Business Statistics in Practice

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: 4279

Business Statistics in Practice: Using Data, Modeling, and Analytics

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

Publisher: McGraw-Hill Education

ISBN: 9781259549465

Category: Business & Economics

Page: 912

View: 4978

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.

Business Statistics in Practice

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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: 3628

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.

Macroeconomics (Canadian Edition)

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Author: N. Gregory Mankiw,William M. Scarth

Publisher: Macmillan

ISBN: 1429234903

Category: Business & Economics

Page: 600

View: 5943

Mankiw's masterful text covers the field as accessibly and concisely as possible, in a way that emphasizes the relevance of macroeconomics's classical roots and its current practice. Featuring the latest data, new case studies focused on recent events, and a number of significant content updates, the Fourth Edition takes the Mankiw legacy even further. It offers the clearest, most up-to-date, most accessible course in macroeconomics in the most concise presentation possible.

Data Visualization and Statistical Literacy for Open and Big Data

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Author: Prodromou, Theodosia

Publisher: IGI Global

ISBN: 1522525130

Category: Computers

Page: 365

View: 6801

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.

Haskell Financial Data Modeling and Predictive Analytics

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Author: Pavel Ryzhov

Publisher: Packt Publishing Ltd

ISBN: 178216944X

Category: Computers

Page: 112

View: 4856

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.

A PRACTITIONER'S GUIDE TO BUSINESS ANALYTICS: Using Data Analysis Tools to Improve Your Organization’s Decision Making and Strategy

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Author: Randy Bartlett

Publisher: McGraw Hill Professional

ISBN: 0071807608

Category: Business & Economics

Page: 256

View: 8703

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.

Linear Statistical Models

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: 5858

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.

Graph Data Modeling for NoSQL and SQL

Visualize Structure and Meaning

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Author: Thomas Frisendal

Publisher: Technics Publications

ISBN: 1634621239

Category: Computers

Page: 214

View: 7212

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.

An Introduction to Statistical Learning

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: 7581

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.

Heuristics in Analytics

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: 1118416740

Category: Business & Economics

Page: 256

View: 9456

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.

Business Analytics Using R - A Practical Approach

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Author: Umesh R Hodeghatta,Umesha Nayak

Publisher: Apress

ISBN: 1484225147

Category: Computers

Page: 280

View: 4745

Learn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated by your organization. This book explains practical business analytics through examples, covers the steps involved in using it correctly, and shows you the context in which a particular technique does not make sense. Further, Practical Business Analytics using R helps you understand specific issues faced by organizations and how the solutions to these issues can be facilitated by business analytics. This book will discuss and explore the following through examples and case studies: An introduction to R: data management and R functions The architecture, framework, and life cycle of a business analytics project Descriptive analytics using R: descriptive statistics and data cleaning Data mining: classification, association rules, and clustering Predictive analytics: simple regression, multiple regression, and logistic regression This book includes case studies on important business analytic techniques, such as classification, association, clustering, and regression. The R language is the statistical tool used to demonstrate the concepts throughout the book. What You Will Learn • Write R programs to handle data • Build analytical models and draw useful inferences from them • Discover the basic concepts of data mining and machine learning • Carry out predictive modeling • Define a business issue as an analytical problem Who This Book Is For Beginners who want to understand and learn the fundamentals of analytics using R. Students, managers, executives, strategy and planning professionals, software professionals, and BI/DW professionals.

Real Estate Analysis in the Information Age

Techniques for Big Data and Statistical Modeling

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Author: Kimberly Winson-Geideman,Andy Krause,Clifford A. Lipscomb,Nick Evangelopoulos

Publisher: Routledge

ISBN: 1315311119

Category: Business & Economics

Page: 164

View: 5095

The creation, accumulation, and use of copious amounts of data are driving rapid change across a wide variety of industries and academic disciplines. This ‘Big Data’ phenomenon is the result of recent developments in computational technology and improved data gathering techniques that have led to substantial innovation in the collection, storage, management, and analysis of data. Real Estate Analysis in the Information Age: Techniques for Big Data and Statistical Modeling focuses on the real estate discipline, guiding researchers and practitioners alike on the use of data-centric methods and analysis from applied and theoretical perspectives.? In it, the authors detail the integration of Big Data into conventional real estate research and analysis. The book is process-oriented, not only describing Big Data and associated methods, but also showing the reader how to use these methods through case studies supported by supplemental online material.? The running theme is the construction of efficient, transparent, and reproducible research through the systematic organization and application of data, both traditional and 'big'.? The final chapters investigate legal issues, particularly related to those data that are publicly available, and conclude by speculating on the future of Big Data in real estate.

Getting Started with Data Science

Making Sense of Data with Analytics

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Author: Murtaza Haider

Publisher: IBM Press

ISBN: 0133991237

Category: Business & Economics

Page: 400

View: 8846

Master Data Analytics Hands-On by Solving Fascinating Problems You’ll Actually Enjoy! Harvard Business Review recently called data science “The Sexiest Job of the 21st Century.” It’s not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it’s indispensable. Unfortunately, there’s been nothing easy about learning data science–until now. Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell’s Outliers: It teaches through a powerful narrative packed with unforgettable stories. Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. Everything’s software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. Every chapter is built around real research challenges, so you’ll always know why you’re doing what you’re doing. You’ll master data science by answering fascinating questions, such as: • Are religious individuals more or less likely to have extramarital affairs? • Do attractive professors get better teaching evaluations? • Does the higher price of cigarettes deter smoking? • What determines housing prices more: lot size or the number of bedrooms? • How do teenagers and older people differ in the way they use social media? • Who is more likely to use online dating services? • Why do some purchase iPhones and others Blackberry devices? • Does the presence of children influence a family’s spending on alcohol? For each problem, you’ll walk through defining your question and the answers you’ll need; exploring how others have approached similar challenges; selecting your data and methods; generating your statistics; organizing your report; and telling your story. Throughout, the focus is squarely on what matters most: transforming data into insights that are clear, accurate, and can be acted upon.

New Advances in Statistics and Data Science

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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: 8776

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.

Forecasting and Time Series

An Applied Approach

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

Publisher: Brooks/Cole Publishing Company

ISBN: 9780534379698

Category: Business & Economics

Page: 726

View: 688

The Third Edition of FORECASTING AND TIME SERIES illustrates the importance of forecasting and the various statistical techniques that can be used to produce forecasts. Bruce L. Bowerman and Richard T. O'Connell clearly demonstrate the necessity of using forecasts to make intelligent decisions in marketing, finance, personnel management, production scheduling, process control, and strategic management.

Loose Leaf for Business Statistics in Practice

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Author: Richard T O'Connell, Professor,Bruce L. Bowerman,Emilly S. Murphree, Professor

Publisher: McGraw-Hill Education

ISBN: 9781260299090

Category: Mathematics

Page: 928

View: 4349

Bowerman 9e covers both standard business statistics and business analytics topics and provides them in a clear presentation that is organized so that business analytics topics may be used or not used. Bowerman provides a continuous case throughout chapters and business analytics topics that allow students to use data for a more applied and practical approach. Featuring Connect, Smartbook, Guided examples, Algorithmic Problems and a Business Statistics, Math and Excel prep component, Bowerman is a perfect fit for the instructor who wants a Business Stats with Business Analytics focus.