Business Statistics in Practice PDF Download

Author: Bruce Bowerman
Publisher: McGraw-Hill
ISBN: 9781260016499
Category :
Languages : en
Pages : 918

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Author: Bowerman
Publisher: McGraw Hill
ISBN: 0077185013
Category : Business & Economics
Languages : en
Pages : 912

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

Author: Bruce Ratner
Publisher: CRC Press
ISBN: 0203496906
Category : Business & Economics
Languages : en
Pages : 383

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Traditional statistical methods are limited in their ability to meet the modern challenge of mining large amounts of data. Data miners, analysts, and statisticians are searching for innovative new data mining techniques with greater predictive power, an attribute critical for reliable models and analyses. Statistical Modeling and Analysis fo

Author: Gábor Békés
Publisher: Cambridge University Press
ISBN: 1108483011
Category : Business & Economics
Languages : en
Pages : 741

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Book Description
A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data.

Author: Thomas W. Miller
Publisher: Pearson Education
ISBN: 0133886018
Category : Business & Economics
Languages : en
Pages : 376

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Book Description
Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you're new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you're already a modeler, programmer, or manager, it will teach you crucial skills you don't yet have. This guide illuminates the discipline through realistic vignettes and intuitive data visualizations-not complex math. Thomas W. Miller, leader of Northwestern University's pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more. Every chapter focuses on one of today's key applications for predictive analytics, delivering skills and knowledge to put models to work-and maximize their value. Reflecting extensive student and instructor feedback, this edition adds five classroom-tested case studies, updates all code for new versions of R, explains code behavior more clearly and completely, and covers modern data science methods even more effectively.

Author: Bruce L. Bowerman
Publisher:
ISBN: 9781259683824
Category :
Languages : en
Pages : 1666

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Author: BOWERMAN
Publisher:
ISBN: 9781260287844
Category : Business & Economics
Languages : en
Pages : 930

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Book Description
Business Statistics and Analytics in Practice 9e covers standard business statistics and business analytics topics, with a continuous case running throughout chapters, allowing students to use data for a more applied and practical approach to the subject. Topics are clearly organised, giving instructors the choice of whether or not to cover business analytics areas. Featuring Connect, SmartBook, Guided Examples, Algorithmic Problems and a business statistics, maths and Excel prep component, Bowerman is a perfect fit for the instructor who wants a business stats text with business analytics focus.

Author: Peter Bruce
Publisher: "O'Reilly Media, Inc."
ISBN: 1491952911
Category : Computers
Languages : en
Pages : 322

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Book Description
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data

Author: Gordon S. Linoff
Publisher: John Wiley & Sons
ISBN: 0470952520
Category : Computers
Languages : en
Pages : 698

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Book Description
Useful business analysis requires you to effectively transform data into actionable information. This book helps you use SQL and Excel to extract business information from relational databases and use that data to define business dimensions, store transactions about customers, produce results, and more. Each chapter explains when and why to perform a particular type of business analysis in order to obtain useful results, how to design and perform the analysis using SQL and Excel, and what the results should look like.

Author: Johannes Ledolter
Publisher: John Wiley & Sons
ISBN: 1118572157
Category : Mathematics
Languages : en
Pages : 304

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Book Description
Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents: A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools Illustrations of how to use the outlined concepts in real-world situations Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials Numerous exercises to help readers with computing skills and deepen their understanding of the material Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.