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Excel Data Analysis For Dummies
Turn jumbles of numbers into graphics, insights, and answers with Excel With Microsoft Excel, you can, well, excel at data analysis. And Excel Data Analysis For Dummies can help, with clear and easy explanations of the mountain of features for creating, visualizing, and analyzing data. PivotTables, charts, what-if analysis, statistical functions—it's all in here, with examples and ideas for Excel users of all skill levels. This latest edition covers the most recent updates to Excel and Microsoft 365. You'll beef up your data skills and learn powerful techniques for turning numbers into knowledge. For students, researchers, and business professionals, Excel is the spreadsheet and data application of choice—and Dummies is the best choice for learning how to make those numbers sing. Learn how to use Excel's built-in data analysis features and write your own functions to explore patterns in your dataCreate striking charts and visualizations, and discover multiple ways to tell the stories hidden in the numbersClean up large datasets and identify statistical operations that will answer your questionsPerform financial calculations, database operations, and more—without leaving Excel Excel Data Analysis For Dummies is the go-to resource for Excel users who are looking for better ways to crunch the numbers.
R 821.00
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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.
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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.
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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.
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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.
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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
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Data-Variant Kernel Analysis
Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications. The book surveys the current status, popular trends, and developments in kernel analysis studies. The author discusses multiple kernel learning algorithms and how to choose the appropriate kernels during the learning phase. Data-Variant Kernel Analysis is a new pattern analysis framework for different types of data configurations. The chapters include data formations of offline, distributed, online, cloud, and longitudinal data, used for kernel analysis to classify and predict future state. Data-Variant Kernel Analysis: Surveys the kernel analysis in the traditionally developed machine learning techniques, such as Neural Networks (NN), Support Vector Machines (SVM), and Principal Component Analysis (PCA)Develops group kernel analysis with the distributed databases to compare speed and memory usagesExplores the possibility of real-time processes by synthesizing offline and online databasesApplies the assembled databases to compare cloud computing environmentsExamines the prediction of longitudinal data with time-sequential configurations Data-Variant Kernel Analysis is a detailed reference for graduate students as well as electrical and computer engineers interested in pattern analysis and its application in colon cancer detection.
R 4,753.00
Exact Statistical Methods for Data Analysis
This book covers some recent developments in statistical inference. The author's main aim is to develop a theory of generalized p-values and generalized confidence intervals and to show how these concepts may be used to make exact statistical inferences in a variety of practical applications. In particular, they provide methods applicable in problems involving nuisance parameters such as those encountered in comparing two exponential distributions or in ANOVA without the assumption of equal error variances. The generalized procedures are shown to be more powerful in detecting significant experimental results and in avoiding misleading conclusions.
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Complex Analysis
An introduction to complex analysis for students with some knowledge of complex numbers from high school. It contains sixteen chapters, the first eleven of which are aimed at an upper division undergraduate audience. The remaining five chapters are designed to complete the coverage of all background necessary for passing PhD qualifying exams in complex analysis. Topics studied include Julia sets and the Mandelbrot set, Dirichlet series and the prime number theorem, and the uniformization theorem for Riemann surfaces, with emphasis placed on the three geometries: spherical, euclidean, and hyperbolic. Throughout, exercises range from the very simple to the challenging. The book is based on lectures given by the author at several universities, including UCLA, Brown University, La Plata, Buenos Aires, and the Universidad Autonomo de Valencia, Spain.
R 3,922.44
Publication Bias in Meta-Analysis
Publication bias is the tendency to decide to publish a study based on the results of the study, rather than on the basis of its theoretical or methodological quality. It can arise from selective publication of favorable results, or of statistically significant results. This threatens the validity of conclusions drawn from reviews of published scientific research. Meta-analysis is now used in numerous scientific disciplines, summarizing quantitative evidence from multiple studies. If the literature being synthesised has been affected by publication bias, this in turn biases the meta-analytic results, potentially producing overstated conclusions. Publication Bias in Meta-Analysis examines the different types of publication bias, and presents the methods for estimating and reducing publication bias, or eliminating it altogether. Written by leading experts, adopting a practical and multidisciplinary approach. Provides comprehensive coverage of the topic including: Different types of publication bias,Mechanisms that may induce them,Empirical evidence for their existence,Statistical methods to address them,Ways in which they can be avoided. Features worked examples and common data sets throughout.Explains and compares all available software used for analysing and reducing publication bias.Accompanied by a website featuring software, data sets and further material. Publication Bias in Meta-Analysis adopts an inter-disciplinary approach and will make an excellent reference volume for any researchers and graduate students who conduct systematic reviews or meta-analyses. University and medical libraries, as well as pharmaceutical companies and government regulatory agencies, will also find this invaluable.
R 4,818.00
Comparative Analysis of Deterministic and Nondeterministic Decision Trees
This book compares four parameters of problems in arbitrary information systems: complexity of problem representation and complexity of deterministic, nondeterministic, and strongly nondeterministic decision trees for problem solving. Deterministic decision trees are widely used as classifiers, as a means of knowledge representation, and as algorithms. Nondeterministic (strongly nondeterministic) decision trees can be interpreted as systems of true decision rules that cover all objects (objects from one decision class). This book develops tools for the study of decision trees, including bounds on complexity and algorithms for construction of decision trees for decision tables with many-valued decisions. It considers two approaches to the investigation of decision trees for problems in information systems: local, when decision trees can use only attributes from the problem representation; and global, when decision trees can use arbitrary attributes from the information system. For both approaches, it describes all possible types of relationships among the four parameters considered and discusses the algorithmic problems related to decision tree optimization. The results presented are useful for researchers who apply decision trees and rules to algorithm design and to data analysis, especially those working in rough set theory, test theory and logical analysis of data. This book can also be used as the basis for graduate courses.
R 3,040.41
Latent Variable Models and Factor Analysis
Latent Variable Models and Factor Analysis provides a comprehensive and unified approach to factor analysis and latent variable modeling from a statistical perspective. This book presents a general framework to enable the derivation of the commonly used models, along with updated numerical examples. Nature and interpretation of a latent variable is also introduced along with related techniques for investigating dependency. This book: Provides a unified approach showing how such apparently diverse methods as Latent Class Analysis and Factor Analysis are actually members of the same family.Presents new material on ordered manifest variables, MCMC methods, non-linear models as well as a new chapter on related techniques for investigating dependency.Includes new sections on structural equation models (SEM) and Markov Chain Monte Carlo methods for parameter estimation, along with new illustrative examples.Looks at recent developments on goodness-of-fit test statistics and on non-linear models and models with mixed latent variables, both categorical and continuous. No prior acquaintance with latent variable modelling is pre-supposed but a broad understanding of statistical theory will make it easier to see the approach in its proper perspective. Applied statisticians, psychometricians, medical statisticians, biostatisticians, economists and social science researchers will benefit from this book.
R 3,320.00
Complex Analysis
This user-friendly textbook introduces complex analysis at the beginning graduate or advanced undergraduate level. Unlike other textbooks, it follows Weierstrass' approach, stressing the importance of power series expansions instead of starting with the Cauchy integral formula, an approach that illuminates many important concepts. This view allows readers to quickly obtain and understand many fundamental results of complex analysis, such as the maximum principle, Liouville's theorem, and Schwarz's lemma. The book covers all the essential material on complex analysis, and includes several elegant proofs that were recently discovered. It includes the zipper algorithm for computing conformal maps, as well as a constructive proof of the Riemann mapping theorem, and culminates in a complete proof of the uniformization theorem. Aimed at students with some undergraduate background in real analysis, though not Lebesgue integration, this classroom-tested textbook will teach the skills and intuition necessary to understand this important area of mathematics.
R 3,249.00
Counting Processes and Survival Analysis
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "The book is a valuable completion of the literature in this field. It is written in an ambitious mathematical style and can be recommended to statisticians as well as biostatisticians." -Biometrische Zeitschrift "Not many books manage to combine convincingly topics from probability theory over mathematical statistics to applied statistics. This is one of them. The book has other strong points to recommend it: it is written with meticulous care, in a lucid style, general results being illustrated by examples from statistical theory and practice, and a bunch of exercises serve to further elucidate and elaborate on the text." -Mathematical Reviews "This book gives a thorough introduction to martingale and counting process methods in survival analysis thereby filling a gap in the literature." -Zentralblatt für Mathematik und ihre Grenzgebiete/Mathematics Abstracts "The authors have performed a valuable service to researchers in providing this material in [a] self-contained and accessible form. . . This text [is] essential reading for the probabilist or mathematical statistician working in the area of survival analysis." -Short Book Reviews, International Statistical Institute Counting Processes and Survival Analysis explores the martingale approach to the statistical analysis of counting processes, with an emphasis on the application of those methods to censored failure time data. This approach has proven remarkably successful in yielding results about statistical methods for many problems arising in censored data. A thorough treatment of the calculus of martingales as well as the most important applications of these methods to censored data is offered. Additionally, the book examines classical problems in asymptotic distribution theory for counting process methods and newer methods for graphical analysis and diagnostics of censored data. Exercises are included to provide practice in applying martingale methods and insight into the calculus itself.
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Complex Analysis
Written by a master of the subject, this text will be appreciated by students and experts for the way it develops the classical theory of functions of a complex variable in a clear and straightforward manner. In general, the approach taken here emphasises geometrical aspects of the theory in order to avoid some of the topological pitfalls associated with this subject. Thus, Cauchy's integral formula is first proved in a topologically simple case from which the author deduces the basic properties of holomorphic functions. Starting from the basics, students are led on to the study of conformal mappings, Riemann's mapping theorem, analytic functions on a Riemann surface, and ultimately the Riemann–Roch and Abel theorems. Profusely illustrated, and with plenty of examples, and problems (solutions to many of which are included), this book should be a stimulating text for advanced courses in complex analysis.
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Inhibitory Rules in Data Analysis
This monograph is devoted to theoretical and experimental study of inhibitory decision and association rules. Inhibitory rules contain on the right-hand side a relation of the kind “attribut = value”. The use of inhibitory rules instead of deterministic (standard) ones allows us to describe more completely infor- tion encoded in decision or information systems and to design classi?ers of high quality. The mostimportantfeatureofthis monographis thatit includesanadvanced mathematical analysis of problems on inhibitory rules. We consider algorithms for construction of inhibitory rules, bounds on minimal complexity of inhibitory rules, and algorithms for construction of the set of all minimal inhibitory rules. We also discuss results of experiments with standard and lazy classi?ers based on inhibitory rules. These results show that inhibitory decision and association rules can be used in data mining and knowledge discovery both for knowledge representation and for prediction. Inhibitory rules can be also used under the analysis and design of concurrent systems. The results obtained in the monograph can be useful for researchers in such areas as machine learning, data mining and knowledge discovery, especially for those who are working in rough set theory, test theory, and logical analysis of data (LAD). The monograph can be used under the creation of courses for graduate students and for Ph.D. studies. TheauthorsofthisbookextendanexpressionofgratitudetoProfessorJanusz Kacprzyk, to Dr. Thomas Ditzinger and to the Studies in Computational Int- ligence sta? at Springer for their support in making this book possible.
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Nodes in Transport Networks – Research, Data Analysis and Modelling
The publication delivers numerous valuable guidelines, particularly useful when making decisions related in the subject matter to road and rail nodes located in dense transport networks. The know-how displayed while discussing practical examples as well as the decision making support systems described in the publication will certainly attract the interest of those who daily face the challenge of seeking solutions to the operational and functional problems of transport nodes in contemporary transport networks and systems. This publication is dedicated to local authorities involved in planning and preparation of development strategies for specific transport-related issues (in both urban and regional areas) as well as to representatives of business and industry, being those who participate directly in the implementation of traffic engineering solutions. The guidelines provided in individual chapters of the publication will make it possible to address the given problem in an advanced manner and simplify the choice of appropriate strategies (including those related to synchronisation of road traffic streams, improving the capacity, road traffic safety analysis, evaluation of changes in drivers’ behaviour on account of introducing countdown timers at signal-controlled intersections using UAV data, the influence of the type of traffic organisation on the behaviour of pedestrians at tram line crossings). On the other hand, since the publication also concerns the new approach to theoretical models (including potential places of integration of public transport with the railway network or the speed adviser for pedestrians enabling them to choose the optimal path at signal-controlled intersections), it should also attract the attention of researches and scientists studying this body of problems. The publication entitled "Nodes in transport networks - research, data analysis and modelling" contains selected papers submitted to and presented at the 16th ”Transport Systems. Theory and Practice” Scientific and Technical Conference organized by the Department of Transport Systems and Traffic Engineering at the Faculty of Transport of the Silesian University of Technology. The conference was held on 16-18 September 2019 in Katowice (Poland).
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Methods of Environmental Data Analysis
ENVIRONMENTAL MANAGEMENT SERIES The current expansion of both public and scientific interest in environ mental issues has not been accompanied by a commensurate production of adequate books, and those which are available are widely variable in approach and depth. The Environmental Management Series has been established with a view to co-ordinating a series of volumes dealing with each topic within the field in some depth. It is hoped that this Series will provide a uniform and quality coverage and that, over a period of years, it will build up to form a library of reference books covering most of the major topics within this diverse field. It is envisaged that the books will be of single, or dual authorship, or edited volumes as appropriate for respective topics. The level of presentation will be advanced, the books being aimed primarily at a research/consultancy readership. The coverage will include all aspects of environmental science and engineering pertinent to manage ment and monitoring of the natural and man-modified environment, as well as topics dealing with the political, t:conomic, legal and social con siderations pertaining to environmental management.
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R 3,606.39
Complex Analysis
This new edition of a classic textbook develops complex analysis from the established theory of real analysis by emphasising the differences that arise as a result of the richer geometry of the complex plane. Key features of the authors' approach are to use simple topological ideas to translate visual intuition to rigorous proof, and, in this edition, to address the conceptual conflicts between pure and applied approaches head-on. Beyond the material of the clarified and corrected original edition, there are three new chapters: Chapter 15, on infinitesimals in real and complex analysis; Chapter 16, on homology versions of Cauchy's theorem and Cauchy's residue theorem, linking back to geometric intuition; and Chapter 17, outlines some more advanced directions in which complex analysis has developed, and continues to evolve into the future. With numerous worked examples and exercises, clear and direct proofs, and a view to the future of the subject, this is an invaluable companion for any modern complex analysis course.
R 2,207.09
Flight Mechanics Modeling and Analysis
Flight Mechanics Modeling and Analysis comprehensively covers flight mechanics and flight dynamics using a systems approach.This book focuses on applied mathematics and control theory in its discussion of flight mechanics to build a strong foundation for solving design and control problems in the areas of flight simulation and flight data analysis. The second edition has been expanded to include two new chapters and coverage of aeroservoelastic topics and engineering mechanics, presenting more concepts of flight control and aircraft parameter estimation.This book is intended for senior undergraduate aerospace students taking Aircraft Mechanics, Flight Dynamics & Controls, and Flight Mechanics courses. It will also be of interest to research students and R&D project-scientists of the same disciplines.Including end-of-chapter exercises and illustrative examples with a MATLAB®-based approach, this book also includes a Solutions Manual and Figure Slides for adopting instructors.Features:Covers flight mechanics, flight simulation, flight testing, flight control, and aeroservoelasticityFeatures artificial neural network- and fuzzy logic-based aspects in modeling and analysis of flight mechanics systems: aircraft parameter estimation and reconfiguration of controlFocuses on a systems-based approachIncludes two new chapters, numerical simulation examples with MATLAB®-based implementations, and end-of-chapter exercisesIncludes a Solutions Manual and Figure Slides for adopting instructors
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System Analysis and Data Mining
This book presents the latest scientific contributions by Ukrainian researchers and their international colleagues in four interrelated fields: systems analysis, systems mathematics, computational intelligence, and data mining. The included papers explore both the theoretical foundations and practical applications of cutting-edge technologies across a wide range of topics--including, but not limited to, global security, artificial intelligence, environmental risks, financial systems, health care, digital twins, distributed computing, assistive robotics, and other critical areas of human activity. These fields are united by the complexity of the systems involved, the presence of uncertainty, and multifactorial risks. The methods and models presented in this volume will be valuable for researchers, educators, graduate and postgraduate students, as well as practitioners who implement modern tools and knowledge in systems analysis, artificial intelligence, and data mining to drive innovation in the digital economy. This book makes a significant contribution to the advancement of a systems based approach to understanding global transformations. It highlights how the integration of fundamental science, mathematical modeling, and intelligent technologies can offer profound insights into complex phenomena and support informed decision-making in vital sectors of contemporary life.
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Heterogeneous Information Network Analysis and Applications
This book offers researchers an understanding of the fundamental issues and a good starting point to work on this rapidly expanding field. It provides a comprehensive survey of current developments of heterogeneous information network. It also presents the newest research in applications of heterogeneous information networks to similarity search, ranking, clustering, recommendation. This information will help researchers to understand how to analyze networked data with heterogeneous information networks. Common data mining tasks are explored, including similarity search, ranking, and recommendation. The book illustrates some prototypes which analyze networked data.Professionals and academics working in data analytics, networks, machine learning, and data mining will find this content valuable. It is also suitable for advanced-level students in computer science who are interested in networking or pattern recognition.
R 6,952.44
Complex Analysis
An introduction to complex analysis for students with some knowledge of complex numbers from high school. It contains sixteen chapters, the first eleven of which are aimed at an upper division undergraduate audience. The remaining five chapters are designed to complete the coverage of all background necessary for passing PhD qualifying exams in complex analysis. Topics studied include Julia sets and the Mandelbrot set, Dirichlet series and the prime number theorem, and the uniformization theorem for Riemann surfaces, with emphasis placed on the three geometries: spherical, euclidean, and hyperbolic. Throughout, exercises range from the very simple to the challenging. The book is based on lectures given by the author at several universities, including UCLA, Brown University, La Plata, Buenos Aires, and the Universidad Autonomo de Valencia, Spain.
R 2,587.57
