Fundamentals of Business Statistics is intended to serve as a core textbook for undergraduate students of BBA, BCA, B Com and CA, ICWA and those who need to understand the basic concepts of business statistics and apply results directly to real-life business problems. The book also suits the requirement of students of AMIE, who need both theoretical and practical knowledge of business statistics. The second edition has been extensively revised with the objective of enhancing and strengthening the conceptual, as well as practical knowledge of readers about various techniques of business statistics. Its easy-to-understand approach will enable readers to develop the required skills and apply statistical techniques to decision-making problems. With a completely new look and feel, this book will facilitate the teaching of business statistics techniques as well as enhance the learning experience for students. New in This Edition • Completely revised and reorganized text to make explanations more cogent through relevant and interesting examples. • Large number of new business-oriented solved as well as practice problems representing the various business statistics techniques. • Explanations well illustrated with numerous interesting and varied business-oriented examples. • Pedagogical features like Conceptual Questions, Self Practice Problems with Hints and Answers. • Complete conformity to the latest trends of questions appearing in universities and professional examinations.

(Black & White version) Fundamentals of Business was created for Virginia Tech's MGT 1104 Foundations of Business through a collaboration between the Pamplin College of Business and Virginia Tech Libraries. This book is freely available at: http://hdl.handle.net/10919/70961 It is licensed with a Creative Commons-NonCommercial ShareAlike 3.0 license.

In this edition, efforts have been made to assist readers in converting data into useful information that can be used by decision-makers in making more thoughtful, information-based decisions.

Statistical analysis is essential to business decision-making and management, but the underlying theory of data collection, organization and analysis is one of the most challenging topics for business students and practitioners. This user-friendly text and CD-ROM package will help you to develop strong skills in presenting and interpreting statistical information in a business or management environment. Based entirely on using Microsoft Excel rather than more complicated applications, it includes a clear guide to using Excel with the key functions employed in the book, a glossary of terms and equations, plus a section specifically for those readers who feel rusty in basic maths. Each chapter has worked examples and explanations to illustrate the use of statistics in real life scenarios, with databases for the worked examples, cases and answers on the accompanying CD-ROM.

This edition of Business Statistics is designed specifically for beginning statistics students who require a book on statistics that will explain everything, without leaving out any of the steps in the process. It summarises what students need to know shows how to select the correct technique to solve a problem ' but it doesn't take an overly mathematical approach. Designed to cover a twelve week semester, this text can be used for first year undergraduate students, for those studying at a technical college, or as part of a bridging unit for those wishing to enter university. This updated 2nd edition of Business Statistics features a Solutions Manual and Solution Slides for the instructor. This customised eBook has been created with the content you need for your studies. Due to the process used to produce this customised eBook, it doesn't offer the same functionality available in other Cengage eBooks, including read aloud and copy text.

This book presents a comprehensive and systematic introduction to transforming process-oriented data into information about the underlying business process, which is essential for all kinds of decision-making. To that end, the authors develop step-by-step models and analytical tools for obtaining high-quality data structured in such a way that complex analytical tools can be applied. The main emphasis is on process mining and data mining techniques and the combination of these methods for process-oriented data. After a general introduction to the business intelligence (BI) process and its constituent tasks in chapter 1, chapter 2 discusses different approaches to modeling in BI applications. Chapter 3 is an overview and provides details of data provisioning, including a section on big data. Chapter 4 tackles data description, visualization, and reporting. Chapter 5 introduces data mining techniques for cross-sectional data. Different techniques for the analysis of temporal data are then detailed in Chapter 6. Subsequently, chapter 7 explains techniques for the analysis of process data, followed by the introduction of analysis techniques for multiple BI perspectives in chapter 8. The book closes with a summary and discussion in chapter 9. Throughout the book, (mostly open source) tools are recommended, described and applied; a more detailed survey on tools can be found in the appendix, and a detailed code for the solutions together with instructions on how to install the software used can be found on the accompanying website. Also, all concepts presented are illustrated and selected examples and exercises are provided. The book is suitable for graduate students in computer science, and the dedicated website with examples and solutions makes the book ideal as a textbook for a first course in business intelligence in computer science or business information systems. Additionally, practitioners and industrial developers who are interested in the concepts behind business intelligence will benefit from the clear explanations and many examples.

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, marketing, and 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, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.