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Essentials of Business Analytics 2/e

+作者:

Camm

+年份:
2017 年2 版
+ISBN:
9781305627734
+書號:
PS0451HC
+規格:
精裝/彩色
+頁數:
896
+出版商:
Cengage
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●New Chapters on Probability and Statistical Inference. Chapters 5 and 6 are new to this edition. Chapter 5 covers an introduction to probability for those students who are not familiar with basic probability concepts such as random variables, conditional probability, Bayes’ theorem, and probability distributions. Chapter 6 presents statistical inference topics such as sampling, sampling distributions, interval estimation, and hypothesis testing. These two chapters extend the basic statistical coverage of Essentials of Business Analytics so that the book includes a full coverage of introductory business statistics for students who are unfamiliar with these concepts.
●Expanded Data Mining Coverage. The Data Mining chapter from the first edition has been broken into two chapters: Chapter 4 on Descriptive Data Mining and Chapter 9 on Predictive Data Mining. Chapter 4 on Descriptive Data Mining covers unsupervised learning methods such as clustering and association rules where the user is interested in identifying relationships among observations rather than predicting specific outcome variables. Chapter 4 also covers very important topics related to data preparation including missing data, outliers, and variable representation. Chapter 9 on Predictive Data Mining introduces supervised learning methods that are used to predict an outcome based on a set of input variables. The methods covered in Chapter 9 include logistic regression, k-nearest neighbors clustering, and classification and regression trees.
●New Appendix to Chapter 8. Chapter 8 on Time Series Analysis and Forecasting now includes an appendix on Excel 2016’s new Forecast Sheet tool for implementing Holt-Winters additive seasonal smoothing model.
●First Mindtap for Business Analytics. MindTap is a customizable digital course solution that includes an interactive eBook, autograded exercises from the textbook, and author-created video walkthroughs of key chapter concepts and select examples that use Analytic Solver platform. Students can complete assignments whenever and wherever they are ready to learn with course material specially customized for students by you streamlined in one proven, easy-to-use interface. MindTap gives students a roadmap to master decision-making in business analytics. With an array of resources, tools, and apps -- including videos, practice opportunities, note taking, and flashcards.
●Coverage of Analytic Solver Platform (ASP) Moved to Chapter Appendices. All coverage of the Excel add-in, Analytics Solver Platform, has been moved to the chapter appendices. This means that instructors can now cover all the material in the bodies of the chapters using only native Excel functionality. However, this change makes it easier for an instructor to tailor a course’s coverage of data mining concepts and the execution of these concepts.
●Updates to ASP. All examples, problems, and solutions have been updated in response to changes in the ASP software. Frontline Systems, the developer of ASP, implemented a major rewrite of the code base that powers ASP shortly after the release of the first edition of Essentials of Business Analytics. All the material related to ASP is updated to correspond to Analytic Solver Platform V2016 (16.0.0).
●Incorporation of Excel 2016. Most updates in Excel 2016 are relatively minor as they relate to its use for statistics and analytics. However, Excel 2016 does have new options for creating Charts in Excel and for implementing forecasting methods. Excel 2016 allows for the creation of box plots, tree maps, and several other data visualization tools that could not be created in previous versions of Excel.
●New Style and More Color. The second edition of Essentials of Business Analytics includes full color figures and a new color template throughout the text. This makes much of the material covered much easier for students to interpret and understand.

    ●Step-by-step instructions show students how to use various software programs to perform the analyses discussed in the text. It uses easy-to-use but powerful Excel add-ons such as XL Miner for data mining.
    ●Practical, relevant problems at a variety of difficulty levels help students learn the material. Applications are drawn from all functional business areas: finance, marketing, operations, etc. Data sets are available for most exercises and cases.
    ●Analytics in Action: Each chapter contains an Analytics in Action that present interesting examples of the use of business analytics in practice. The examples are drawn from many different organizations in a variety of areas including healthcare, finance, manufacturing, marketing, and others.
    ●DATAfiles and MODELfiles: All data sets used as examples and in student exercises are also provided online as files available for download by the student. DATAfiles are Excel files that contain data needed for the examples and problems given in the textbook. MODELfiles contain additional modeling features such as extensive use of Excel formulas or the use of Excel Solver or Analytic Solver Platform.
    ●Excel is completely integrated throughout the book, so students learn the latest methods for solving practical problems. It includes step-by-step instructions to help students learn how to use Excel 2016 to apply material in the book. It also includes by-hand calculation approaches to convey insights when this is appropriate.

      Jeffrey D. Camm - Wake Forest University
      James J. Cochran - University of Alabama
      Michael J. Fry - University of Cincinnati
      Jeffrey W. Ohlmann - University of Lowa
      David R. Anderson - University of Cincinnati
      Dennis J. Sweeney - University of Cincinnati
      Thomas A. Williams - Rochester Institue of Technology

      1. Introduction 
      2. Descriptive Statistics.
      3. Data Visualization.
      4. Descriptive Data Mining.
      5. Probability: An Introduction to Modeling Uncertainty.
      6. Statistical Inference.
      7. Linear Regression.
      8. Time Series Analysis and Forecasting.
      9. Predictive Data Mining.
      10. Spreadsheet Models.
      11. Linear Optimization Models.
      12. Integer Linear Optimization Models.
      13. Nonlinear Optimization Models.
      14. Monte Carlo Simulation.
      15. Decision Analysis.
      Appendix A: Basics of Excel.
      Appendix B: Database Basics with Microsoft Access.
      Appendix C: Solutions to Even-Numbered Questions (online).