Products related to Linear:
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Data Analytics & Visualization All-in-One For Dummies
Install data analytics into your brain with this comprehensive introduction Data Analytics & Visualization All-in-One For Dummies collects the essential information on mining, organizing, and communicating data, all in one place.Clocking in at around 850 pages, this tome of a reference delivers eight books in one, so you can build a solid foundation of knowledge in data wrangling.Data analytics professionals are highly sought after these days, and this book will put you on the path to becoming one.You’ll learn all about sources of data like data lakes, and you’ll discover how to extract data using tools like Microsoft Power BI, organize the data in Microsoft Excel, and visually present the data in a way that makes sense using a Tableau.You’ll even get an intro to the Python, R, and SQL coding needed to take your data skills to a new level.With this Dummies guide, you’ll be well on your way to becoming a priceless data jockey. Mine data from data sourcesOrganize and analyze data Use data to tell a story with TableauExpand your know-how with Python and R New and novice data analysts will love this All-in-One reference on how to make sense of data.Get ready to watch as your career in data takes off.
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Spatial Analysis with R : Statistics, Visualization, and Computational Methods
In the five years since the publication of the first edition of Spatial Analysis: Statistics, Visualization, and Computational Methods, many new developments have taken shape regarding the implementation of new tools and methods for spatial analysis with R.The use and growth of artificial intelligence, machine learning and deep learning algorithms with a spatial perspective, and the interdisciplinary use of spatial analysis are all covered in this second edition along with traditional statistical methods and algorithms to provide a concept-based problem-solving learning approach to mastering practical spatial analysis.Spatial Analysis with R: Statistics, Visualization, and Computational Methods, Second Edition provides a balance between concepts and practicums of spatial statistics with a comprehensive coverage of the most important approaches to understand spatial data, analyze spatial relationships and patterns, and predict spatial processes.New in the Second Edition: Includes new practical exercises and worked-out examples using R Presents a wide range of hands-on spatial analysis worktables and lab exercises All chapters are revised and include new illustrations of different concepts using data from environmental and social sciences Expanded material on spatiotemporal methods, visual analytics methods, data science, and computational methods Explains big data, data management, and data miningThis second edition of an established textbook, with new datasets, insights, excellent illustrations, and numerous examples with R, is perfect for senior undergraduate and first-year graduate students in geography and the geosciences.
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Spatial Epidemiological Approaches in Disease Mapping and Analysis
Containing method descriptions and step-by-step procedures, the Spatial Epidemiological Approaches in Disease Mapping and Analysis equips readers with skills to prepare health-related data in the proper format, process these data using relevant functions and software, and display the results as mapped or statistical summaries.Describing the wide range of available methods and key GIS concepts for spatial epidemiology, this book illustrates the utilities of the software using real-world data.Additional topics include geographic data models, address matching, geostatistical analysis, universal kriging, point pattern analysis, kernel density, spatio-temporal display, and disease surveillance.
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Crime Mapping and Spatial Data Analysis using R
Crime mapping and analysis sit at the intersection of geocomputation, data visualisation and cartography, spatial statistics, environmental criminology, and crime analysis.This book brings together relevant knowledge from these fields into a practical, hands-on guide, providing a useful introduction and reference material for topics in crime mapping, the geography of crime, environmental criminology, and crime analysis.It can be used by students, practitioners, and academics alike, whether to develop a university course, to support further training and development, or to hone skills in self-teaching R and crime mapping and spatial data analysis.It is not an advanced statistics textbook, but rather an applied guide and later useful reference books, intended to be read and for readers to practice the learnings from each chapter in sequence.In the first part of this volume we introduce key concepts for geographic analysis and representation and provide the reader with the foundations needed to visualise spatial crime data.We then introduce a series of tools to study spatial homogeneity and dependence.A key focus in this section is how to visualise and detect local clusters of crime and repeat victimisation.The final chapters introduce the use of basic spatial models, which account for the distribution of crime across space.In terms of spatial data analysis the focus of the book is on spatial point pattern analysis and lattice or area data analysis.
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What is a linear mapping?
A linear mapping, also known as a linear transformation, is a function between two vector spaces that preserves the operations of vector addition and scalar multiplication. In other words, it maps a vector from one space to another in a way that maintains the structure of the vector space. Linear mappings are fundamental in linear algebra and are used to describe various mathematical concepts and relationships in a geometrically meaningful way. They play a crucial role in solving systems of linear equations, studying eigenvalues and eigenvectors, and understanding the properties of matrices.
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To what extent does this sentence make sense: Linear algebra, linear mapping?
The sentence "Linear algebra, linear mapping" makes sense to a certain extent. It suggests a connection between linear algebra and linear mapping, both of which are fundamental concepts in mathematics. Linear algebra deals with vector spaces, linear transformations, and systems of linear equations, while linear mapping refers to the process of transforming one vector space into another in a linear manner. Therefore, the sentence hints at the relationship between these two concepts within the broader field of mathematics.
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Are linear functions a mapping or not?
Yes, linear functions are a type of mapping. A linear function is a mathematical relationship between two variables that can be represented by a straight line on a graph. Each input value (x) is mapped to a unique output value (y) based on a specific equation. This mapping allows us to predict the output value for any given input value within the defined range of the function.
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What does the notation linear mapping of polynomials mean?
The notation "linear mapping of polynomials" refers to a function that takes a polynomial as input and produces another polynomial as output, while satisfying the properties of linearity. This means that the function preserves addition and scalar multiplication, i.e., f(a*p(x) + b*q(x)) = a*f(p(x)) + b*f(q(x)), where p(x) and q(x) are polynomials, and a and b are scalars. In other words, a linear mapping of polynomials is a function that behaves like a linear transformation on the space of polynomials.
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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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1~10PCS LM10UU LM16UU LM12UU Linear Bearing Linear Bushing 8mm Rods Linear Rail Linear Shaft Parts CNC 3D Printer
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An Introduction to R for Spatial Analysis and Mapping
The ever-expanding availability of spatial data continues to revolutionise research.This book is your go-to guide to getting the most out of handling, mapping and analysing location-based data. Without assuming prior knowledge of GIS, geocomputation or R, this book helps you understand spatial analysis and mapping and develop your programming skills, from learning about scripting and writing functions to point pattern analysis and spatial attribute analysis. The book:Illustrates approaches to analysis on a range of datasets that are new to this edition. Enables you to put your skills into practice with embedded exercises and over 30 self-test questions. Showcases the possibilities of using spatial analysis to explore spatial inequalities. Whether you’re an R novice or experienced user, this book equips upper undergraduates, postgraduates and researchers with the tools needed for spatial data handling and rich analysis.
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What is spatial visualization ability?
Spatial visualization ability refers to the capacity to mentally manipulate and comprehend spatial relationships between objects. Individuals with strong spatial visualization skills can easily visualize and understand how objects relate to each other in space, such as rotating or manipulating shapes in their mind. This ability is crucial in various fields such as engineering, architecture, and mathematics, as it allows individuals to solve complex problems and understand spatial concepts more effectively. Improving spatial visualization ability can enhance problem-solving skills and overall cognitive performance.
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How can one define a linear mapping with three vectors?
A linear mapping with three vectors can be defined as a function that takes three vectors from one vector space and maps them to three vectors in another vector space, while preserving the properties of linearity. In other words, for a linear mapping to be defined with three vectors, it must satisfy the properties of additivity and homogeneity. Additivity means that the mapping of the sum of two vectors is equal to the sum of their individual mappings, and homogeneity means that the mapping of a scalar multiple of a vector is equal to the scalar multiple of the vector's mapping. These properties ensure that the linear mapping preserves the structure of the vector space.
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Is spatial visualization important for engineers?
Yes, spatial visualization is important for engineers as it allows them to mentally manipulate and understand complex 3D objects and structures. Engineers often need to design and analyze various components and systems, and spatial visualization skills help them to conceptualize and communicate their ideas effectively. Whether it's designing a new product, creating blueprints for a building, or solving complex problems, spatial visualization is a crucial skill that allows engineers to think critically and innovate in their field.
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How do you determine the mapping rule of a linear function?
To determine the mapping rule of a linear function, you need to identify the slope and the y-intercept of the function. The slope is the rate at which the function is increasing or decreasing, and it can be calculated by finding the change in y divided by the change in x for any two points on the line. The y-intercept is the point where the line intersects the y-axis, and it can be found by setting x=0 and solving for y. Once you have the slope and y-intercept, you can write the mapping rule in the form y = mx + b, where m is the slope and b is the y-intercept.
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