Search results for "Exploring Complex Survey Data Analysis Using R"
Items we need to order for you
R 662.27
R 3,605.64
Quantitative Analysis
Written in a lecture format with solved problems at the end of each chapter, this book surveys quantitative modeling and decision analysis techniques. It serves to familiarize the reader with quantitative techniques utilized in planning and optimizing complex systems, as well as students experiencing the subject for the first time. It can be used by students of business and public administration without a background in calculus as well as engineers with significant scientific training. It allows the reader to comprehend the material through examples and problems and also demonstrates the value and shortcomings of many methods. Quantitative Analysis: An introduction developed out of the author's experience teaching the material to students at the University of California Los Angeles, California State University, Northridge, and the University of Southern California, Los Angeles.
R 12,822.00
Data Acquisition and Analysis for Multimedia GIS
Containing 31 papers collected in five chapters, this book deals with different fields of application and the problems of modelling and organising data in structures, as well as the processing techniques of GIS data for queries to the system and the so-called Dynamic GIS. The authors cover data acquisition with low cost DGPS for road surveys, vehicle navigation systems, and robust statistical techniques applied to pre-processing, analysis and testing. They report on the testing and calibration of different scanners for GIS data acquisition as well as some original approaches to the automatic DTM generation for cartographic and close range applications. Finally, applications to the environmental monitoring and the use of different kinds of geodetic data in multipurpose regional GIS are shown.
R 1,726.46
Data Analysis in Astronomy III
In the book are reported the main results presented at the Third International Workshop on Data Analysis in Astronomy, held at the EUore Majorana Center for Scientific Culture, Erice, Sicily, Italy, on June 20-27,1988. The Workshop was the natural evolution of the two previous ones. The main goal of the first edition (Erice 1984) was to start a scientific interaction between Astronomers and Computer Scientists. Aim of the second (Erice 1986) was to look at the progress in data analysis methods and dedicated hardware technology. Data analysis problems become harder whenever the data are poor in statistics or the signal is weak and embedded in structured background. Experiments collecting data of such a nature require new and non-standard methodologies. Possibilistic approaches could be merged with the statistical ones, in order to formalize all the knowledge used by the scientists to reach conclusions. Moreover, the last decade has been characterized by very fast developments of Intelligent Systems for data analysis (knowledge based systems, ... ) that would be useful to support astronomers in complex decision making. For these reasons, the last edition of the workshop was intended to provide an overview on the state of the art in the data analysis methodologies and tools in the new frontieres of the astrophysics (y-astronomy, neutrino astronomy, gravitational waves, background radiation and extreme cosmic ray energy spectrum). The book is organized in two sections: - Data analysis methods and tools, - New frontieres in astronomy.
R 1,610.76
Complex Analysis
Today, the theory of complex-valued functions finds widespread applications in various areas of mathematical research, as well as in electrical and mechanical engineering, aeronautics, and other disciplines. Complex analysis has become a basic course in mathematics, physics, and select engineering departments.This concise textbook provides a thorough introduction to the function theory of one complex variable. It presents the fundamental concepts with clarity and rigor, offering concise proofs that avoid lengthy and tedious arguments commonly found in mathematics textbooks. It goes beyond traditional texts by exploring less common topics, including the different approaches to constructing analytic functions, the conformal mapping criterion, integration of analytic functions along arbitrary curves, global analytic functions and their Riemann surfaces, the general inverse function theorem, the Lagrange–Bürmann formula, and Puiseux series.Drawing from several decades of teaching experience, this book is ideally suited for one or two semester courses in complex analysis. It also serves as a valuable companion for courses in topology, approximation theory, asymptotic analysis, and functional analysis. Abundant examples and exercises make it suitable for self-study as well.
R 3,297.00
Student Solutions Manual to Accompany Introduction to Time Series Analysis and Forecasting
An accessible introduction to the most current thinking in and practicality of forecasting techniques in the context of time-oriented data Analyzing time-oriented data and forecasting are among the most important problems that analysts face across many fields, ranging from finance and economics to production operations and the natural sciences. As a result, there is a widespread need for large groups of people in a variety of fields to understand the basic concepts of time series analysis and forecasting. Introduction to Time Series Analysis and Forecasting presents the time series analysis branch of applied statistics as the underlying methodology for developing practical forecasts, and it also bridges the gap between theory and practice by equipping readers with the tools needed to analyze time-oriented data and construct useful, short- to medium-term, statistically based forecasts. Seven easy-to-follow chapters provide intuitive explanations and in-depth coverage of key forecasting topics, including: Regression-based methods, heuristic smoothing methods, and general time series models Basic statistical tools used in analyzing time series data Metrics for evaluating forecast errors and methods for evaluating and tracking forecasting performanceover time Cross-section and time series regression data, least squares and maximum likelihood model fitting, model adequacy checking, prediction intervals, and weighted and generalized least squares Exponential smoothing techniques for time series with polynomial components and seasonal data Forecasting and prediction interval construction with a discussion on transfer function models as well as intervention modeling and analysis Multivariate time series problems, ARCH and GARCH models, and combinations of forecasts The ARIMA model approach with a discussion on how to identify and fit these models for non-seasonal and seasonal time series The intricate role of computer software in successful time series analysis is acknowledged with the use of Minitab, JMP, and SAS software applications, which illustrate how the methods are imple-mented in practice. An extensive FTP site is available for readers to obtain data sets, Microsoft Office PowerPoint slides, and selected answers to problems in the book. Requiring only a basic working knowledge of statistics and complete with exercises at the end of each chapter as well as examples from a wide array of fields, Introduction to Time Series Analysis and Forecasting is an ideal text for forecasting and time series coursesat the advanced undergraduate and beginning graduate levels. The book also serves as an indispensablereference for practitioners in business, economics, engineering, statistics, mathematics, and the social, environmental, and life sciences.
R 1,411.00
Nonparametric Regression Analysis of Longitudinal Data
This monograph reviews some of the work that has been done for longitudi nal data in the rapidly expanding field of nonparametric regression. The aim is to give the reader an impression of the basic mathematical tools that have been applied, and also to provide intuition about the methods and applications. Applications to the analysis of longitudinal studies are emphasized to encourage the non-specialist and applied statistician to try these methods out. To facilitate this, FORTRAN programs are provided which carry out some of the procedures described in the text. The emphasis of most research work so far has been on the theoretical aspects of nonparametric regression. It is my hope that these techniques will gain a firm place in the repertoire of applied statisticians who realize the large potential for convincing applications and the need to use these techniques concurrently with parametric regression. This text evolved during a set of lectures given by the author at the Division of Statistics at the University of California, Davis in Fall 1986 and is based on the author's Habilitationsschrift submitted to the University of Marburg in Spring 1985 as well as on published and unpublished work. Completeness is not attempted, neither in the text nor in the references. The following persons have been particularly generous in sharing research or giving advice: Th. Gasser, P. Ihm, Y. P. Mack, V. Mammi tzsch, G . G. Roussas, U. Stadtmuller, W. Stute and R.
R 3,098.48
Complex Analysis
This volume contains the Proceedings of the International Workshop "Complex Analysis", which was held from February 12-16, 1990, in Wuppertal (Germany) in honour of H. Grauert, one of the most creative mathematicians in Complex Analysis of this century. In complete accordance with the width of the work of Grauert the book contains research notes and longer articles of many important mathematicians from all areas of Complex Analysis (Altogether there a re 49 articles in the volume). Some of the main subjects are: Cau chy-Riemann Equations with estimates, q-convexity, CR structures, deformation theory, envelopes of holomorphy, function algebras, complex group actions, Hodge theory, instantons, Kähler geometry, Lefschetz theorems, holomorphic mappings, Nevanlinna theory, com plex singularities, twistor theory, uniformization.
R 3,046.88
Biomedical Data Science: A Step-by-Step Guide to Analysis and Interpretation
Biomedical Data Science: A Step-by-Step Guide to Analysis and Interpretation is a practical roadmap for transforming complex biomedical data into reliable insights. Designed for readers at the crossroads of biology, medicine, and computation, the book walks readers through the entire lifecycle of analysis, from formulating clear questions to designing robust studies, quantifying uncertainty, building and validating models, and interpreting results responsibly.Instead of overwhelming the reader with derivations, it emphasises conceptual clarity, reproducibility, and interpretability, linking key ideas with Python workflows using widely adopted libraries. Core statistical tools (estimation, confidence intervals, hypothesis testing, multiple testing) are integrated with essential machine-learning practices (cross-validation, metrics, baseline vs. null models, sanity checks, and model explanation).An end-to-end clinical case study ties everything together—demonstrating how design decisions, preprocessing, statistical analysis, and predictive modelling collectively influence conclusions and clinical significance. As the first volume of the River Series, this book establishes a practical, open, and interdisciplinary approach to data-driven biomedicine, guiding readers to “get it right” from the outset.
R 5,001.00
Data Analysis and Signal Processing in Chromatography
This book gives an overview of the numerical data analysis and signal treatment techniques that are used in chromatography and related separation techniques. Emphasis is given to the description of the symmetrical and asymmetrical chromatographic peak shape models. Both theoretical and empirical models are discussed.The fundamentals of data acquisition, types and effect of baseline noise, and methods of improving the signal-to-noise ratio (either in time or in frequency and wavelet domain) are thoroughly discussed. Resolution enhancement techniques, such as curve fitting, deconvolution by Fourier and wavelet transforms, iterative deconvolution, Kalman filtering and multivariate methods of curve resolution are all discussed with several chromatographic examples. Quantitative analysis by peak area of peak height measurement, the precision and accuracy of the quantitation of stand-alone or overlapping and symmetrical or asymmetrical peaks are treated. In a separate chapter, guidelines are given for the use of transform techniques for the analysis of chromatograms. A statistical description of peak overlap is given in the final chapters. Since the concept of resolution has to be reconsidered when one separates complex mixtures, the problem of resolution and overlap is quantitatively discussed by means of statistical methods, and by using Fourier analysis of the complex chromatogram.Features of this book• The ultimate source of numerical techniques to enhance chromatographic data• Gives a detailed description of signal and resolution enhancement techniques in a manner applicable for enhancing not only chromatography, but also spectroscopic and other analytical signals• The first book with a thorough overview of the statistics of peak overlap.This is the first volume to encompass both the simple and more sophisticated methods for the numerical treatment of chromatograms. It is, therefore, the fundamental resource of numerical analysis methods for every analyst.
R 8,004.00
Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video
This thesis proposes machine learning methods for understanding scenes via behaviour analysis and online anomaly detection in video. The book introduces novel Bayesian topic models for detection of events that are different from typical activities and a novel framework for change point detection for identifying sudden behavioural changes.Behaviour analysis and anomaly detection are key components of intelligent vision systems. Anomaly detection can be considered from two perspectives: abnormal events can be defined as those that violate typical activities or as a sudden change in behaviour. Topic modelling and change-point detection methodologies, respectively, are employed to achieve these objectives.The thesis starts with the development of learning algorithms for a dynamic topic model, which extract topics that represent typical activities of a scene. These typical activities are used in a normality measure in anomaly detection decision-making. The book also proposes a novel anomaly localisation procedure. In the first topic model presented, a number of topics should be specified in advance. A novel dynamic nonparametric hierarchical Dirichlet process topic model is then developed where the number of topics is determined from data. Batch and online inference algorithms are developed.The latter part of the thesis considers behaviour analysis and anomaly detection within the change-point detection methodology. A novel general framework for change-point detection is introduced. Gaussian process time series data is considered. Statistical hypothesis tests are proposed for both offline and online data processing and multiple change point detection are proposed and theoretical properties of the tests are derived. The thesis is accompanied by open-source toolboxes that can be used by researchers and engineers.
R 3,339.80
Machine Learning for Emotion Analysis
Kickstart your emotion analysis journey with this hands-on, step-by-step guide to data science successKey Features* Discover the ins and outs of the end-to-end emotional analysis workflow* Explore the use of various ML models to derive meaningful insights from all sorts of data* Hone your craft by building and tweaking complex emotion analysis models in practical projectsBook DescriptionThe AI winter has long thawed, but many organizations are still failing to harness the power of machine learning (ML). If you want to tap that potential and add value to your own business with cutting-edge emotion analysis, you've found what you need in this trusty guide.In Machine Learning for Emotion Analysis, you'll take your foundational data science skills and grow them in the exciting realm of emotion analysis. With its practical approach, you'll be equipped with everything you need to give your company a clear insight into what your customers are thinking.This no-nonsense guide jumps right into the practicalities of emotion analysis, teaching you how to preprocess data, build a serviceable dataset, and ensure top-notch data quality. Once you're set up for success, we get hands-on with complex ML techniques. This is where you go from the intermediate to the advanced, covering deep neural networks, support vector machines, conditional probabilities, and more, as you experience the full breadth of possibilities with emotion analysis. The book finally rounds out with a couple of in-depth use cases – a sort of sandbox for you to experiment with your newly acquired skill set.By the end of this book, you'll be ready to present yourself as a valuable asset to any organization that takes data science seriously.What you will learn* Distinguish between sentiment analysis and emotion analysis* Master the art of data preprocessing and ensure high-quality input* Expand your use of data sources through data transformation* Build models that employ cutting-edge deep learning techniques* Discover how best to tune your models' hyperparameters* Explore the use of KNN, SVM, and DNNs for advanced use cases* Build APIs and integrate your models into existing solutions* Practice your new skills by working on real-world scenariosWho This Book Is ForThis book is for data scientists and Python developers who want to gain insights into what people are saying about their product, company, brand, governorship, and more. Basic knowledge of machine learning and Python programming knowledge is necessary to grasp the concepts covered.
R 1,693.00
R 766.14
R 903.45
R 1,498.00
Bayesian Data Analysis
Winner of the 2016 De Groot Prize from the International Society for Bayesian AnalysisNow in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice.New to the Third Edition Four new chapters on nonparametric modelingCoverage of weakly informative priors and boundary-avoiding priorsUpdated discussion of cross-validation and predictive information criteriaImproved convergence monitoring and effective sample size calculations for iterative simulationPresentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagationNew and revised software codeThe book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
R 5,778.29
Fablab Global Survey. Resultados De Un Estudio Sobre El Desarrollo De La Cultura Colaborativa.
No hay duda de que los Laboratorios de Fabricacion Digital son, actualmente, un fenomeno en auge. La popularizacion de elementos tecnologicos apropiados para el prototipado y el diseno como las impresoras 3D, las cortadoras laser o la microelectronica OpenHardware y el cambio cultural que propicia la evolucion del DIY (Do It Yourself) al DIWO (Do It With Others), han servido de perfecto trampolin para la popularizacion de un nuevo espacio de creacion digital amparado en la cultura Maker: los FabLabs. A traves de este documento se comparte la informacion referente al "estado del arte" del movimiento FabLab en el ano 2016 a traves de sus datos, tomando como referencia la informacion obtenida a partir del cuestionario FabLab Global Survey desarrollado por el grupo ATICI del Departamento de Administracion de Empresas de la Universidad de Cantabria.
R 856.95
Higher Education Policy Analysis Using Quantitative Techniques
This textbook introduces graduate students in education and policy research to data and statistical methods in state-level higher education policy analysis. It also serves as a methodological guide to students, practitioners, and researchers who want a clear approach to conducting higher education policy analysis that involves the use of institutional- and state-level secondary data and quantitative methods ranging from descriptive to advanced statistical techniques. This book is unique in that it introduces readers to various types of data sources and quantitative methods utilized in policy research and in that it demonstrates how results of statistical analyses should be presented to higher education policy makers. It helps to bridge the gap between researchers, policy makers, and practitioners both within education policy and between other fields.Coverage includes identifying pertinent data sources, the creation and management of customized data sets, teaching beginning and advanced statistical methods and analyses, and the presentation of analyses for different audiences (including higher education policy makers).
R 3,055.93
Methods for Multilevel Analysis and Visualisation of Geographical Networks
This leading-edge study focuses on the latest techniques in analysing and representing the complex, multi-layered data now available to geographers studying urban zones and their populations. The volume tracks the successful results of the SPANGEO Project, which was set up in 2005 to standardize, and share, the syncretic, multinational mapping techniques already developed by geographers and computer scientists. SPANGEO sought new and responsive ways of visualising urban geographical and social data that reflected the fine-grained detail of the inputs. It allowed for visual representation of the large and complex networks and flows which are such an integral feature of the dynamism of urban geography. SPANGEO developed through the ‘visual analytics loop’ in which geographers collaborated with computer scientists by feeding data into the design of visualisations that in turn spawned the urge to incorporate more varied data into the visualisation. This volume covers all the relevant aspects, from conceptual principles to the tools of network analysis and the actual results flowing from their deployment. Detailed case studies set out in this volume include spatial multi-level analyses of flows in airports and sea ports, as well as the fascinating scientific networks in European cities. The volume shows how the primary concern of geography—the interaction of society with physical space—has been revivified by the complexities of new cartographical and statistical methodologies, which allow for highly detailed mapping and far more powerful computer analysis of spatial relationships.
R 3,026.59
Topological Data Analysis with Applications
The continued and dramatic rise in the size of data sets has meant that new methods are required to model and analyze them. This timely account introduces topological data analysis (TDA), a method for modeling data by geometric objects, namely graphs and their higher-dimensional versions: simplicial complexes. The authors outline the necessary background material on topology and data philosophy for newcomers, while more complex concepts are highlighted for advanced learners. The book covers all the main TDA techniques, including persistent homology, cohomology, and Mapper. The final section focuses on the diverse applications of TDA, examining a number of case studies drawn from monitoring the progression of infectious diseases to the study of motion capture data. Mathematicians moving into data science, as well as data scientists or computer scientists seeking to understand this new area, will appreciate this self-contained resource which explains the underlying technology and how it can be used.
R 1,938.00
Ridges in Image and Data Analysis
The concept of ridges has appeared numerous times in the image processing liter ature. Sometimes the term is used in an intuitive sense. Other times a concrete definition is provided. In almost all cases the concept is used for very specific ap plications. When analyzing images or data sets, it is very natural for a scientist to measure critical behavior by considering maxima or minima of the data. These critical points are relatively easy to compute. Numerical packages always provide support for root finding or optimization, whether it be through bisection, Newton's method, conjugate gradient method, or other standard methods. It has not been natural for scientists to consider critical behavior in a higher-order sense. The con cept of ridge as a manifold of critical points is a natural extension of the concept of local maximum as an isolated critical point. However, almost no attention has been given to formalizing the concept. There is a need for a formal development. There is a need for understanding the computation issues that arise in the imple mentations. The purpose of this book is to address both needs by providing a formal mathematical foundation and a computational framework for ridges. The intended audience for this book includes anyone interested in exploring the use fulness of ridges in data analysis.
R 3,026.76
Advances in Intelligent Data Analysis
Formanyyearstheintersectionofcomputing anddataanalysiscontainedme- based statistics packages and not much else. Recently, statisticians have - braced computing, computer scientists have started using statistical theories and methods, and researchers in all corners have invented algorithms to nd structure in vast online datasets. Data analysts now have access to tools for exploratory data analysis, decision tree induction, causal induction, function - timation,constructingcustomizedreferencedistributions,andvisualization,and thereareintelligentassistantsto adviseonmatters ofdesignandanalysis.There aretoolsfortraditional,relativelysmallsamples,andalsoforenormousdatasets. In all, the scope for probing data in new and penetrating ways has never been so exciting. The IDA-99 conference brings together a wide variety of researchers c- cerned with extracting knowledge from data, including people from statistics, machine learning, neural networks, computer science, pattern recognition, da- base management, and other areas.The strategiesadopted by people from these areas are often di erent, and a synergy results if this is recognized.The IDA series of conferences is intended to stimulate interaction between these di erent areas,sothatmorepowerfultoolsemergeforextractingknowledgefromdataand a better understanding is developed of the process of intelligent data analysis. The result is a conference that has a clear focus (one application area:intelligent data analysis) and a broad scope (many di erent methods and techniques).
R 3,348.60
R 611.00
R 1,757.00
Neurocomputation in Remote Sensing Data Analysis
This volume gives a state of the art view of recent developments in the use of artificial neural networks for the analysis of remotely sensed satellite data. Remote sensing has now become a discipline in which ever increasing volumes of data, gathered from space together with growing application needs for high precision spatial products, need to be interpreted in shorter times and with increasing accuracy. Neural networks, as a new form of computational paradigm, seem well suited to many of the tasks involved in remotely sensed image analysis. This book demonstrates a wide range of uses of neural networks for remote sensing applications and provides the views of a large number European experts brought together in the framework of a concerted action supported by the European Commission.
R 2,741.13
R 1,298.94
R 618.00
Big Data Analysis on Global Community Formation and Isolation
In this book, the authors analyze big data on global interdependence caused by the flows of commodities, money, and people, using a network science approach to obtain differing views of globalization and to clarify the facts on isolation of communities.Globalization reduces international economic inequality, i.e., it allows emerging countries to catch up while it increases relative poverty in some advanced countries. How should this trade-off between international and domestic inequalities be resolved? At the same time, the reduction of biocultural diversity caused by globalization needs to be avoided. What kind of change is required in local communities to conserve biocultural diversity? On the issue of commodity flow, research results of the supply-chain network, isolation in industry, and resource flows and stocks are presented in this book. For monetary flow, ownership networks, value-added networks, and profit shifting were studied; and regarding the flow of people, linkage of ethnic groups, immigrant assimilation, and refugees were examined. Based on the resulting view of globalization and isolation, the development of the isolation index using machine learning is discussed. Finally, recommendations for evidence-based policymaking in the United Nations are considered.
R 4,722.50
Business Statistics Using Excel
This book gives readers hands-on understanding of Excel assisted statistical techniques to take effective business decisions. It showcases applications of the tools and techniques of statistics for analysing business data from the domain of business statistics.The volume provides an exhaustive introduction to the application of statistics in solving business problems and implement data analytics for effective decision making in all kinds of business situations around the world. With an emphasis for simplicity in presentation of concepts of statistical methods and associated Excel functions, the volume explores the implementation of Excel functions through well-defined sequence of steps. It covers an array of key topics which includes,- Discussions on real world problems, decision support systems, scope of business statistics, types, and steps of research;- Introduction to excel and its mathematical and preliminary statistical functions, usage of different types of average function, mean, median and mode functions, measures of variation, measures of skewness of Excel;- In depth discussions on probability distributions, sampling distributions, testing of hypothesis, chi-square test, non-parametric test of Excel;- Extensive coverage on correlation and covariance, forecasting, Analysis of Variance, charts of Excel, and- Analysis of concept of linear programming, problem formulations, and techniques of linear programming, followed by the application of Excel.Comprehensive in scope and simple in approach, this book will be key for students and researchers of business studies, business administration, economics, finance, commerce, data analytics/ science, computer science. This will also serve as useful guidebook for business executives and working professionals across the globe.
R 2,052.00
Statistical Design, Monitoring, and Analysis of Clinical Trials
Statistical Design, Monitoring, and Analysis of Clinical Trials, Second Edition concentrates on the biostatistics component of clinical trials. This new edition is updated throughout and includes five new chapters.Developed from the authors’ courses taught to public health and medical students, residents, and fellows during the past 20 years, the text shows how biostatistics in clinical trials is an integration of many fundamental scientific principles and statistical methods. The book begins with ethical and safety principles, core trial design concepts, the principles and methods of sample size and power calculation, and analysis of covariance and stratified analysis. It then focuses on sequential designs and methods for two-stage Phase II cancer trials to Phase III group sequential trials, covering monitoring safety, futility, and efficacy. The authors also discuss the development of sample size reestimation and adaptive group sequential procedures, phase 2/3 seamless design and trials with predictive biomarkers, exploit multiple testing procedures, and explain the concept of estimand, intercurrent events, and different missing data processes, and describe how to analyze incomplete data by proper multiple imputations. This text reflects the academic research, commercial development, and public health aspects of clinical trials. It gives students and practitioners a multidisciplinary understanding of the concepts and techniques involved in designing, monitoring, and analyzing various types of trials. The book’s balanced set of homework assignments and in-class exercises are appropriate for students and researchers in (bio)statistics, epidemiology, medicine, pharmacy, and public health.
R 5,661.83
R 519.00
Fuzzy Surfaces in GIS and Geographical Analysis
Surfaces are a central to geographical analysis. Their generation and manipulation are a key component of geographical information systems (GISs). However, geographical surface data is often not precise. When surfaces are used to model geographical entities, the data inherently contains uncertainty in terms of both position and attribute. Fuzzy Surface in GIS and Geographical Analysis sets out a process to identify the uncertainty in geographic entities. It describes how to successfully obtain, model, analyze, and display data, as well as interpret results within the context of GIS.Focusing on uncertainty that arises from transitional boundaries, the book limits its study to three types of uncertainties: intervals, fuzzy sets, and possibility distributions. The book explains that uncertainty in geographical data typically stems from these three and it is only natural to incorporate them into the analysis and display of surface data. The book defines the mathematics associated with each method for analysis, then develops related algorithms, and moves on to illustrate various applications. Fuzzy Surface in GIS and Geographical Analysis clearly defines how to develop a routine that will adequately account for the uncertainties inherent in surface data.
R 3,967.53
Complex Analysis
This is a very successful textbook for undergraduate students of pure mathematics. Students often find the subject of complex analysis very difficult. Here the authors, who are experienced and well-known expositors, avoid many of such difficulties by using two principles: (1) generalising concepts familiar from real analysis; (2) adopting an approach which exhibits and makes use of the rich geometrical structure of the subject. An opening chapter provides a brief history of complex analysis which sets it in context and provides motivation.
R 4,516.03
Introduction to Environmental Data Analysis and Modeling
This book introduces numerical methods for processing datasets which may be of any form, illustrating adequately computational resolution of environmental alongside the use of open source libraries. This book solves the challenges of misrepresentation of datasets that are relevant directly or indirectly to the research. It illustrates new ways of screening datasets or images for maximum utilization. The adoption of various numerical methods in dataset treatment would certainly create a new scientific approach. The book enlightens researchers on how to analyse measurements to ensure 100% utilization. It introduces new ways of data treatment that are based on a sound mathematical and computational approach.
R 5,161.46
R 1,073.00
R 597.31
Sequence Data Analysis Guidebook
Computers have revolutionized the analysis of sequencing data. It is unlikely that any sequencing projects have been performed in the last few years without the aid of computers. Recently their role has taken a further major step forward. Computers have become smaller and more powerful and the software has become simpler to use as it has grown in sophistication. This book reflects that change since the majority of packages described here are designed to be used on desktop computers. Computer software is now available that can run gels, collect data, and assess its accuracy. It can assemble, align, or compare multiple fragments, perform restriction analyses, identify coding regions and specific motifs, and even design the primers needed to extend the sequencing. Much of this soft ware may now be used on relatively inexpensive computers. It is now possible to progress from isolate d DNA to database submission without writing a single base down. To reflect this progression, the chapters in our Sequence Data Analysis Guidebook are arranged, not by software package, but by fimction. The early chapters deal with examining the data produced by modem automated sequenc ers, assessing its quality, and removing extraneous data. The following chap ters describe the process of aligning multiple sequences in order to assemble overlapping fragments into sequence contigs to compare similar sequences from different sources. Subsequent chapters describe procedures for compar ing the newly derived sequence to the massive amounts of information in the sequence databases.
R 4,660.02
R 1,008.00
Big Data: Conceptual Analysis and Applications
The book is devoted to the analysis of big data in order to extract from these data hidden patterns necessary for making decisions about the rational behavior of complex systems with the different nature that generate this data. To solve these problems, a group of new methods and tools is used, based on the self-organization of computational processes, the use of crisp and fuzzy cluster analysis methods, hybrid neural-fuzzy networks, and others. The book solves various practical problems. In particular, for the tasks of 3D image recognition and automatic speech recognition large-scale neural networks with applications for Deep Learning systems were used. Application of hybrid neuro-fuzzy networks for analyzing stock markets was presented. The analysis of big historical, economic and physical data revealed the hidden Fibonacci pattern about the course of systemic world conflicts and their connection with the Kondratieff big economic cycles and the Schwabe–Wolf solar activity cycles. The book is useful for system analysts and practitioners working with complex systems in various spheres of human activity.
R 5,028.92
R 1,041.00
Grey Data Analysis
This book inclusively and systematically presents the fundamental methods, models and techniques of practical application of grey data analysis, bringing together the authors’ many years of theoretical exploration, real-life application, and teaching. It also reflects the majority of recent theoretical and applied advances in the theory achieved by scholars from across the world, providing readers a vivid overall picture of this new theory and its pioneering research activities. The book includes 12 chapters, covering the introduction to grey systems, a novel framework of grey system theory, grey numbers and their operations, sequence operators and grey data mining, grey incidence analysis models, grey clustering evaluation models, series of GM models, combined grey models, techniques for grey systems forecasting, grey models for decision-making, techniques for grey control, etc. It also includes a software package that allows practitioners to conveniently and practically employ the theory and methods presented in this book. All methods and models presented here were chosen for their practical applicability and have been widely employed in various research works.I still remember 1983, when I first participated in a course on Grey System Theory. The mimeographed teaching materials had a blue cover and were presented as a book. It was like finding a treasure: This fascinating book really inspired me as a young intellectual going through a period of confusion and lack of academic direction. It shone with pearls of wisdom and offered a beacon in the mist for a man trying to find his way in academic research. This book became the guiding light in my life journey, inspiring me to forge an indissoluble bond with Grey System Theory.——Sifeng Liu
R 4,233.17
