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Introduction to Survey Research, Polling, and Data Analysis
"The writing style is mature and experienced. There is noticeable care in the presentation of materials and appropriate sensitivity to the intended audience. . . . The book categorizes large bodies of wisdom in the conduct of survey research over the past half century and provides ′′rules-of-thumb′′ guidance to the production, analysis, and consumption of survey data." --Steve Seitz, University of Illinois "The authors have provided a well-written update of their textbook, which will be useful as a textbook or supplement in upper-division undergraduate or introductory graduate courses on survey research, statistical data analysis, or research design. The book provides readers with the knowledge that enables them to evaluate the results of survey research. It sensitizes the readers to potential problems that arise from flaws in research design, sampling, questionnaire construction, data collection, coding, data analysis techniques, and improper presentation or interpretation of findings." --Mike Margolis, University of Cincinnati Why should we believe the results of surveys based on relatively few interviews? What are the appropriate cautions in interpreting survey reports? Aimed at answering these questions, An Introduction to Survey Research, Polling, and Data Analysis, Third Edition describes how surveys are conducted in such a way that they can be believed, explains how to read statistical reports and analyze data, and provides guidelines that are useful in evaluating polls. Using frequent examples from contemporary, large national surveys and polls as well as from the media, the authors stress understanding tables thoroughly before moving to interval statistics. In addition, they cover survey design, sampling and question-writing steps, interviewing and coding strategies, and survey analysis from frequency distributions and cross-tabulations to control tables and correlation/regression. They also cover the ethics of survey research and how to read and write survey research reports. Readers of this book will learn to distinguish good from bad polls and to better design and conduct their own surveys and polls.
R 4,937.57
Applied Reinforcement Learning with Python
Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. This book covers important topics such as policy gradients and Q learning, and utilizes frameworks such as Tensorflow, Keras, and OpenAI Gym. Applied Reinforcement Learning with Python introduces you to the theory behind reinforcement learning (RL) algorithms and the code that will be used to implement them. You will take a guided tour through features of OpenAI Gym, from utilizing standard libraries to creating your own environments, then discover how to frame reinforcement learning problems so you can research, develop, and deploy RL-based solutions.What You'll LearnImplement reinforcement learning with Python Work with AI frameworks such as OpenAI Gym, Tensorflow, and KerasDeploy and train reinforcement learning–based solutions via cloud resourcesApply practical applications of reinforcement learning Who This Book Is For Data scientists, machine learning engineers and software engineers familiar with machine learning and deep learning concepts.
R 1,079.73
Hands-On Image Processing with Python
Explore the mathematical computations and algorithms for image processing using popular Python tools and frameworks.Key FeaturesPractical coverage of every image processing task with popular Python librariesIncludes topics such as pseudo-coloring, noise smoothing, computing image descriptorsCovers popular machine learning and deep learning techniques for complex image processing tasksBook DescriptionImage processing plays an important role in our daily lives with various applications such as in social media (face detection), medical imaging (X-ray, CT-scan), security (fingerprint recognition) to robotics & space. This book will touch the core of image processing, from concepts to code using Python.The book will start from the classical image processing techniques and explore the evolution of image processing algorithms up to the recent advances in image processing or computer vision with deep learning. We will learn how to use image processing libraries such as PIL, scikit-mage, and scipy ndimage in Python. This book will enable us to write code snippets in Python 3 and quickly implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. We will be able to use machine learning models using the scikit-learn library and later explore deep CNN, such as VGG-19 with Keras, and we will also use an end-to-end deep learning model called YOLO for object detection. We will also cover a few advanced problems, such as image inpainting, gradient blending, variational denoising, seam carving, quilting, and morphing.By the end of this book, we will have learned to implement various algorithms for efficient image processing.What you will learnPerform basic data pre-processing tasks such as image denoising and spatial filtering in PythonImplement Fast Fourier Transform (FFT) and Frequency domain filters (e.g., Weiner) in PythonDo morphological image processing and segment images with different algorithmsLearn techniques to extract features from images and match imagesWrite Python code to implement supervised / unsupervised machine learning algorithms for image processingUse deep learning models for image classification, segmentation, object detection and style transferWho this book is forThis book is for Computer Vision Engineers, and machine learning developers who are good with Python programming and want to explore details and complexities of image processing. No prior knowledge of the image processing techniques is expected.
R 1,661.00
Python for Mathematics
Python for Mathematics introduces readers to effective methods for doing mathematics using the Python programming language. Most programming texts introduce readers to the building blocks of programming and build up to using more sophisticated tools for a specific purpose, like doing particular mathematical tasks. This is akin to teaching someone how to forge metal so as to make a nail, and then slowly working up to using sophisticated power tools so as to actually build something. This book does things in a different way, by first getting readers to begin using and understanding the tools that are going to be helpful to them as mathematicians, and only then moving onto the granular details. In this way, the practical application of the tools can aid in the understanding of the theory.Features· Complete with engaging, practical exercises· Many useful and detailed coding examples· Suitable for undergraduates in mathematics, and other quantitative sciences· Empowers readers to design and create their own Python tools.
R 2,711.00
Applied Categorical and Count Data Analysis
Developed from the authors’ graduate-level biostatistics course, Applied Categorical and Count Data Analysis, Second Edition explains how to perform the statistical analysis of discrete data, including categorical and count outcomes. The authors have been teaching categorical data analysis courses at the University of Rochester and Tulane University for more than a decade. This book embodies their decade-long experience and insight in teaching and applying statistical models for categorical and count data. The authors describe the basic ideas underlying each concept, model, and approach to give readers a good grasp of the fundamentals of the methodology without relying on rigorous mathematical arguments.The second edition covers classic concepts and popular topics, such as contingency tables, logistic regression models, and Poisson regression models, along with modern areas that include models for zero-modified count outcomes, parametric and semiparametric longitudinal data analysis, reliability analysis, and methods for dealing with missing values. As in the first edition, R, SAS, SPSS, and Stata programming codes are provided for all the examples, enabling readers to immediately experiment with the data in the examples and even adapt or extend the codes to fit data from their own studies. Designed for a one-semester course for graduate and senior undergraduate students in biostatistics, this self-contained text is also suitable as a self-learning guide for biomedical and psychosocial researchers. It will help readers analyze data with discrete variables in a wide range of biomedical and psychosocial research fields.Features:Describes the basic ideas underlying each concept and modelIncludes R, SAS, SPSS and Stata programming codes for all the examplesFeatures significantly expanded Chapters 4, 5, and 8 (Chapters 4-6, and 9 in the second editionExpands discussion for subtle issues in longitudinal and clustered data analysis such as time varying covariates and comparison of generalized linear mixed-effect models with GEE
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Seriation in Combinatorial and Statistical Data Analysis
This monograph offers an original broad and very diverse exploration of the seriation domain in data analysis, together with building a specific relation to clustering.Relative to a data table crossing a set of objects and a set of descriptive attributes, the search for orders which correspond respectively to these two sets is formalized mathematically and statistically.State-of-the-art methods are created and compared with classical methods and a thorough understanding of the mutual relationships between these methods is clearly expressed. The authors distinguish two families of methods:Geometric representation methodsAlgorithmic and Combinatorial methodsOriginal and accurate methods are provided in the framework for both families. Their basis and comparison is made on both theoretical and experimental levels. The experimental analysis is very varied and very comprehensive. Seriation in Combinatorial and Statistical Data Analysis has a unique character in the literature falling within the fields of Data Analysis, Data Mining and Knowledge Discovery. It will be a valuable resource for students and researchers in the latter fields.
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Intelligent Techniques for Data Analysis in Diverse Settings
Data analysis forms the basis of many forms of research ranging from the scientific to the governmental. With the advent of machine intelligence and neural networks, extracting, modeling, and approaching data has been unimpeachably altered. These changes, seemingly small, affect the way societies organize themselves, deliver services, or interact with each other.Intelligent Techniques for Data Analysis in Diverse Settings addresses the specialized requirements of data analysis in a comprehensive way. This title contains a comprehensive overview of the most innovative recent approaches borne from intelligent techniques such as neural networks, rough sets, fuzzy sets, and metaheuristics. Combining new data analysis technologies, applications, emerging trends, and case studies, this publication reviews the intelligent, technological, and organizational aspects of the field. This book is ideally designed for IT professionals and students, data analysis specialists, healthcare providers, and policy makers.
R 7,571.49
Handbook of Neuroimaging Data Analysis
This book explores various state-of-the-art aspects behind the statistical analysis of neuroimaging data. It examines the development of novel statistical approaches to model brain data. Designed for researchers in statistics, biostatistics, computer science, cognitive science, computer engineering, biomedical engineering, applied mathematics, physics, and radiology, the book can also be used as a textbook for graduate-level courses in statistics and biostatistics or as a self-study reference for Ph.D. students in statistics, biostatistics, psychology, neuroscience, and computer science.
R 10,257.00
Multicriteria Portfolio Construction with Python
This book covers topics in portfolio management and multicriteria decision analysis (MCDA), presenting a transparent and unified methodology for the portfolio construction process. The most important feature of the book includes the proposed methodological framework that integrates two individual subsystems, the portfolio selection subsystem and the portfolio optimization subsystem. An additional highlight of the book includes the detailed, step-by-step implementation of the proposed multicriteria algorithms in Python. The implementation is presented in detail; each step is elaborately described, from the input of the data to the extraction of the results. Algorithms are organized into small cells of code, accompanied by targeted remarks and comments, in order to help the reader to fully understand their mechanics. Readers are provided with a link to access the source code through GitHub.This Work may also be considered as a reference which presents the state-of-art research on portfolio construction with multiple and complex investment objectives and constraints. The book consists of eight chapters. A brief introduction is provided in Chapter 1. The fundamental issues of modern portfolio theory are discussed in Chapter 2. In Chapter 3, the various multicriteria decision aid methods, either discrete or continuous, are concisely described. In Chapter 4, a comprehensive review of the published literature in the field of multicriteria portfolio management is considered. In Chapter 5, an integrated and original multicriteria portfolio construction methodology is developed. Chapter 6 presents the web-based information system, in which the suggested methodological framework has been implemented. In Chapter 7, the experimental application of the proposed methodology is discussed and in Chapter 8, the authors provide overall conclusions.The readership of the book aims to be a diverse group, including fund managers, risk managers, investment advisors, bankers, private investors, analytics scientists, operations researchers scientists, and computer engineers, to name just several. Portions of the book may be used as instructional for either advanced undergraduate or post-graduate courses in investment analysis, portfolio engineering, decision science, computer science, or financial engineering.
R 4,170.57
Data Analysis in the Cloud
Data Analysis in the Cloud introduces and discusses models, methods, techniques, and systems to analyze the large number of digital data sources available on the Internet using the computing and storage facilities of the cloud. Coverage includes scalable data mining and knowledge discovery techniques together with cloud computing concepts, models, and systems. Specific sections focus on map-reduce and NoSQL models. The book also includes techniques for conducting high-performance distributed analysis of large data on clouds. Finally, the book examines research trends such as Big Data pervasive computing, data-intensive exascale computing, and massive social network analysis.
R 1,176.00
Signal Processing and Data Analysis
This book presents digital signal processing theories and methods and their applications in data analysis, error analysis and statistical signal processing. Algorithms and Matlab programming are included to guide readers step by step in dealing with practical difficulties. Designed in a self-contained way, the book is suitable for graduate students in electrical engineering, information science and engineering in general.
R 1,847.00
Design and Analysis of Experiments with SAS
A culmination of the author’s many years of consulting and teaching, Design and Analysis of Experiments with SAS provides practical guidance on the computer analysis of experimental data. It connects the objectives of research to the type of experimental design required, describes the actual process of creating the design and collecting the data, shows how to perform the proper analysis of the data, and illustrates the interpretation of results.Drawing on a variety of application areas, from pharmaceuticals to machinery, the book presents numerous examples of experiments and exercises that enable students to perform their own experiments. Harnessing the capabilities of SAS 9.2, it includes examples of SAS data step programming and IML, along with procedures from SAS Stat, SAS QC, and SAS OR. The text also shows how to display experimental results graphically using SAS ODS graphics. The author emphasizes how the sample size, the assignment of experimental units to combinations of treatment factor levels (error control), and the selection of treatment factor combinations (treatment design) affect the resulting variance and bias of estimates as well as the validity of conclusions.This textbook covers both classical ideas in experimental design and the latest research topics. It clearly discusses the objectives of a research project that lead to an appropriate design choice, the practical aspects of creating a design and performing experiments, and the interpretation of the results of computer data analysis. SAS code and ancillaries are available at http://lawson.mooo.com
R 5,890.70
Bayesian Data Analysis for the Behavioral and Neural Sciences
This textbook bypasses the need for advanced mathematics by providing in-text computer code, allowing students to explore Bayesian data analysis without the calculus background normally considered a prerequisite for this material. Now, students can use the best methods without needing advanced mathematical techniques. This approach goes beyond “frequentist” concepts of p-values and null hypothesis testing, using the full power of modern probability theory to solve real-world problems. The book offers a fully self-contained course, which demonstrates analysis techniques throughout with worked examples crafted specifically for students in the behavioral and neural sciences. The book presents two general algorithms that help students solve the measurement and model selection (also called “hypothesis testing”) problems most frequently encountered in real-world applications.
R 2,939.00
Statistical Analysis of Human Growth and Development
Statistical Analysis of Human Growth and Development is an accessible and practical guide to a wide range of basic and advanced statistical methods that are useful for studying human growth and development. Designed for nonstatisticians and statisticians new to the analysis of growth and development data, the book collects methods scattered throughout the literature and explains how to use them to solve common research problems. It also discusses how well a method addresses a specific scientific question and how to interpret and present the analytic results. Stata is used to implement the analyses, with Stata codes and macros for generating example data sets, a detrended Q-Q plot, and weighted maximum likelihood estimation of binary items available on the book’s CRC Press web page.After reviewing research designs and basic statistical tools, the author discusses the use of existing tools to transform raw data into analyzable variables and back-transform them to raw data. He covers regression analysis of quantitative, binary, and censored data as well as the analysis of repeated measurements and clustered data. He also describes the development of new growth references and developmental indices, the generation of key variables based on longitudinal data, and the processes to verify the validity and reliability of measurement tools. Looking at the larger picture of research practice, the book concludes with coverage of missing values, multiplicity problems, and multivariable regression.Along with two simulated data sets, numerous examples from real experimental and observational studies illustrate the concepts and methods. Although the book focuses on examples of anthropometric measurements and changes in cognitive, social-emotional, locomotor, and other abilities, the ideas are applicable to many other physical and psychosocial phenomena, such as lung function and depressive symptoms.
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Mathematical Foundations for Data Analysis
This textbook, suitable for an early undergraduate up to a graduate course, provides an overview of many basic principles and techniques needed for modern data analysis. In particular, this book was designed and written as preparation for students planning to take rigorous Machine Learning and Data Mining courses. It introduces key conceptual tools necessary for data analysis, including concentration of measure and PAC bounds, cross validation, gradient descent, and principal component analysis. It also surveys basic techniques in supervised (regression and classification) and unsupervised learning (dimensionality reduction and clustering) through an accessible, simplified presentation. Students are recommended to have some background in calculus, probability, and linear algebra. Some familiarity with programming and algorithms is useful to understand advanced topics on computational techniques.
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Secondary Data Analysis
In recent decades, social work and other social science research disciplines have become increasingly reliant on large secondary data sets, which have increased in both number and accessibility. When starting a new research project, how does one determine whether to use a secondary data set? Which of the thousands available should be used? This invaluable and expertly written guide provides an in-depth introduction to 29 of the most widely used data sets in socialwork, such as the Early Childhood Longitudinal Study, the National Health and Nutrition Examination Survey, and the U.S. Census. This book also examines the years covered by these cross-sectional and longitudinal data sets, the units of analysis, and the sample sizes.Readers will learn where to find the data and the key variables contained within, and how to use them in SAS and Stata. Screen shots guide researchers through data sets in a step-by-step process: how to download the data, how to merge it with other data sets, and how to program it when necessary. Each section also profiles studies that have used the respective data sets, giving researchers a clear feel for the depth and range of questions that a given data source can be used to answer, like theuse of government data to explore issues ranging from pathways out of poverty to the relationship between marital dissolution and women's health and well-being. Exceptionally well calibrated and filled with real-world examples, this pocket guide will give beginning and advanced researchers a comprehensive understanding of these data sets that they can use in their research on clinical, policy, and other types studies.
R 2,554.85
Qualitative Analysis Using NVivo
Software is cut and dried – every button you press has a predictable effect – but qualitative analysis is open ended and unfolds in unpredictable ways. This contradiction is best resolved by separating analytic strategies – what you plan to do – from software tactics – how you plan to do it. Expert NVivo users have unconsciously learned to do this. The Five-Level QDA® method unpacks the process so that you can learn it consciously and efficiently. The first part of the book explains how the contradiction between analytic strategies and software tactics is reconciled by "translating" between them. The second part provides both an in-depth description of how NVivo works and comprehensive instruction in the five steps of "translation". These steps are illustrated with examples from a variety of research projects. The third part contains real-world qualitative research projects from a variety of disciplines, methodologies, and kinds of qualitative analysis, all illustrated in NVivo using the Five-Level QDA method. The book is accompanied by three sets of video demonstrations on the companion website.The book and accompanying videos illustrate the Windows version of NVivo. As there are some differences in screen and interface design between the Mac and Windows versions please watch the video 'The NVivo Mac Interface' in the Component Orientation series of videos (available on the companion website).The Five-Level QDA method is based on the authors’ combined 40 years of experience teaching NVivo and other software packages used as platforms for conducting qualitative analysis. After many years observing their students’ challenges they developed the Five-Level QDA method to describe the process that long-time NVivo experts unconsciously adopt. The Five-Level QDA method is independent of software program or methodology, and the principles apply to any type of qualitative project.Please see the following URL to access the accompanying materials for this book: https://www.qdaservices.co.uk/five-level-qda
R 2,684.00
Topological Data Analysis for Scientific Visualization
Combining theoretical and practical aspects of topology, this book provides a comprehensive and self-contained introduction to topological methods for the analysis and visualization of scientific data.Theoretical concepts are presented in a painstaking but intuitive manner, with numerous high-quality color illustrations. Key algorithms for the computation and simplification of topological data representations are described in detail, and their application is carefully demonstrated in a chapter dedicated to concrete use cases.With its fine balance between theory and practice, "Topological Data Analysis for Scientific Visualization" constitutes an appealing introduction to the increasingly important topic of topological data analysis for lecturers, students and researchers.
R 6,706.43
Qualitative Data Analysis
In this fully updated and expanded second edition, Carol Grbich provides a guide through current issues in the analysis of qualitative data. Packed with detailed examples, a glossary, further reading lists and a section on writing up, this book is exactly what you need to get you started in qualitative research. The new edition covers analytical approaches including: - grounded theory - classical, existential and hermeneutic phenomenology - feminist research including memory work - classical, auto- and cyberethnography as well as ethnodrama - content, narrative, conversation and discourse analysis - visual interpretation - semiotic, structural and poststructural analyses A one-stop-shop for students new to qualitative data analysis!
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User-Defined Tensor Data Analysis
The SpringerBrief introduces FasTensor, a powerful parallel data programming model developed for big data applications. This book also provides a user's guide for installing and using FasTensor. FasTensor enables users to easily express many data analysis operations, which may come from neural networks, scientific computing, or queries from traditional database management systems (DBMS). FasTensor frees users from all underlying and tedious data management tasks, such as data partitioning, communication, and parallel execution.This SpringerBrief gives a high-level overview of the state-of-the-art in parallel data programming model and a motivation for the design of FasTensor. It illustrates the FasTensor application programming interface (API) with an abundance of examples and two real use cases from cutting edge scientific applications. FasTensor can achieve multiple orders of magnitude speedup over Spark and other peer systems in executing big data analysis operations. FasTensor makes programming for data analysis operations at large scale on supercomputers as productively and efficiently as possible. A complete reference of FasTensor includes its theoretical foundations, C++ implementation, and usage in applications.Scientists in domains such as physical and geosciences, who analyze large amounts of data will want to purchase this SpringerBrief. Data engineers who design and develop data analysis software and data scientists, and who use Spark or TensorFlow to perform data analyses, such as training a deep neural network will also find this SpringerBrief useful as a reference tool.
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Advances in Intelligent Data Analysis VIII
The general theme of the Intelligent Data Analysis (IDA) Symposia is the - telligent use of computers in complex data analysis problems. The ?eld has matured su?ciently that some re-considerationof our objectives was required in order to retain the distinctiveness of IDA. Thus, in addition to the more tra- tional algorithm- and application-oriented submissions, we sought submissions that speci?cally focus on aspects of the data analysis process. For example, - teractive tools to guide and support data analysis in complex scenarios. With the increasingavailabilityofautomaticallycollecteddata,toolsthatintelligently support and assist human analysts are becoming important. IDA-09, the 8th International Symposium on Intelligent Data Analysis, took place in Lyon from August 31 to September 2, 2009. The invited speakers were PaulCohen(UniversityofArizona,USA)andPabloJensen(ENSLyon,France). The meeting received more than 80 submissions. The Programme Committee selected 33 submissions for publication: 18 for full oral presentation, and 15 for poster and short oralpresentation. Eachcontribution was evaluated by three expertsandhas beenallocated12pagesintheproceedings.Theacceptedpapers cover a broad range of topics and applications, and include contributions on the re?ned focus of IDA.
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Geographical Data Analysis
It is increasingly important for the earth science student to appreciate that the acquisition of skills in statistics and computerised data analysis is as much part of modern geography as work in the field, laboratory or library. In this respect, Geographical Data Analysis aims to link the use of statistical techniques by means of computer software, to the acquisition of geographical-knowledge and the scientific method of enquiry. The book has three objectives: to explain basic statistical techniques and demonstrate their application to quantitative geography; to equip students with the knowledge and skills necessary for carrying out research projects; and to make the link between statistical analysis and the substantive topics taught as part of a geography course. An important innovative feature of the book is its project-orientated approach, which utilises exemplar projects drawn from human and physical geography. Each exemplar project shows the progress from the conception of the initial research through to the formulation of tentative hypotheses and the subsequent statistical analysis. The projects exemplify both primary and secondary methods for collecting geographical data, with the computer-based application of a wide range of statistical techniques. Thus, these projects allow discussion of sample design, data collection and computerisation, and a selection of appropriate statistical techniques. As such, Geographical Data Analysis integrates quantitative and geographical methodologies and provides a thorough understanding of basic statistical techniques for the undergraduate geography student; it will be of use from first year through to final degree dissertations.
R 4,362.00
Bayesian Data Analysis for the Behavioral and Neural Sciences
This textbook bypasses the need for advanced mathematics by providing in-text computer code, allowing students to explore Bayesian data analysis without the calculus background normally considered a prerequisite for this material. Now, students can use the best methods without needing advanced mathematical techniques. This approach goes beyond “frequentist” concepts of p-values and null hypothesis testing, using the full power of modern probability theory to solve real-world problems. The book offers a fully self-contained course, which demonstrates analysis techniques throughout with worked examples crafted specifically for students in the behavioral and neural sciences. The book presents two general algorithms that help students solve the measurement and model selection (also called “hypothesis testing”) problems most frequently encountered in real-world applications.
R 5,434.75
Longitudinal Categorical Data Analysis
This is the first book in longitudinal categorical data analysis with parametric correlation models developed based on dynamic relationships among repeated categorical responses. This book is a natural generalization of the longitudinal binary data analysis to the multinomial data setup with more than two categories. Thus, unlike the existing books on cross-sectional categorical data analysis using log linear models, this book uses multinomial probability models both in cross-sectional and longitudinal setups. A theoretical foundation is provided for the analysis of univariate multinomial responses, by developing models systematically for the cases with no covariates as well as categorical covariates, both in cross-sectional and longitudinal setups. In the longitudinal setup, both stationary and non-stationary covariates are considered. These models have also been extended to the bivariate multinomial setup along with suitable covariates. For the inferences, the book uses the generalized quasi-likelihood as well as the exact likelihood approaches.The book is technically rigorous, and, it also presents illustrations of the statistical analysis of various real life data involving univariate multinomial responses both in cross-sectional and longitudinal setups. This book is written mainly for the graduate students and researchers in statistics and social sciences, among other applied statistics research areas. However, the rest of the book, specifically the chapters from 1 to 3, may also be used for a senior undergraduate course in statistics.
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Matrix-Based Introduction to Multivariate Data Analysis
This is the first textbook that allows readers who may be unfamiliar with matrices to understand a variety of multivariate analysis procedures in matrix forms. By explaining which models underlie particular procedures and what objective function is optimized to fit the model to the data, it enables readers to rapidly comprehend multivariate data analysis. Arranged so that readers can intuitively grasp the purposes for which multivariate analysis procedures are used, the book also offers clear explanations of those purposes, with numerical examples preceding the mathematical descriptions.Supporting the modern matrix formulations by highlighting singular value decomposition among theorems in matrix algebra, this book is useful for undergraduate students who have already learned introductory statistics, as well as for graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis.The book begins by explaining fundamental matrix operations and the matrix expressions of elementary statistics. Then, it offers an introduction to popular multivariate procedures, with each chapter featuring increasing advanced levels of matrix algebra. Further the book includes in six chapters on advanced procedures, covering advanced matrix operations and recently proposed multivariate procedures, such as sparse estimation, together with a clear explication of the differences between principal components and factor analyses solutions. In a nutshell, this book allows readers to gain an understanding of the latest developments in multivariate data science.
R 4,762.00
Health, Safety, and Environmental Data Analysis
Professionals in environmental health and safety (EHS) management use statistics every day in making decisions. This book was created to provide the quantitative tools and techniques necessary to make important EHS assessments. Readers need not be statistically or mathematically inclined to make the most of this book-mathematical derivations are kept to a minimum and subjects are approached in a simple and factual manner, complemented with plenty of real-world examples. Chapters 1-3 cover knowledge of basic statistical concepts such as presentation of data, measurements of location and dispersion, and elementary probability and distributions. Data gathering and analysis topics including sampling methods, sampling theory, testing, and interference as well as skills for critically evaluating published numerical material is presented in Chapters 4-6. Chapters 7-11 discuss information generation topics-regression and correlation analysis, time series, linear programming, network and Gnatt charting, and decision analysis-tools that can be used to convert data into meaningful information. Chapter 12 features six examples of projects made successful through statistical approaches being applied. Readers can use these approaches to solve their own unique problems.Whether you are a EHS professional, manager, or student, Health, Safety, and Environmental Data Analysis: A Business Approach will help you communicate statistical data effectively.
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R 611.00
Approximation Theory and Algorithms for Data Analysis
This textbook offers an accessible introduction to the theory and numerics of approximation methods, combining classical topics of approximation with recent advances in mathematical signal processing, and adopting a constructive approach, in which the development of numerical algorithms for data analysis plays an important role.The following topics are covered:* least-squares approximation and regularization methods* interpolation by algebraic and trigonometric polynomials* basic results on best approximations* Euclidean approximation* Chebyshev approximation* asymptotic concepts: error estimates and convergence rates* signal approximation by Fourier and wavelet methods* kernel-based multivariate approximation* approximation methods in computerized tomographyProviding numerous supporting examples, graphical illustrations, and carefully selected exercises, this textbook is suitable for introductory courses, seminars, and distance learning programs on approximation for undergraduate students.
R 2,620.18
Data Analysis, Machine Learning and Knowledge Discovery
Data analysis, machine learning and knowledge discovery are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medicine, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and knowledge discovery presented during the 36th annual conference of the German Classification Society (GfKl). The conference was held at the University of Hildesheim (Germany) in August 2012.
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Analysis of Messy Data, Volume III
Analysis of covariance is a very useful but often misunderstood methodology for analyzing data where important characteristics of the experimental units are measured but not included as factors in the design. Analysis of Messy Data, Volume 3: Analysis of Covariance takes the unique approach of treating the analysis of covariance problem by looking at a set of regression models, one for each of the treatments or treatment combinations. Using this strategy, analysts can use their knowledge of regression analysis and analysis of variance to help attack the problem. The authors describe the strategy for one- and two-way treatment structures with one and multiple covariates in a completely randomized design structure. They present new methods for comparing models and sets of parameters, including beta-hat models. They carefully investigate the effect of blocking, explore mixed models, and present a new methodology for using covariates to analyze data from nonreplicated experiments.Analysis of covariance provides an invaluable set of strategies for analyzing data.;With its careful balance of theory and examples, Analysis of Messy Data: Volume 3 provides a unique and outstanding guide to
R 10,004.04
Multivariate Data Analysis on Matrix Manifolds
This graduate-level textbook aims to give a unified presentation and solution of several commonly used techniques for multivariate data analysis (MDA). Unlike similar texts, it treats the MDA problems as optimization problems on matrix manifolds defined by the MDA model parameters, allowing them to be solved using (free) optimization software Manopt. The book includes numerous in-text examples as well as Manopt codes and software guides, which can be applied directly or used as templates for solving similar and new problems. The first two chapters provide an overview and essential background for studying MDA, giving basic information and notations. Next, it considers several sets of matrices routinely used in MDA as parameter spaces, along with their basic topological properties. A brief introduction to matrix (Riemannian) manifolds and optimization methods on them with Manopt complete the MDA prerequisite. The remaining chapters study individual MDA techniques in depth. The number of exercises complement the main text with additional information and occasionally involve open and/or challenging research questions. Suitable fields include computational statistics, data analysis, data mining and data science, as well as theoretical computer science, machine learning and optimization. It is assumed that the readers have some familiarity with MDA and some experience with matrix analysis, computing, and optimization.
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R 611.00
Coping with Complexity: Model Reduction and Data Analysis
This volume contains the extended version of selected talks given at the international research workshop "Coping with Complexity: Model Reduction and Data Analysis", Ambleside, UK, August 31 – September 4, 2009. The book is deliberately broad in scope and aims at promoting new ideas and methodological perspectives. The topics of the chapters range from theoretical analysis of complex and multiscale mathematical models to applications in e.g., fluid dynamics and chemical kinetics.
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Data Analysis and Research for Sport and Exercise Science
Data Analysis and Research for Sport and Exercise Science is tailored to suit undergraduate sports and exercise science students seeking a clear understanding of data and statistics to support their scientific research. The text is divided into three main areas: Research and Design, Data Analysis and the Interpretation of Findings. Topics covered in the book include:* introduction to the scientific research method* the literature review* developing your research question and experimental design* using statistical analysis to interpret results* presentation of your data* discussing your results and drawing conclusions.Both authors have supervised many student dissertations and have an excellent understanding of the concerns and pitfalls facing those new to this field.
R 3,114.00
Geostatistical Analysis of Compositional Data
'Geostatistical Analysis of Compositional Data' provides a comprehensive coverage of the theory and practice of analysis of data that have both spatial and compositional dependence, characteristics of most earth science and environmental measurements.
R 4,941.48
Data Analysis in Bi-partial Perspective: Clustering and Beyond
This book presents the bi-partial approach to data analysis, which is both uniquely general and enables the development of techniques for many data analysis problems, including related models and algorithms. It is based on adequate representation of the essential clustering problem: to group together the similar, and to separate the dissimilar. This leads to a general objective function and subsequently to a broad class of concrete implementations. Using this basis, a suboptimising procedure can be developed, together with a variety of implementations.This procedure has a striking affinity with the classical hierarchical merger algorithms, while also incorporating the stopping rule, based on the objective function. The approach resolves the cluster number issue, as the solutions obtained include both the content and the number of clusters. Further, it is demonstrated how the bi-partial principle can be effectively applied to a wide variety of problems in data analysis.The book offers a valuable resource for all data scientists who wish to broaden their perspective on basic approaches and essential problems, and to thus find answers to questions that are often overlooked or have yet to be solved convincingly. It is also intended for graduate students in the computer and data sciences, and will complement their knowledge and skills with fresh insights on problems that are otherwise treated in the standard “academic” manner.
R 3,389.96
Introduction to Computer-Intensive Methods of Data Analysis in Biology
This 2006 guide to the contemporary toolbox of methods for data analysis will serve graduate students and researchers across the biological sciences. Modern computational tools, such as Maximum Likelihood, Monte Carlo and Bayesian methods, mean that data analysis no longer depends on elaborate assumptions designed to make analytical approaches tractable. These new 'computer-intensive' methods are currently not consistently available in statistical software packages and often require more detailed instructions. The purpose of this book therefore is to introduce some of the most common of these methods by providing a relatively simple description of the techniques. Examples of their application are provided throughout, using real data taken from a wide range of biological research. A series of software instructions for the statistical software package S-PLUS are provided along with problems and solutions for each chapter.
R 6,644.64
Biostatistical Design and Analysis Using R
R — the statistical and graphical environment is rapidly emerging as an important set of teaching and research tools for biologists. This book draws upon the popularity and free availability of R to couple the theory and practice of biostatistics into a single treatment, so as to provide a textbook for biologists learning statistics, R, or both. An abridged description of biostatistical principles and analysis sequence keys are combined together with worked examples of the practical use of R into a complete practical guide to designing and analyzing real biological research. Topics covered include: simple hypothesis testing, graphingexploratory data analysis and graphical summariesregression (linear, multi and non-linear)simple and complex ANOVA and ANCOVA designs (including nested, factorial, blocking, spit-plot and repeated measures)frequency analysis and generalized linear models. Linear mixed effects modeling is also incorporated extensively throughout as an alternative to traditional modeling techniques. The book is accompanied by a companion website www.wiley.com/go/logan/r with an extensive set of resources comprising all R scripts and data sets used in the book, additional worked examples, the biology package, and other instructional materials and links.
R 2,962.00
Real Data Analysis
The invited authors of this edited volume have been prolific in the arena of Real Data Analysis (RDA) as it applies to the social and behavioral sciences, especially in the disciplines of education and psychology. Combined, this brain trust represents 3,247 articles in refereed journals, 127 books published, US $45.3 Million in extramural research funding, 34 teaching and 92 research awards, serve(d) as Editor/Assistant Editor/Editorial Board Member for 95 peer reviewed journals, and provide( d) ad hoc reviews for 362 journals. Their enormous footprint on real data analysis is showcased for professors, researchers, educators, administrators, and graduate students in the second text in the AERA/SIG ES Quantitative Methods series.
R 4,744.57
Analysis of Variance for Sensory Data
Analysis of Variance (ANOVA) is a statistical technique used in a number of chemical areas including the food industry. This handbook is a highly readable guide to the uses of the very important technique of ANOVA applied to sensory analysis. It is unique in stressing the practical implications of the topic rather than the theoretical background. This is important since ANOVA is a complex statistical technique which can be misunderstood and misused by sensory analysts. Typical problems from sensory analysis are used as examples.
R 6,023.00
R 641.00
