Statistics, Data Analysis and Decision Modeling and Student CD-ROM (2nd Edition)

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9780130783837: Statistics, Data Analysis and Decision Modeling and Student CD-ROM (2nd Edition)

For the one semester (or briefer) course in business statistics and quantitative analysis in the MBA or Executive MBA program. This text covers the basic concepts of business statistics, data analysis and management science integrated in a contemporary spreadsheet environment. The authors emphasize practical applications and business decision-making.

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From the Back Cover:

This concise text covers the basic concepts of business statistics, data analysis, and management science in a contemporary spreadsheet environment. The authors emphasize practical applications of the approaches to business decision making. Some of-the text highlights include:

  • Concise approach—This book integrates fundamental concepts of business statistics and decision models in twelve chapters. The authors take a practical, nonmathematical approach.
  • Practical applications—A comprehensive database using marketing, finance, and production data, is used throughout the text to give students hands-on experience in analyzing data for practical decisions.
  • Spreadsheet emphasis—The emphasis is on using spreadsheets for data analysis and decision modeling. The book features Excel and the Excel add-ins: PHStat, student version of Crystal Ball, and TreePlan.

About the CD-ROM

EVERY NEW COPY OF THIS TEXTBOOK IS PACKAGED WITH A CD-ROM CONTAINING SOFTWARE ESSENTIAL FOR THIS COURSE:

  • Premium Solver
  • Crystal Ball—Spreadsheet add-in for simulation and risk analysis
  • TreePlan—Used in chapter 8
  • PHStat2Spreadsheet add—in for statistics

This free CD-ROM also contains all of the Excel files required for many of the examples in this text.

Excerpt. Reprinted by permission. All rights reserved.:

In recent years, we have seen a rather significant trend in business schools as Microsoft Excel and other spreadsheet add-ins have become the principal tool for applications of quantitative methods. Statistics, Data Analysis, and Decision Modeling was initially written to meet the need for an introductory text that provides a basic introduction to business statistics, focusing on practical applications of data analysis and decision modeling. To support this purpose, we have integrated fundamental theory and practical applications in a spreadsheet environment. Spreadsheet add-ins, specifically PHStat, a collection of statistical tools that enhance the capabilities of Excel, published by Pearson Education; the student version of Crystal Ball (including CBPredictor for forecasting and OptQuest for optimization), the most popular commercial package for risk analysis; TreePlan, a decision analysis add-in; and Premium Solver, a more powerful version of Excels Solver, are used for additional analysis capability.

The second edition of this book has been updated and expanded to better meet the needs of our users and provide more flexibility for use in shorter, modular courses. The most significant changes are

  1. Increased coverage of simulation and optimization, resulting in the net addition of three chapters
  2. A more complete coverage of important statistical topics, better aligned with the capabilities of PHStat2
  3. An assortment of new data sets for both illustrative examples and problems
  4. Many new problems and exercises from a variety of disciplines

Users of the first edition will also note that the Tracway scenario and database have been converted to case problems and exercises at the end of each chapter.

The book consists of 12 chapters. The first seven chapters deal with statistical and data analysis topics, while the last five chapters deal with decision modeling and applications.

  • Chapter 1, "Data and Business Decisions," describes the importance of, and types of data used in, business decision making. This chapter also provides the foundation for working with Excel and other add-ins and introduces some fundamental concepts of measurement, sample data, and decision models. The coverage of basic Excel skills has been increased, and new topics such as data tables and PivotTables have been included.
  • Chapter 2, "Displaying and Summarizing Data," focuses on data visualization and descriptive statistics. We have added a new section on contingency tables and cross tabulations, as well as many new examples.
  • Chapter 3, "Random Variables and Probability Distributions," introduces basic concepts of probability distributions, random sampling, sampling distributions, and sampling error. Crystal Ball is introduced and used as a tool for simulating sampling distributions to gain insight into their nature. New material on probability and probability calculations has been added in this edition, as well as a revised treatment of Monte Carlo methods in statistics.
  • Chapter 4, "Sampling and Statistical Inference," addresses sampling methods, statistical analysis of sample data, estimation, and hypothesis testing. This edition provides increased coverage of confidence intervals and hypothesis testing. It also provides an introduction to analysis of variance and distribution fitting using Crystal Ball.
  • Chapter 5, "Regression Analysis," introduces fundamental concepts and methods of both single and multiple regression. Stepwise regression is included in this edition.
  • Chapter 6, "Forecasting," discusses both qualitative and quantitative forecasting methods. These include statistical time series models and applications of regression analysis to forecasting. The topics are arranged to facilitate the use of CBPredictor as an Excel-based tool.
  • Chapter 7, "Statistical Quality Control," develops some applications of statistical concepts presented in the previous chapters to the design and use of control charts and process capability studies.
  • Chapter 8, "Risk Analysis and Monte Carlo Simulation," is the first of two chapters that deal with simulation. In this edition we provide a more complete coverage of Monte Carlo simulation with Crystal Ball and its applications to risk analysis.
  • Chapter 9, "System Simulation Modeling and Analysis," is, new to this edition and provides a basic introduction to concepts of dynamic simulation, all within a spreadsheet environment. This provides students with the ability to gain a solid understanding of simulation concepts, while not requiring additional time to learn a commercial simulation language or software package.
  • Chapter 10, "Selection Models and Decision Analysis," is expanded in this edition to provide a more complete coverage of decision trees (using TreePlan). Other new topics include scoring models, the analytic hierarchy process, and portfolio risk.
  • Chapter 11, "Optimization Modeling," introduces basic concepts of linear, integer, and nonlinear optimization models and spreadsheet formulations. A variety of new examples such as blending, media selection, cash management, distribution center location, and Markowitz portfolio models are included in this edition to provide better insight into the modeling process and applications of optimization.
  • Chapter 12, "Solving and Analyzing Optimization Models," describes the use of Excel Solver and Premium Solver for solving linear, integer, and nonlinear optimization models. This chapter also illustrates how Monte Carlo simulation can provide additional insight about optimization results and how search procedures such as OptQuest can be used in stochastic problem environments.

The first six or seven chapters can be used for a one-quarter or shorter module for introductory statistics. The second part of the book can be used for a follow-up course on decision models and optimization. The book can also be used for a semester course on both topics.

Throughout the book we have placed boxed "Notes" for Excel, PHStat, and Crystal Ball that provide procedural details of using specific functions, tools, or techniques. These provide key information for the student to apply the tools but do not disrupt from the flow of the discussion. The CD-ROM accompanying this book contains all the data and model files used throughout the book, PHStat, the student version of Crystal Ball, TreePlan software and documentation, and Premium Solver.

We would like to thank the following individuals who have provided reviews, suggestions, and guidance during the development of both editions of this book: Samir Barman, University of Oklahoma; James Cochran, University of Cincinnati; Lillian Fok, University of New Orleans; Soumen Ghosh, Georgia Institute of Technology; Jim Grayson, Augusta State University; Peter Jurkat, Stevens Institute of Technology; Ina Markham, James Madison University; Tom McCullough, University of California at Berkeley; David Pentico, Duquesne University; Barbara Price, Georgia Southern University; Jeffrey Rummell, University of Connecticut at Stows; Barbara Russell, Saint Bonaventure University; and Lee Tangedahl, University of Montana.

In addition, we thank our students who provided numerous suggestions, data sets and problem ideas, and insights into how to better present the material. Finally, we express our appreciation to our editor Tom Tucker, and the entire production staff at Pearson Education for their dedication in developing and producing this book. If you have any suggestions or corrections, please contact us care of james.evans@uc.edu.

James R. Evans
David L. Olson

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