Business Statistics in Practice

Using Data, Modeling, and Analytics
Author: Bruce Bowerman,Dr Richard O'Connell,Emilly Murphree
Publisher: McGraw-Hill
ISBN: 9781260016499
Category:
Page: 918
View: 6552
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Business Statistics in Practice: Using Data, Modeling, and Analytics


Author: Bruce Bowerman,Richard O'Connell,Emilly Murphree
Publisher: McGraw-Hill Education
ISBN: 9781259549465
Category: Business & Economics
Page: 912
View: 9332
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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: Using Data, Modeling, and Analytics


Author: Bruce Bowerman
Publisher: McGraw-Hill Higher Education
ISBN: 1259683826
Category:
Page: 864
View: 1525
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Business Statistics in Practice


Author: Bruce L. Bowerman,Richard T. O'Connell,J. B. Orris
Publisher: Irwin Professional Pub
ISBN: 9780072977479
Category: Business & Economics
Page: 881
View: 4054
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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.

Haskell Financial Data Modeling and Predictive Analytics


Author: Pavel Ryzhov
Publisher: Packt Publishing Ltd
ISBN: 178216944X
Category: Computers
Page: 112
View: 5342
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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


Author: Randy Bartlett
Publisher: McGraw Hill Professional
ISBN: 0071807608
Category: Business & Economics
Page: 256
View: 8733
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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
Author: Bruce L. Bowerman,Richard T. O'Connell
Publisher: Brooks/Cole
ISBN: 9780534380182
Category: Mathematics
Page: 1024
View: 4714
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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.

Heuristics in Analytics

A Practical Perspective of What Influences Our Analytical World
Author: Carlos Andre Reis Pinheiro,Fiona McNeill
Publisher: John Wiley & Sons
ISBN: 1118416740
Category: Business & Economics
Page: 256
View: 6841
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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 Introduction to Statistical Learning

with Applications in R
Author: Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani
Publisher: Springer Science & Business Media
ISBN: 1461471389
Category: Mathematics
Page: 426
View: 3518
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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.

Financial Accounting, 6/E


Author: Harrison
Publisher: Pearson Education India
ISBN: 9788131719282
Category:
Page: 752
View: 2030
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Data, Models, and Decisions

The Fundamentals of Management Science
Author: Dimitris Bertsimas,Robert Michael Freund
Publisher: South-Western Pub
ISBN: N.A
Category: Business & Economics
Page: 530
View: 4881
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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.

Forecasting and Time Series

An Applied Approach
Author: Bruce L. Bowerman,Richard T. O'Connell
Publisher: Brooks/Cole Publishing Company
ISBN: 9780534379698
Category: Business & Economics
Page: 726
View: 4230
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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.

New Advances in Statistics and Data Science


Author: Ding-Geng Chen,Zhezhen Jin,Gang Li,Yi Li,Aiyi Liu,Yichuan Zhao
Publisher: Springer
ISBN: 3319694162
Category: Mathematics
Page: 348
View: 2892
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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, Time Series, and Regression

An Applied Approach
Author: Richard T. O'Connell,Anne B. Koehler
Publisher: South-Western Pub
ISBN: 9780534409777
Category: Mathematics
Page: 686
View: 6835
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Awarded Outstanding Academic Book by CHOICE magazine in its first edition, FORECASTING, TIME SERIES, AND REGRESSION: AN APPLIED APPROACH illustrates the vital importance of forecasting and the various statistical techniques that can be used to produce them. With an emphasis on applications, this book provides both the conceptual development and practical motivation you need to effectively implement forecasts of your own. You'll understand why using forecasts to make intelligent decisions in marketing, finance, personnel management, production scheduling, process control, and strategic management is so vital.

Practical Predictive Analytics and Decisioning Systems for Medicine

Informatics Accuracy and Cost-Effectiveness for Healthcare Administration and Delivery Including Medical Research
Author: Linda Miner,Pat Bolding,Joseph Hilbe,Mitchell Goldstein,Thomas Hill,Robert Nisbet,Nephi Walton,Gary Miner
Publisher: Academic Press
ISBN: 012411640X
Category: Computers
Page: 1110
View: 7310
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With the advent of electronic medical records years ago and the increasing capabilities of computers, our healthcare systems are sitting on growing mountains of data. Not only does the data grow from patient volume but the type of data we store is also growing exponentially. Practical Predictive Analytics and Decisioning Systems for Medicine provides research tools to analyze these large amounts of data and addresses some of the most pressing issues and challenges where data integrity is compromised: patient safety, patient communication, and patient information. Through the use of predictive analytic models and applications, this book is an invaluable resource to predict more accurate outcomes to help improve quality care in the healthcare and medical industries in the most cost–efficient manner. Practical Predictive Analytics and Decisioning Systems for Medicine provides the basics of predictive analytics for those new to the area and focuses on general philosophy and activities in the healthcare and medical system. It explains why predictive models are important, and how they can be applied to the predictive analysis process in order to solve real industry problems. Researchers need this valuable resource to improve data analysis skills and make more accurate and cost-effective decisions. Includes models and applications of predictive analytics why they are important and how they can be used in healthcare and medical research Provides real world step-by-step tutorials to help beginners understand how the predictive analytic processes works and to successfully do the computations Demonstrates methods to help sort through data to make better observations and allow you to make better predictions

Predictive Analytics for Marketers

Using Data Mining for Business Advantage
Author: Barry Leventhal
Publisher: Kogan Page Publishers
ISBN: 0749479949
Category: Business & Economics
Page: 272
View: 2297
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Predictive analytics has revolutionized marketing practice. It involves using many techniques from data mining, statistics, modelling, machine learning and artificial intelligence, to analyse current data and make predictions about unknown future events. In business terms, this enables companies to forecast consumer behaviour and much more. Predictive Analytics for Marketers will guide marketing professionals on how to apply predictive analytical tools to streamline business practices. Including comprehensive coverage of an array of predictive analytic tools and techniques, this book enables readers to harness patterns from past data, to make accurate and useful predictions that can be converted to business success. Truly global in its approach, the insights these techniques offer can be used to manage resources more effectively across all industries and sectors. Written in clear, non-technical language, Predictive Analytics for Marketers contains case studies from the author's more than 25 years of experience and articles from guest contributors, demonstrating how predictive analytics has been used to successfully achieve a range of business purposes.

Forecasting: principles and practice


Author: Rob J Hyndman,George Athanasopoulos
Publisher: OTexts
ISBN: 0987507117
Category: Business & Economics
Page: 380
View: 9566
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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.

Python for Data Analysis

Data Wrangling with Pandas, NumPy, and IPython
Author: Wes McKinney
Publisher: "O'Reilly Media, Inc."
ISBN: 1491957611
Category: Computers
Page: 550
View: 765
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Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

Business Analytics Using R - A Practical Approach


Author: Umesh R Hodeghatta,Umesha Nayak
Publisher: Apress
ISBN: 1484225147
Category: Computers
Page: 280
View: 2893
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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.