Using Data, Modeling, and Analytics

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Author: Bruce Bowerman,Dr Richard O'Connell,Emilly Murphree

Publisher: McGraw-Hill

ISBN: 9781260016499

Category:

Page: 918

View: 2799

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Author: Bruce Bowerman

Publisher: McGraw-Hill Higher Education

ISBN: 1259683826

Category:

Page: 864

View: 5758

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Author: Bruce Bowerman,Richard O'Connell,Emilly Murphree

Publisher: McGraw-Hill Education

ISBN: 9781259549465

Category: Business & Economics

Page: 912

View: 6673

Business Statistics in Practice, Eighth 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. Connect is the only integrated learning system that empowers students by continuously adapting to deliver precisely what they need, when they need it, and how they need it, so that your class time is more engaging and effective.

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Author: Bruce L. Bowerman,Richard T. O'Connell,J. B. Orris

Publisher: Irwin Professional Pub

ISBN: 9780072977479

Category: Business & Economics

Page: 881

View: 9309

The new edition of Business Statistics in Practice delivers clear and understandable explanations of business statistics concepts through the use of continuing case studies and an emphasis on business improvement. The cases and examples show real applications of statistics relevant to today's business students. The authors motivate students by showing persuasively how the use of statistical techniques in support of business decision-making helps to improve business processes. A variety of computer centered examples and exercises, and a robust, technology-based ancillary package are designed to help students master this subject. Acknowledging the importance of spreadsheets and statistical software in their statistical instruction, the authors continue to integrate Excel and Minitab output throughout the text. In addition, a new enhanced version of MegaStat, an Excel add-in program designed to optimize Excel for statistical application, is available free on the Student CD. For students and instructors who want to explore statistical concepts from a graphical perspective, Visual Statistics is again available on the Student CD. New Business Improvement icons are integrated throughout the text to illustrate the ‘BI’ theme.

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Author: Pavel Ryzhov

Publisher: Packt Publishing Ltd

ISBN: 178216944X

Category: Computers

Page: 112

View: 5577

This book is a hands-on guide that teaches readers how to use Haskell's tools and libraries to analyze data from real-world sources in an easy-to-understand manner.This book is great for developers who are new to financial data modeling using Haskell. A basic knowledge of functional programming is not required but will be useful. An interest in high frequency finance is essential.

Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition

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Author: Bruce Ratner

Publisher: CRC Press

ISBN: 1466551216

Category: Business & Economics

Page: 542

View: 7752

The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, contained 17 chapters of innovative and practical statistical data mining techniques. In this second edition, renamed to reflect the increased coverage of machine-learning data mining techniques, the author has completely revised, reorganized, and repositioned the original chapters and produced 14 new chapters of creative and useful machine-learning data mining techniques. In sum, the 31 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. The statistical data mining methods effectively consider big data for identifying structures (variables) with the appropriate predictive power in order to yield reliable and robust large-scale statistical models and analyses. In contrast, the author's own GenIQ Model provides machine-learning solutions to common and virtually unapproachable statistical problems. GenIQ makes this possible — its utilitarian data mining features start where statistical data mining stops. This book contains essays offering detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. They address each methodology and assign its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.

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Author: Prodromou, Theodosia

Publisher: IGI Global

ISBN: 1522525130

Category: Computers

Page: 365

View: 3278

Data visualization has emerged as a serious scholarly topic, and a wide range of tools have recently been developed at an accelerated pace to aid in this research area. Examining different ways of analyzing big data can result in increased efficiency for many corporations and organizations. Data Visualization and Statistical Literacy for Open and Big Data highlights methodological developments in the way that data analytics is both learned and taught. Featuring extensive coverage on emerging relevant topics such as data complexity, statistics education, and curriculum development, this publication is geared toward teachers, academicians, students, engineers, professionals, and researchers that are interested in expanding their knowledge of data examination and analysis.

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Author: Randy Bartlett

Publisher: McGraw Hill Professional

ISBN: 0071807608

Category: Business & Economics

Page: 256

View: 5177

Gain the competitive edge with the smart use of business analytics In today’s volatile business environment, the strategic use of business analytics is more important than ever. A Practitioners Guide to Business Analytics helps you get the organizational commitment you need to get business analytics up and running in your company. It provides solutions for meeting the strategic challenges of applying analytics, such as: Integrating analytics into decision making, corporate culture, and business strategy Leading and organizing analytics within the corporation Applying statistical qualifications, statistical diagnostics, and statistical review Providing effective building blocks to support analytics—statistical software, data collection, and data management Randy Bartlett, Ph.D., is Chief Statistical Officer of the consulting company Blue Sigma Analytics. He currently works with Infosys, where he has helped build their new Business Analytics practice.

The Fundamentals of Management Science

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Author: Dimitris Bertsimas,Robert Michael Freund

Publisher: South-Western Pub

ISBN: N.A

Category: Business & Economics

Page: 530

View: 1374

The book combines topics from two traditionally distinct quantitative subjects: probability/statistics and optimization models, into one unified treatment of quantitative methods and models for management and business. The book stresses those fundamental concepts that are most important for the practical analysis of management decisions: modeling and evaluating uncertainty explicitly, understanding the dynamic nature of decision-making, using historical data and limited information effectively, simulating complex systems, and allocating scarce resources optimally.

What You Need to Know about Data Mining and Data-Analytic Thinking

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Author: Foster Provost,Tom Fawcett

Publisher: "O'Reilly Media, Inc."

ISBN: 144937428X

Category: Computers

Page: 414

View: 8507

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates

A Practical Perspective of What Influences Our Analytical World

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Author: Carlos Andre Reis Pinheiro,Fiona McNeill

Publisher: John Wiley & Sons

ISBN: 1118347609

Category: Business & Economics

Page: 256

View: 2549

Employ heuristic adjustments for truly accurate analysis Heuristics in Analytics presents an approach to analysis that accounts for the randomness of business and the competitive marketplace, creating a model that more accurately reflects the scenario at hand. With an emphasis on the importance of proper analytical tools, the book describes the analytical process from exploratory analysis through model developments, to deployments and possible outcomes. Beginning with an introduction to heuristic concepts, readers will find heuristics applied to statistics and probability, mathematics, stochastic, and artificial intelligence models, ending with the knowledge applications that solve business problems. Case studies illustrate the everyday application and implication of the techniques presented, while the heuristic approach is integrated into analytical modeling, graph analysis, text analytics, and more. Robust analytics has become crucial in the corporate environment, and randomness plays an enormous role in business and the competitive marketplace. Failing to account for randomness can steer a model in an entirely wrong direction, negatively affecting the final outcome and potentially devastating the bottom line. Heuristics in Analytics describes how the heuristic characteristics of analysis can be overcome with problem design, math and statistics, helping readers to: Realize just how random the world is, and how unplanned events can affect analysis Integrate heuristic and analytical approaches to modeling and problem solving Discover how graph analysis is applied in real-world scenarios around the globe Apply analytical knowledge to customer behavior, insolvency prevention, fraud detection, and more Understand how text analytics can be applied to increase the business knowledge Every single factor, no matter how large or how small, must be taken into account when modeling a scenario or event—even the unknowns. The presence or absence of even a single detail can dramatically alter eventual outcomes. From raw data to final report, Heuristics in Analytics contains the information analysts need to improve accuracy, and ultimately, predictive, and descriptive power.

An Applied Approach

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Author: Bruce L. Bowerman,Richard T. O'Connell

Publisher: Brooks/Cole

ISBN: 9780534380182

Category: Mathematics

Page: 1024

View: 6387

The focus of Linear Statistical Models: An Applied Approach, Second Editon, is on the conceptual, concrete, and applied aspects of model building, data analysis, and interpretaion. Without sacrificing depth and breadth of coverage, Bruce L. Bowerman and Richard T. O'Connell's clear and concise explanantions make the material accessible even to those with limited statistical experience.

The New Science of Winning

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Author: Thomas H. Davenport,Jeanne G. Harris

Publisher: Harvard Business Press

ISBN: 1422156303

Category: Business & Economics

Page: 218

View: 3427

You have more information at hand about your business environment than ever before. But are you using it to “out-think” your rivals? If not, you may be missing out on a potent competitive tool. In Competing on Analytics: The New Science of Winning, Thomas H. Davenport and Jeanne G. Harris argue that the frontier for using data to make decisions has shifted dramatically. Certain high-performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. Their secret weapon? Analytics: sophisticated quantitative and statistical analysis and predictive modeling. Exemplars of analytics are using new tools to identify their most profitable customers and offer them the right price, to accelerate product innovation, to optimize supply chains, and to identify the true drivers of financial performance. A wealth of examples—from organizations as diverse as Amazon, Barclay’s, Capital One, Harrah’s, Procter & Gamble, Wachovia, and the Boston Red Sox—illuminate how to leverage the power of analytics.

Visualize Structure and Meaning

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Author: Thomas Frisendal

Publisher: Technics Publications

ISBN: 1634621239

Category: Computers

Page: 214

View: 1674

Master a graph data modeling technique superior to traditional data modeling for both relational and NoSQL databases (graph, document, key-value, and column), leveraging cognitive psychology to improve big data designs. From Karen Lopez’s Foreword: In this book, Thomas Frisendal raises important questions about the continued usefulness of traditional data modeling notations and approaches: Are Entity Relationship Diagrams (ERDs) relevant to analytical data requirements? Are ERDs relevant in the new world of Big Data? Are ERDs still the best way to work with business users to understand their needs? Are Logical and Physical Data Models too closely coupled? Are we correct in using the same notations for communicating with business users and developers? Should we refine our existing notations and tools to meet these new needs, or should we start again from a blank page? What new notations and approaches will we need? How will we use those to build enterprise database systems? Frisendal takes us through the history of data modeling, enterprise data models and traditional modeling methods. He points out, quite contentiously, where he feels we have gone wrong and in a few places where we got it right. He then maps out the psychology of meaning and context, while identifying important issues about where data modeling may or may not fit in business modeling. The main subject of this work is a proposal for a new exploration-driven modeling approach and new modeling notations for business concept models, business solutions models, and physical data models with examples on how to leverage those for implementing into any target database or datastore. These new notations are based on a property graph approach to modeling data.

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Author: RYAN. TERRELL TEETER (KATIE. RICHARDSON, VERNON.),Vernon J. Richardson,Katie L. Terrell

Publisher: N.A

ISBN: 9781260288407

Category: Accounting

Page: N.A

View: 8050

with Applications in R

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Author: Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani

Publisher: Springer Science & Business Media

ISBN: 1461471389

Category: Mathematics

Page: 426

View: 2671

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

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Author: Ding-Geng Chen,Zhezhen Jin,Gang Li,Yi Li,Aiyi Liu,Yichuan Zhao

Publisher: Springer

ISBN: 3319694162

Category: Mathematics

Page: 348

View: 6627

This book is comprised of the presentations delivered at the 25th ICSA Applied Statistics Symposium held at the Hyatt Regency Atlanta, on June 12-15, 2016. This symposium attracted more than 700 statisticians and data scientists working in academia, government, and industry from all over the world. The theme of this conference was the “Challenge of Big Data and Applications of Statistics,” in recognition of the advent of big data era, and the symposium offered opportunities for learning, receiving inspirations from old research ideas and for developing new ones, and for promoting further research collaborations in the data sciences. The invited contributions addressed rich topics closely related to big data analysis in the data sciences, reflecting recent advances and major challenges in statistics, business statistics, and biostatistics. Subsequently, the six editors selected 19 high-quality presentations and invited the speakers to prepare full chapters for this book, which showcases new methods in statistics and data sciences, emerging theories, and case applications from statistics, data science and interdisciplinary fields. The topics covered in the book are timely and have great impact on data sciences, identifying important directions for future research, promoting advanced statistical methods in big data science, and facilitating future collaborations across disciplines and between theory and practice.

A Guide for Everyone Who Has Ever Been Afraid of Statistics

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Author: David M. Levine,David F. Stephan

Publisher: FT Press

ISBN: 9780137025930

Category: Business & Economics

Page: 384

View: 5579

Even You Can Learn Statistics: A Guide for Everyone Who Has Ever Been Afraid of Statisticsi s a practical, up-to-date introduction to statistics—for everyone! Thought you couldn’t learn statistics? You can—and you will! One easy step at a time, this fully updated book teaches you all the statistical techniques you’ll need for finance, quality, marketing, the social sciences, or anything else! Simple jargon-free explanations help you understand every technique. Practical examples and worked-out problems give you hands-on practice. Special sections present detailed instructions for developing statistical answers, using spreadsheet programs or any TI-83/TI-84 compatible calculator. This edition delivers new examples, more detailed problems and sample solutions, plus an all-new chapter on powerful multiple regression techniques. Hate math? No sweat. You’ll be amazed at how little you need. Like math? Optional “Equation Blackboard” sections reveal the mathematical foundations of statistics right before your eyes! You’ll learn how to: • Construct and interpret statistical charts and tables with Excel or OpenOffice.org Calc 3 • Work with mean, median, mode, standard deviation, Z scores, skewness, and other descriptive statistics • Use probability and probability distributions • Work with sampling distributions and confidence intervals • Test hypotheses with Z, t, chi-square, ANOVA, and other techniques • Perform powerful regression analysis and modeling • Use multiple regression to develop models that contain several independent variables • Master specific statistical techniques for quality and Six Sigma programs About the Web Site Download practice files, templates, data sets, and sample spreadsheet models—including ready-to-use solutions for your own work! www.ftpress.com/youcanlearnstatistics2e

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Author: Rob J Hyndman,George Athanasopoulos

Publisher: OTexts

ISBN: 0987507117

Category: Business & Economics

Page: 380

View: 6036

Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.

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Author: Bruce Bowerman

Publisher: McGraw-Hill Higher Education

ISBN: 0077641213

Category: Mathematics

Page: 656

View: 5260

The primary goal of Essentials of Business Statistics is to illustrate an accurate view of business statistics in a way that students can easily understand. This is achieved in the following ways: New statistical topics and tools are introduced by using continuing case studies. This approach helps to alleviate student anxiety in learning new concepts and enhances overall comprehension Streamlined and clarified coverage of graphical and numerical methods New graphically based procedures for finding confidence intervals and performing hypothesis tests Increased emphasis on Excel and MINITAB with improved and updated step-by-step instructions in the end of chapter material Connect Business Statistics homework management