5.641 results (0,25132 seconds)

Brand

Merchant

Price (EUR)

Reset filter

Products
From
Shops

Regression Modeling Methods Theory and Computation with SAS

Computational Statistics Handbook with MATLAB

An Introduction to Scientific Computing with MATLAB and Python Tutorials

Concise Introduction to Linear Algebra

Univariate and Multivariate General Linear Models Theory and Applications with SAS Second Edition

Univariate and Multivariate General Linear Models Theory and Applications with SAS Second Edition

Reviewing the theory of the general linear model (GLM) using a general framework Univariate and Multivariate General Linear Models: Theory and Applications with SAS Second Edition presents analyses of simple and complex models both univariate and multivariate that employ data sets from a variety of disciplines such as the social and behavioral sciences. With revised examples that include options available using SAS 9. 0 this expanded edition divides theory from applications within each chapter. Following an overview of the GLM the book introduces unrestricted GLMs to analyze multiple regression and ANOVA designs as well as restricted GLMs to study ANCOVA designs and repeated measurement designs. Extensions of these concepts include GLMs with heteroscedastic errors that encompass weighted least squares regression and categorical data analysis and multivariate GLMs that cover multivariate regression analysis MANOVA MANCOVA and repeated measurement data analyses. The book also analyzes double multivariate linear growth curve seeming unrelated regression (SUR) restricted GMANOVA and hierarchical linear models. New to the Second EditionTwo chapters on finite intersection tests and power analysis that illustrates the experimental GLMPOWER procedureExpanded theory of unrestricted general linear multivariate general linear SUR and restricted GMANOVA models to comprise recent developments Expanded material on missing data to include multiple imputation and the EM algorithmApplications of MI MIANALYZE TRANSREG and CALIS proceduresA practical introduction to GLMs Univariate and Multivariate General Linear Models demonstrates how to fully grasp the generality of GLMs by discussing them within a general framework. | Univariate and Multivariate General Linear Models Theory and Applications with SAS Second Edition

GBP 56.99
1

Signals and Systems A Primer with MATLAB

Signals and Systems A Primer with MATLAB

Signals and Systems: A Primer with MATLAB® provides clear interesting and easy-to-understand coverage of continuous-time and discrete-time signals and systems. Each chapter opens with a historical profile or career talk followed by an introduction that states the chapter objectives and links the chapter to the previous ones. All principles are presented in a lucid logical step-by-step approach. As much as possible the authors avoid wordiness and detail overload that could hide concepts and impede understanding. In recognition of the requirements by the Accreditation Board for Engineering and Technology (ABET) on integrating computer tools the use of MATLAB® is encouraged in a student-friendly manner. MATLAB is introduced in Appendix B and applied gradually throughout the book. Each illustrative example is immediately followed by a practice problem along with its answer. Students can follow the example step by step to solve the practice problem without flipping pages or looking at the end of the book for answers. These practice problems test students’ comprehension and reinforce key concepts before moving on to the next section. Toward the end of each chapter the authors discuss some application aspects of the concepts covered in the chapter. The material covered in the chapter is applied to at least one or two practical problems or devices. This helps students see how the concepts are applied to real-life situations. In addition thoroughly worked examples are given liberally at the end of every section. These examples give students a solid grasp of the solutions as well as the confidence to solve similar problems themselves. Some of the problems are solved in two or three ways to facilitate a deeper understanding and comparison of different approaches. Ten review questions in the form of multiple-choice objective items are provided at the end of each chapter with answers. The review questions are intended to cover the little tricks that the examples and end-of-chapter problems may not cover. They serve as a self-test device and help students determine chapter mastery. Each chapter also ends with a summary of key points and formulas. Designed for a three-hour semester course on signals and systems Signals and Systems: A Primer with MATLAB® is intended as a textbook for junior-level undergraduate students in electrical and computer engineering. The prerequisites for a course based on this book are knowledge of standard mathematics (including calculus and differential equations) and electric circuit analysis. | Signals and Systems A Primer with MATLAB®

GBP 44.99
1

Geographic Data Science with R Visualizing and Analyzing Environmental Change

Geographic Data Science with R Visualizing and Analyzing Environmental Change

The burgeoning field of data science has provided a wealth of techniques for analysing large and complex geospatial datasets including descriptive explanatory and predictive analytics. However applying these methods is just one part of the overall process of geographic data science. Other critical steps include screening for suspect data values handling missing data harmonizing data from multiple sources summarizing the data and visualizing data and analysis results. Although there are many books available on statistical and machine learning methods few encompass the broader topic of scientific workflows for geospatial data processing and analysis. The purpose of Geographic Data Science with R is to fill this gap by providing a series of tutorials aimed at teaching good practices for using geospatial data to address problems in environmental geography. It is based on the R language and environment which currently provides the best option for working with diverse spatial and non-spatial data in a single platform. Fundamental techniques for processing and visualizing tabular vector and raster data are introduced through a series of practical examples followed by case studies that combine multiple types of data to address more complex problems. The book will have a broad audience. Both students and professionals can use it as a workbook to learn high-level techniques for geospatial data processing and analysis with R. It is also suitable as a textbook. Although not intended to provide a comprehensive introduction to R it is designed to be accessible to readers who have at least some knowledge of coding but little to no experience with R. Key Features: Focus on developing practical workflows for processing and integrating multiple sources of geospatial data in R Example-based approach that teaches R programming and data science concepts through real-world applications related to climate land cover and land use and natural hazards. Consistent use of tidyverse packages for tabular data manipulation and visualization. Strong focus on analysing continuous and categorical raster datasets using the new terra package Organized so that each chapter builds on the topics and techniques covered in the preceding chapters Can be used for self-study or as the textbook for a geospatial science course. | Geographic Data Science with R Visualizing and Analyzing Environmental Change

GBP 74.99
1

Handbook of Conformal Mappings and Applications

Principles and Practice of Structural Equation Modeling Fifth Edition

Principles and Practice of Structural Equation Modeling Fifth Edition

Significantly revised the fifth edition of the most complete accessible text now covers all three approaches to structural equation modeling (SEM)-covariance-based SEM nonparametric SEM (Pearl’s structural causal model) and composite SEM (partial least squares path modeling). With increased emphasis on freely available software tools such as the R lavaan package the text uses data examples from multiple disciplines to provide a comprehensive understanding of all phases of SEM-what to know best practices and pitfalls to avoid. It includes exercises with answers rules to remember topic boxes and new self-tests on significance testing regression and psychometrics. The companion website supplies helpful primers on these topics as well as data syntax and output for the book's examples in files that can be opened with any basic text editor. New to This Edition *Chapters on composite SEM also called partial least squares path modeling or variance-based SEM; conducting SEM analyses in small samples; and recent developments in mediation analysis. *Coverage of new reporting standards for SEM analyses; piecewise SEM also called confirmatory path analysis; comparing alternative models fitted to the same data; and issues in multiple-group SEM. *Extended tutorials on techniques for dealing with missing data in SEM and instrumental variable methods to deal with confounding of target causal effects. Pedagogical Features *New self-tests of knowledge about background topics (significance testing regression and psychometrics) with scoring key and online primers. *End-of-chapter suggestions for further reading and exercises with answers. *Troublesome examples from real data with guidance for handling typical problems in analyses. *Topic boxes on special issues and boxed rules to remember. *Website promoting a learn-by-doing approach including data extensively annotated syntax and output files for all the book’s detailed examples. | Principles and Practice of Structural Equation Modeling Fifth Edition

GBP 62.99
1

Global Research Ethics Case Studies from International Research Contexts

Global Research Ethics Case Studies from International Research Contexts

Global Research Ethics is a guide for students and their instructors as well as practitioners and researchers to understand topics linked to research ethics from a more global perspective. Research plays a key role in identifying health disparity trends and evaluating interventions to improve the health and well-being of the populations at the individual local national and global levels. Conducting ethically sound research is imperative in these contexts. This book (a) uses case studies to offer examples of current research ethical dilemmas and (b) considers regulatory and cultural frameworks in a number of country contexts that highlight diverse methods of identifying and managing these ethical dilemmas. Chapters cover different types (groups) of participants issues in research and ways of doing research; then each chapter looks at least three exemplar case studies with at least two analytical commentaries. Case studies include health and social care research and originate from countries such as Brazil Chile South Africa Botswana Australia and New Zealand as well as the U. S. and U. K. The different viewpoints showcased will allow for dialogue to ensue about the ways in which populations and topics in research need to be conceptualized. Global Research Ethics is suitable for all undergraduates and postgraduates on research methods courses in the social and health sciences. It provides academic researchers students and community partners with guidelines to reflect on as they develop their own research studies. | Global Research Ethics Case Studies from International Research Contexts

GBP 44.99
1

Learn Audio Electronics with Arduino Practical Audio Circuits with Arduino Control

Learn Audio Electronics with Arduino Practical Audio Circuits with Arduino Control

Learn Audio Electronics with Arduino: Practical Audio Circuits with Arduino Control teaches the reader how to use Arduino to control analogue audio circuits and introduces electronic circuit theory through a series of practical projects including a MIDI drum controller and an Arduino-controlled two-band audio equalizer amplifier. Learn Audio Electronics with Arduino provides all the theoretical knowledge needed to design analyse and build audio circuits for amplification and filtering with additional topics like C programming being introduced in a practical context for Arduino control. The reader will learn how these circuits work and also how to build them allowing them to progress to more advanced audio circuits in the future. Beginning with electrical fundamentals and control systems DC circuit theory is then combined with an introduction to C programming to build Arduino-based systems for audio (tone sequencer) and MIDI (drum controller) output. The second half of the book begins with AC circuit theory to allow analogue audio circuits for amplification and filtering to be analysed simulated and built. These circuits are then combined with Arduino control in the final project – an Arduino-controlled two-band equalizer amplifier. Building on high-school physics and mathematics in an accessible way Learn Audio Electronics with Arduino is suitable for readers of all levels. An ideal tool for those studying audio electronics including as a component within other fields of study such as computer science human-computer interaction acoustics music technology and electronics engineering. | Learn Audio Electronics with Arduino Practical Audio Circuits with Arduino Control

GBP 44.99
1

Jehovah's Witnesses Continuity and Change

Speaking with a Purpose

The Shadow Cabinet in British Politics

Linear Models with Python

Linear Models with Python

Praise for Linear Models with R: This book is a must-have tool for anyone interested in understanding and applying linear models. The logical ordering of the chapters is well thought out and portrays Faraway’s wealth of experience in teaching and using linear models. … It lays down the material in a logical and intricate manner and makes linear modeling appealing to researchers from virtually all fields of study. Biometrical Journal Throughout it gives plenty of insight … with comments that even the seasoned practitioner will appreciate. Interspersed with R code and the output that it produces one can find many little gems of what I think is sound statistical advice well epitomized with the examples chosen…I read it with delight and think that the same will be true with anyone who is engaged in the use or teaching of linear models. Journal of the Royal Statistical Society Like its widely praised best-selling companion version Linear Models with R this book replaces R with Python to seamlessly give a coherent exposition of the practice of linear modeling. Linear Models with Python offers up-to-date insight on essential data analysis topics from estimation inference and prediction to missing data factorial models and block designs. Numerous examples illustrate how to apply the different methods using Python. Features: Python is a powerful open source programming language increasingly being used in data science machine learning and computer science. Python and R are similar but R was designed for statistics while Python is multi-talented. This version replaces R with Python to make it accessible to a greater number of users outside of statistics including those from Machine Learning. A reader coming to this book from an ML background will learn new statistical perspectives on learning from data. Topics include Model Selection Shrinkage Experiments with Blocks and Missing Data. Includes an Appendix on Python for beginners. Linear Models with Python explains how to use linear models in physical science engineering social science and business applications. It is ideal as a textbook for linear models or linear regression courses.

GBP 82.99
1

Geocomputation with R

Geocomputation with R

Geocomputation with R is for people who want to analyze visualize and model geographic data with open source software. It is based on R a statistical programming language that has powerful data processing visualization and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data including those with scientific societal and environmental implications. This book will interest people from many backgrounds especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science and R users interested in extending their skills to handle spatial data. The book is divided into three parts: (I) Foundations aimed at getting you up-to-speed with geographic data in R (II) extensions which covers advanced techniques and (III) applications to real-world problems. The chapters cover progressively more advanced topics with early chapters providing strong foundations on which the later chapters build. Part I describes the nature of spatial datasets in R and methods for manipulating them. It also covers geographic data import/export and transforming coordinate reference systems. Part II represents methods that build on these foundations. It covers advanced map making (including web mapping) bridges to GIS sharing reproducible code and how to do cross-validation in the presence of spatial autocorrelation. Part III applies the knowledge gained to tackle real-world problems including representing and modeling transport systems finding optimal locations for stores or services and ecological modeling. Exercises at the end of each chapter give you the skills needed to tackle a range of geospatial problems. Solutions for each chapter and supplementary materials providing extended examples are available at https://geocompr. github. io/geocompkg/articles/. Dr. Robin Lovelace is a University Academic Fellow at the University of Leeds where he has taught R for geographic research over many years with a focus on transport systems. Dr. Jakub Nowosad is an Assistant Professor in the Department of Geoinformation at the Adam Mickiewicz University in Poznan where his focus is on the analysis of large datasets to understand environmental processes. Dr. Jannes Muenchow is a Postdoctoral Researcher in the GIScience Department at the University of Jena where he develops and teaches a range of geographic methods with a focus on ecological modeling statistical geocomputing and predictive mapping. All three are active developers and work on a number of R packages including stplanr sabre and RQGIS.

GBP 44.99
1

Early Childhood Intervention Working with Families of Young Children with Special Needs

Early Childhood Intervention Working with Families of Young Children with Special Needs

Early childhood is considered a critical but often vulnerable period in a child’s development where early identification and intervention can be crucial for improving children’s developmental outcomes. Systems and family-centred perspectives are vital to support families and build their capacities to lead normalized lives with improved family quality of life. This book explores the family-centred practices and systems factors which influence families’ experiences raising children with complex needs. It also considers the ways in which professionals can work with families to build and support parent and child competence. Conceptual and practical work from Australia Canada Europe and the United States present descriptions of and implications for different family system frameworks and early-childhood programs. Contributors in this edited volume bring together contemporary information that bridges the research to practice gap in supporting families of young children with disabilities or delays. Chapters include: Early Intervention for Young Children with Developmental Delays: Contributions of the Developmental Systems Approach Family Composition and Family Needs in Australia: What Makes a Family? Working with Families in Early Childhood Intervention: Family-Centred Practices in an Individualised Funding Landscape Family Systems and Family-Centred Intervention Practices in Portugal and Spain: Iberian Reflections on Early Childhood Intervention This book will attract the attention scholars of Parenting and Families; Child Development and Childcare. | Early Childhood Intervention Working with Families of Young Children with Special Needs

GBP 44.99
1

Data Mining with R Learning with Case Studies Second Edition

Data Mining with R Learning with Case Studies Second Edition

Data Mining with R: Learning with Case Studies Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition this new edition is divided into two parts. The first part will feature introductory material including a new chapter that provides an introduction to data mining to complement the already existing introduction to R. The second part includes case studies and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R. The book does not assume any prior knowledge about R. Readers who are new to R and data mining should be able to follow the case studies and they are designed to be self-contained so the reader can start anywhere in the document. The book is accompanied by a set of freely available R source files that can be obtained at the book’s web site. These files include all the code used in the case studies and they facilitate the do-it-yourself approach followed in the book. Designed for users of data analysis tools as well as researchers and developers the book should be useful for anyone interested in entering the world of R and data mining. About the AuthorLuís Torgo is an associate professor in the Department of Computer Science at the University of Porto in Portugal. He teaches Data Mining in R in the NYU Stern School of Business’ MS in Business Analytics program. An active researcher in machine learning and data mining for more than 20 years Dr. Torgo is also a researcher in the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) of INESC Porto LA. | Data Mining with R Learning with Case Studies Second Edition

GBP 44.99
1

Cyclic and Computer Generated Designs

Composite-Based Structural Equation Modeling Analyzing Latent and Emergent Variables

Telling Stories with Data With Applications in R

Telling Stories with Data With Applications in R

The book equips students with the end-to-end skills needed to do data science. That means gathering cleaning preparing and sharing data then using statistical models to analyse data writing about the results of those models drawing conclusions from them and finally using the cloud to put a model into production all done in a reproducible way. At the moment there are a lot of books that teach data science but most of them assume that you already have the data. This book fills that gap by detailing how to go about gathering datasets cleaning and preparing them before analysing them. There are also a lot of books that teach statistical modelling but few of them teach how to communicate the results of the models and how they help us learn about the world. Very few data science textbooks cover ethics and most of those that do have a token ethics chapter. Finally reproducibility is not often emphasised in data science books. This book is based around a straight-forward workflow conducted in an ethical and reproducible way: gather data prepare data analyse data and communicate those findings. This book will achieve the goals by working through extensive case studies in terms of gathering and preparing data and integrating ethics throughout. It is specifically designed around teaching how to write about the data and models so aspects such as writing are explicitly covered. And finally the use of GitHub and the open-source statistical language R are built in throughout the book. Key Features: Extensive code examples. Ethics integrated throughout. Reproducibility integrated throughout. Focus on data gathering messy data and cleaning data. Extensive formative assessment throughout. | Telling Stories with Data With Applications in R

GBP 74.99
1

Charged Particle and Photon Interactions with Matter Chemical Physicochemical and Biological Consequences with Applications

The Human Sustainable City Challenges and Perspectives from the Habitat Agenda

Creative Storytelling with Children at Risk