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Author: Bowerman
Publisher: McGraw Hill
ISBN: 0077185013
Category : Business & Economics
Languages : en
Pages : 912
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Book Description
Ebook: Business Statistics in Practice: Using Data, Modeling and Analytics
Author: Bowerman
Publisher: McGraw Hill
ISBN: 0077185013
Category : Business & Economics
Languages : en
Pages : 912
Get Book Here
Book Description
Ebook: Business Statistics in Practice: Using Data, Modeling and Analytics
Author: Bruce Bowerman
Publisher: McGraw-Hill
ISBN: 9781260016499
Category :
Languages : en
Pages : 918
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Book Description
Author: Ignacio Castillo
Publisher: John Wiley & Sons
ISBN: 1119983223
Category :
Languages : en
Pages : 850
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Book Description
Show students why business statistics is an increasingly important business skill through a student-friendly pedagogy. In this fourth Canadian edition of Business Statistics For Contemporary Decision Making authors Ken Black, Tiffany Bayley, and Ignacio Castillo uses current real-world data to equip students with the business analytics techniques and quantitative decision-making skills required to make smart decisions in today's workplace.
Author: Alexander Holmes
Publisher:
ISBN:
Category : Business & Economics
Languages : en
Pages : 1801
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Book Description
Introductory Business Statistics 2e aligns with the topics and objectives of the typical one-semester statistics course for business, economics, and related majors. The text provides detailed and supportive explanations and extensive step-by-step walkthroughs. The author places a significant emphasis on the development and practical application of formulas so that students have a deeper understanding of their interpretation and application of data. Problems and exercises are largely centered on business topics, though other applications are provided in order to increase relevance and showcase the critical role of statistics in a number of fields and real-world contexts. The second edition retains the organization of the original text. Based on extensive feedback from adopters and students, the revision focused on improving currency and relevance, particularly in examples and problems. This is an adaptation of Introductory Business Statistics 2e by OpenStax. You can access the textbook as pdf for free at openstax.org. Minor editorial changes were made to ensure a better ebook reading experience. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution 4.0 International License.
Author: Richard M. Heiberger
Publisher: Springer Science & Business Media
ISBN: 1475742843
Category : Mathematics
Languages : en
Pages : 739
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Book Description
This presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis. The authors demonstrate how to analyze data—showing code, graphics, and accompanying computer listings. They emphasize how to construct and interpret graphs, discuss principles of graphical design, and show how tabular results are used to confirm the visual impressions derived from the graphs. Many of the graphical formats are novel and appear here for the first time in print.
Author: Ron S. Kenett
Publisher: John Wiley & Sons
ISBN: 1119956722
Category : Business & Economics
Languages : en
Pages : 339
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Book Description
Models and methods for operational risks assessment and mitigation are gaining importance in financial institutions, healthcare organizations, industry, businesses and organisations in general. This book introduces modern Operational Risk Management and describes how various data sources of different types, both numeric and semantic sources such as text can be integrated and analyzed. The book also demonstrates how Operational Risk Management is synergetic to other risk management activities such as Financial Risk Management and Safety Management. Operational Risk Management: a practical approach to intelligent data analysis provides practical and tested methodologies for combining structured and unstructured, semantic-based data, and numeric data, in Operational Risk Management (OpR) data analysis. Key Features: The book is presented in four parts: 1) Introduction to OpR Management, 2) Data for OpR Management, 3) OpR Analytics and 4) OpR Applications and its Integration with other Disciplines. Explores integration of semantic, unstructured textual data, in Operational Risk Management. Provides novel techniques for combining qualitative and quantitative information to assess risks and design mitigation strategies. Presents a comprehensive treatment of "near-misses" data and incidents in Operational Risk Management. Looks at case studies in the financial and industrial sector. Discusses application of ontology engineering to model knowledge used in Operational Risk Management. Many real life examples are presented, mostly based on the MUSING project co-funded by the EU FP6 Information Society Technology Programme. It provides a unique multidisciplinary perspective on the important and evolving topic of Operational Risk Management. The book will be useful to operational risk practitioners, risk managers in banks, hospitals and industry looking for modern approaches to risk management that combine an analysis of structured and unstructured data. The book will also benefit academics interested in research in this field, looking for techniques developed in response to real world problems.
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: 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: Bruce L. Bowerman, Professor
Publisher: McGraw-Hill Education
ISBN: 9781259549465
Category : Business & Economics
Languages : en
Pages : 0
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Book Description
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 Eighth 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.
Author: Max Kuhn
Publisher: Springer Science & Business Media
ISBN: 1461468493
Category : Medical
Languages : en
Pages : 595
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Book Description
Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.
