between Linear A and B.
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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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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.
Price: 43.99 £ | Shipping*: 0.00 £
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What is the difference between a linear analysis and an aspect-oriented analysis?
A linear analysis focuses on the flow of control and data through a program, examining the sequence of operations and their interactions. It is concerned with the overall structure and organization of the code. On the other hand, an aspect-oriented analysis focuses on identifying and modularizing cross-cutting concerns, such as logging, security, and error handling, which may be scattered across different parts of the code. It aims to separate these concerns from the main codebase, making the code more modular and easier to maintain. In essence, while linear analysis looks at the program's structure and flow, aspect-oriented analysis focuses on identifying and addressing cross-cutting concerns.
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Which is more difficult: Linear Algebra I or Analysis I?
The difficulty of Linear Algebra I versus Analysis I can vary depending on the individual's strengths and background. Generally, Analysis I is considered more difficult due to its focus on rigorous proofs and abstract concepts. It requires a strong foundation in mathematical reasoning and a deep understanding of calculus. On the other hand, Linear Algebra I is more computational and visual, making it more accessible to some students. However, both courses are fundamental and essential for a strong understanding of mathematics.
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How difficult are Analysis and Linear Algebra in the study?
Analysis and Linear Algebra can be challenging subjects in the study of mathematics. Both require a strong foundation in algebra and calculus, and they involve abstract concepts and rigorous proofs. However, with dedication and practice, students can develop a deep understanding of these subjects. It is important to seek help from professors, tutors, and peers when facing difficulties, and to approach the material with patience and persistence.
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What does linear and aspect-oriented mean in text analysis?
In text analysis, linear refers to the traditional approach of processing text data sequentially, where each step is executed in a linear fashion. On the other hand, aspect-oriented text analysis involves identifying and analyzing specific aspects or dimensions of the text data, such as sentiment, topic, or entity recognition. This approach allows for a more focused and targeted analysis of the text data, enabling deeper insights to be extracted from the text.
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