Statistics, Data Analysis and Decision Modeling and Student CD-ROM / Edition 2

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Overview

This book covers basic concepts of business statistics, data analysis, and management science in a spreadsheet environment. Practical applications are emphasized throughout the book for business decision-making; a comprehensive database is developed, with marketing, financial, and production data already formatted on Excel worksheets. This shows how real data is used and decisions are made. Using Excel as the basic software, and including such add-ins as PHStat2, Crystal Ball, and TreePlan, this book covers a wide variety of topics related to business statistics: statistical thinking in business; displaying and summarizing data; random variables; sampling; regression analysis; forecasting; statistical quality control; risk analysis and Monte-Carlo simulation; systems simulation modeling and analysis; selection models and decision analysis; optimization modeling; and solving and analyzing optimization models. For those employed in the fields of quality control, management science, operations management, statistical science, and those who need to interpret data to make informed business decisions.
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Product Details

  • ISBN-13: 9780130783837
  • Publisher: Pearson
  • Publication date: 8/6/2002
  • Edition description: Older Edition
  • Edition number: 2
  • Pages: 478
  • Product dimensions: 8.04 (w) x 9.86 (h) x 0.78 (d)

Table of Contents

(NOTE: Each chapter begins with an Introduction and includes Questions and Problems.)
1. Data and Business Decisions.
Statistical Thinking in Business. Data in the Business Environment. Sources and Types of Data. Populations and Samples. Decision Models. Using Microsoft Excel. Working with Data in Excel. Case: The Tracway Balanced Scorecard.

2. Displaying and Summarizing Data.
Displaying Data with Charts and Graphs. Contingency Tables and Cross-Tabulations. Descriptive Statistics. Measures of Dispersion. Calculations for Grouped Data. Coefficient of Variation. Measures of Shape. Excel's Descriptive Statistics Tool. Data Profiles and Proportions. Visual Display of Statistical Measures. Statistical Relationships. Case: Descriptive Statistical Analysis of Tracway Data.

3. Random Variables and Probability Distributions.
Basic Concepts. Random Variables. Probability Distributions. Expected Value and Variance of a Random Variable. Discrete Probability Distributions. Continuous Probability Distributions. Other Useful Distributions. Monte-Carlo Methods in Statistics. Sampling Distributions and Sampling Error. Case: Probability Modeling for Tracway Quality Measurements.

4. Sampling and Statistical Inference.
Statistical Sampling. Statistical Analysis of Sample Data. Estimation. Interval Estimates. Confidence Intervals for the Mean. Confidence Intervals for Proportions. Confidence Intervals and Sample Size. Additional Types of Confidence Intervals. Hypothesis Testing. One Sample Hypothesis Tests. Two-Sample Hypothesis Tests. ANOVA: Testing Differences of Several Means. Non Parametric Hypothesis Tests. Distribution Fitting: An Application of HypothesisTesting. Case: Statistical Inference for Tracway.

5. Regression Analysis.
Simple Linear Regression. Measuring Variation About the Regression Line. Regression as Analysis of Variance. Assumptions of Regression Analysis. Application of Regression Analysis to Investment Risk. Multiple Linear Regression. Building Good Regression Models. Regression with Categorical Independent Variables. Regression Models with Nonlinear Terms. Case: Regression Analysis for Tracway.

6. Forecasting.
Qualitative and Judgmental Methods. Indicators and Indexes. Statistical Forecasting Models. Forecasting Models for Stationary Time Series. Forecasting Models with Linear Trends. Models for Time Series with Trend and Seasonal Components. CB Predictor. Regression Models. The Practice of Forecasting.

7. Statistical Quality Control.
The Role of Statistics and Data Analysis in Quality Control. Statistical Process Control. Analyzing Control Charts. Control Charts for Attributes. Statistical Issues in the Design of Control Charts. Process Capability Analysis. Case: Quality Control at Tracway. References.

8. Risk Analysis and Monte-Carlo Simulation.
Risk Analysis. Monte-Carlo Simulation with Crystal Ball. Additional Crystal Ball Modeling and Analysis Options. Applications of Monte-Carlo Simulation.

9. Systems Simulation Modeling and Analysis.
System Simulating Modeling Approaches. Simulating Inventory Systems Using Activity Scanning. Simulating Waiting Line Systems Using Process-Driven Models. Event-Driven Simulation Models. An Event-Driven Inventory Simulation Model. Entities and Attributes. Continuous Simulation Modeling. Case: Tracway Production/Inventory Planning. References.

10. Selection Models and Decision Analysis.
Decisions Involving a Single Alternative. Decisions Involving Non-Mutually Exclusive Alternatives. Decisions Involving Mutually Exclusive Alternatives. Decisions Involving Uncertainty. Case: A Tracway Decision Problem. References.

11. Optimization Modeling.
Constrained Optimization. Linear Optimization Models. Spreadsheet Implementation of Linear Programming Models. Integer Optimization Models. Nonlinear Optimization. Case: Distribution Center Location for Tracway.

12. Solving and Analyzing Optimization Models.
Using Spreadsheet Models for Optimization. Solving Linear Optimization Models. Solving Integer Optimization Models. Solving Nonlinear Optimization Models. Risk Analysis of Optimization Results. Combining Optimization and Simulation. Case: Distribution Center Location for Tracway.

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Preface

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 willalso 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.

Read More Show Less

Introduction

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 thefirst 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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