Language : English
Published : 2016-04-01
Pages : 552
Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner 3rd Edition
Praise for the Second Edition ” full of vivid and thought-provoking anecdotes…needs to be read by anyone with a serious interest in research and marketing.” Research Magazine “Shmueli et al. have done a wonderful job in presenting the field of data mining – a welcome addition to the literature.” ComputingReviews.com “Excellent choice for business analysts…The book is a perfect fit for its intended audience.” Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization ” extremely well organized, clearly written and introduces all of the basic ideas quite well.” Robert L. Phillips, Professor of Professional Practice, Columbia Business School Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner(R), Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft(R) Office Excel(R) add-in XLMiner(R) to develop predictive models and learn how to obtain business value from Big Data. Featuring updated topical coverage on text mining, social network analysis, collaborative filtering, ensemble methods, uplift modeling and more, the Third Edition also includes: * Real-world examples to build a theoretical and practical understanding of key data mining methods * End-of-chapter exercises that help readers better understand the presented material * Data-rich case studies to illustrate various applications of data mining techniques * Completely new chapters on social network analysis and text mining * A companion site with additional data sets, instructors material that include solutions to exercises and case studies, and Microsoft PowerPoint(R) slides * Free 140-day license to use XLMiner for Education software Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner(R), Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology.
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Business Statistics in Practice, Seventh Edition provides a modern, practical and unique framework for teaching an introductory course in Business Statistics. The textbook employs realistic examples, continuing case studies and a business improvement theme to teach the material. The Seventh Edition features more concise and lucid explanations, an improved topic flow and a sensible use of the best and most compelling examples.
Navidi/Monk, Essential Statistics was developed around three central themes – Clarity, Quality, and Accuracy. These central themes were born out of extensive market research and feedback from statistics instructors across the country. The authors paid close attention to how material is presented to students, ensuring that the content in the text is very clear, concise, and digestible. High quality exercises, examples and integration of technology are important aspects of an Introductory Statistics text. The authors have provided robust exercise sets that range in difficulty. They have also focused keen attention to ensure that examples provide clear instruction to students. Technology is integrated throughout the text, providing students examples of how to use the TI-83 Plus and TI-84 Plus Graphing Calculators, Microsoft Excel and Minitab. The accuracy of Elementary Statistics was a foundational principle always on the minds of the authors. While this certainly pertains to all aspects of the text, the authors also exhausted energy in ensuring the supplements have been developed to fit cohesively with the text.
Praise for the First Edition
“. . . a readable, comprehensive volume that . . . belongs on the desk, close at hand, of any serious researcher or practitioner.” —Mathematical Geosciences
The state of the art in geostatistics
Geostatistical models and techniques such as kriging and stochastic multi-realizations exploit spatial correlations to evaluate natural resources, help optimize their development, and address environmental issues related to air and water quality, soil pollution, and forestry. Geostatistics: Modeling Spatial Uncertainty, Second Edition presents a comprehensive, up-to-date reference on the topic, now featuring the latest developments in the field.
The authors explain both the theory and applications of geostatistics through a unified treatment that emphasizes methodology. Key topics that are the foundation of geostatistics are explored in-depth, including stationary and nonstationary models; linear and nonlinear methods; change of support; multivariate approaches; and conditional simulations. The Second Edition highlights the growing number of applications of geostatistical methods and discusses three key areas of growth in the field:
- New results and methods, including kriging very large datasets; kriging with outliers; nonse??parable space-time covariances; multipoint simulations; pluri-gaussian simulations; gradual deformation; and extreme value geostatistics
- Newly formed connections between geostatistics and other approaches such as radial basis functions, Gaussian Markov random fields, and data assimilation
- New perspectives on topics such as collocated cokriging, kriging with an external drift, discrete Gaussian change-of-support models, and simulation algorithms
Geostatistics, Second Edition is an excellent book for courses on the topic at the graduate level. It also serves as an invaluable reference for earth scientists, mining and petroleum engineers, geophysicists, and environmental statisticians who collect and analyze data in their everyday work.
From the Back Cover
A novel, practical approach to modeling spatial uncertainty.
This book deals with statistical models used to describe natural variables distributed in space or in time and space. It takes a practical, unified approach to geostatistics-integrating statistical data with physical equations and geological concepts while stressing the importance of an objective description based on empirical evidence. This unique approach facilitates realistic modeling that accounts for the complexity of natural phenomena and helps solve economic and development problems-in mining, oil exploration, environmental engineering, and other real-world situations involving spatial uncertainty.
Up-to-date, comprehensive, and well-written, Geostatistics: Modeling Spatial Uncertainty explains both theory and applications, covers many useful topics, and offers a wealth of new insights for nonstatisticians and seasoned professionals alike. This volume:
* Reviews the most up-to-date geostatistical methods and the types of problems they address.
* Emphasizes the statistical methodologies employed in spatial estimation.
* Presents simulation techniques and digital models of uncertainty.
* Features more than 150 figures and many concrete examples throughout the text.
* Includes extensive footnoting as well as a thorough bibliography.
Geostatistics: Modeling Spatial Uncertainty is the only geostatistical book to address a broad audience in both industry and academia. An invaluable resource for geostatisticians, physicists, mining engineers, and earth science professionals such as petroleum geologists, geophysicists, and hydrogeologists, it is also an excellent supplementary text for graduate-level courses in related subjects. –This text refers to an out of print or unavailable edition of this title.