PHStat Notes Using the PHStat Stack Data and .docx
- 1. PHStat Notes Using the PHStat Stack Data and Unstack Data Tools p. 28 One‐ and Two‐Way Tables and Charts p. 63 Normal Probability Tools p. 97 Generating Probabilities in PHStat p. 98 Confi dence Intervals for the Mean p. 136 Confi dence Intervals for Proportions p. 136 Confi dence Intervals for the Population Variance p. 137 Determining Sample Size p. 137 One‐Sample Test for the Mean, Sigma Unknown p. 169 One‐Sample Test for Proportions p. 169 Using Two‐Sample t ‐Test Tools p. 169 Testing for Equality of Variances p. 170 Chi‐Square Test for Independence p. 171 Using Regression Tools p. 209 Stepwise Regression p. 211 Best-Subsets Regression p. 212 Creating x ‐ and R ‐Charts p. 267 Creating p ‐Charts p. 268 Using the Expected Monetary Value Tool p. 375 Excel Notes Creating Charts in Excel 2010 p. 29 Creating a Frequency Distribution and Histogram p. 61 Using the Descriptive Statistics Tool p. 61 Using the Correlation Tool p. 62 Creating Box Plots p. 63
- 2. Creating PivotTables p. 63 Excel‐Based Random Sampling Tools p. 134 Using the VLOOKUP Function p. 135 Sampling from Probability Distributions p. 135 Single‐Factor Analysis of Variance p. 171 Using the Trendline Option p. 209 Using Regression Tools p. 209 Using the Correlation Tool p. 211 Forecasting with Moving Averages p. 243 Forecasting with Exponential Smoothing p. 243 Using CB Predictor p. 244 Creating Data Tables p. 298 Data Table Dialog p. 298 Using the Scenario Manager p. 298 Using Goal Seek p. 299 Net Present Value and the NPV Function p. 299 Using the IRR Function p. 375 Crystal Ball Notes Customizing Defi ne Assumption p. 338 Sensitivity Charts p. 339 Distribution Fitting with Crystal Ball p. 339 Correlation Matrix Tool p. 341 Tornado Charts p. 341 Bootstrap Tool p. 342 TreePlan Note Constructing Decision Trees in Excel p. 376 This page intentionally left blank
- 3. Useful Statistical Functions in Excel 2010 Description AVERAGE( data range ) Computes the average value (arithmetic mean) of a set of data. BINOM.DIST( number_s, trials, probability_s, cumulative ) Returns the individual term binomial distribution. BINOM.INV( trials, probability_s, alpha) CHISQ.DIST( x, deg_freedom, cumulative ) CHISQ.DIST.RT( x, deg_freedom, cumulative ) CHISQ.TEST( actual_range, expected_range ) Returns the smallest value for which the cumulative binomial distribution is greater than or equal to a criterion value. Returns the left-tailed probability of the chi-square distribution. Returns the right-tailed probability of the chi-square distribution. Returns the test for independence; the value of the chi-square distribution and the appropriate degrees of freedom. CONFIDENCE.NORM( alpha, standard_dev, size ) Returns the confidence interval for a population mean using a normal distribution. CONFIDENCE.T( alpha, standard_dev, size ) CORREL( arrayl, array2 ) Returns the confidence interval for a population mean using a t -distribution. Computes the correlation coefficient between two data sets. EXPON.DIST( x, lambda, cumulative ) Returns the
- 4. exponential distribution. F.DIST( x. deg_freedom1, deg_freedom2, cumulative ) F.DIST.RT( x. deg_freedom1, deg_freedom2, cumulative ) FORECAST( x, known_y's, known_x's ) Returns the left-tailed F -probability distribution value . Returns the left-tailed F-probability distribution value . Calculates a future value along a linear trend. GROWTH( known_y's, known_x's, new_x's, constant) Calculates predicted exponential growth . LINEST (known_y's, known_x's, new_x's, constant, stats ) Returns an array that describes a straight line that best fits the data. LOGNORM.DIST( x, mean, standard_deviation ) Returns the cumulative lognormal distribution of x , where ln ( x ) is normally distributed with parameters mean and standard deviation. MEDIAN( data range ) Computes the median (middle value) of a set of data. MODE.MULT( data range ) Computes the modes (most frequently occurring values) of a set of data. MODE.SNGL( data range ) NORM.DIST( x, mean, standard_dev, cumulative ) Computes the mode of a set of data. Returns the normal cumulative distribution for the specified mean and standard deviation. NORM.INV( probability, mean, standard_dev ) NORM.S.DIST( z )
- 5. Returns the inverse of the cumulative normal distribution. Returns the standard normal cumulative distribution (mean = 0, standard deviation = 1). NORM.S.INV( probability ) PERCENTILE.EXC( array, k ) PERCENTILE.INC( array, k ) Returns the inverse of the standard normal distribution. Computes the kth percentile of data in a range, exclusive. Computes the kth percentile of data in a range, inclusive. POISSON.DIST( x, mean, cumulative ) Returns the Poisson distribution. QUARTILE( array, quart ) Computes the quartile of a distribution. SKEW( data range ) Computes the skewness, a measure of the degree to which a distribution is not symmetric around its mean. STANDARDIZE( x, mean, standard_deviation ) Returns a normalized value for a distribution characterized by a mean and standard deviation. STDEV.S( data range ) Computes the standard deviation of a set of data, assumed to be a sample. STDEV.P( data range ) Computes the standard deviation of a set of data, assumed to be an entire population. TREND( known_y's, known_x's, new_x's, constant ) Returns values along a linear trend line. T.DIST( x, deg_freedom, cumulative ) T.DIST.2T( x, deg_freedom )
- 6. T.DIST.RT( x, deg_freedom ) T.INV( probability, deg_freedom ) T.INV.2T( probability, deg_freedom ) T.TEST( arrayl, array2, tails, type ) Returns the left-tailed t -distribution value. Returns the two-tailed t -distribution value. Returns the right-tailed t -distribution. Returns the left-tailed inverse of the t -distribution. Returns the two-tailed inverse of the t -distribution. Returns the probability associated with a t -test. VAR.S( data range ) Computes the variance of a set of data, assumed to be a sample. VAR.P( data range ) Computes the variance of a set of data, assumed to be an entire population. Z.TEST( array, x, sigma ) Returns the two-tailed p -value of a z -test. This page intentionally left blank Fifth Edition STATISTICS, DATA ANALYSIS, AND DECISION MODELING James R. Evans University of Cincinnati Boston Columbus Indianapolis New York San Francisco Upper
- 7. Saddle River Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto Delhi Mexico City São Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo Editorial Director: Sally Yagan Editor in Chief: Donna Battista Senior Acquisitions Editor: Chuck Synovec Editorial Project Manager: Mary Kate Murray Editorial Assistant: Ashlee Bradbury Director of Marketing: Maggie Moylan Executive Marketing Manager: Anne Fahlgren Production Project Manager: Renata Butera Operations Specialist: Renata Butera Creative Art Director: Jayne Conte Cover Designer: Suzanne Duda Manager, Rights and Permissions: Hessa Albader Cover Art: pedrosek/Shutterstock Images Media Project Manager: John Cassar Media Editor: Sarah Peterson Full-Service Project Management: Shylaja Gatttupalli Composition: Jouve India Pvt Ltd Printer/Binder: Edwards Brothers Cover Printer: Lehigh-Phoenix Color/Hagerstown Text Font: Palatino Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook appear on the appropriate page within text.
- 8. Microsoft and/or its respective suppliers make no representations about the suitability of the information contained in the documents and related graphics published as part of the services for any purpose. All such documents and related graphics are provided “as is” without warranty of any kind. Microsoft and/ or its respective suppliers hereby disclaim all warranties and conditions with regard to this information, including all warranties and conditions of merchantability, whether express, implied or statutory, fitness for a particular purpose, title and non-infringement. In no event shall Microsoft and/or its respective suppliers be liable for any special, indirect or consequential damages or any damages whatsoever resulting from loss of use, data or profits, whether in an action of contract, negligence or other tortious action, arising out of or in connection with the use or performance of information available from the services. The documents and related graphics contained herein could include technical inaccuracies or typographical errors. Changes are periodically added to the information herein. Microsoft and/or its respective suppliers may make improvements and/or changes in the product(s) and/or the program(s) described herein at any time. Partial screen shots may be viewed in full within the software version specified. Microsoft® and Windows® are registered trademarks of the Microsoft Corporation in the U.S.A. and other countries. This book is not sponsored or endorsed by or affiliated with the Microsoft Corporation. Copyright © 2013, 2010, 2007, 2003, 2000 by Pearson Education, Inc., publishing as Prentice Hall. All rights reserved. Manufactured in the United States of
- 9. America. This publication is protected by Copyright, and permission should be obtained from the publisher prior to any prohibited reproduction, storage in a retrieval system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording, or likewise. To obtain permission(s) to use material from this work, please submit a written request to Pearson Education, Inc., Permissions Department, One Lake Street, Upper Saddle River, New Jersey 07458, or you may fax your request to 201-236-3290. Many of the designations by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and the publisher was aware of a trademark claim, the designations have been printed in initial caps or all caps. Library of Congress Cataloging-in-Publication Data Evans, James R. (James Robert) Statistics, data analysis, and decision modeling / James R. Evans. —5th ed. p. cm. ISBN-13: 978-0-13-274428-7 ISBN-10: 0-13-274428-7 1. Industrial management—Statistical methods. 2. Statistical decision. I. Title. HD30.215.E93 2012 658.4r033—dc23 2011039310 10 9 8 7 6 5 4 3 2 1
- 10. ISBN 10: 0-13-274428-7 ISBN 13: 978-0-13-274428-7 To Beverly, Kristin, and Lauren, the three special women in my life. — James R. Evans This page intentionally left blank vii BRIEF CONTENTS PART I Statistics and Data Analysis 1 Chapter 1 Data and Business Decisions 3 Chapter 2 Descriptive Statistics and Data Analysis 31 Chapter 3 Probability Concepts and Distributions 65 Chapter 4 Sampling and Estimation 99 Chapter 5 Hypothesis Testing and Statistical Inference 138 Chapter 6 Regression Analysis 172 Chapter 7 Forecasting 213 Chapter 8 Introduction to Statistical Quality Control 248
- 11. PART II Decision Modeling and Analysis 269 Chapter 9 Building and Using Decision Models 271 Chapter 10 Decision Models with Uncertainty and Risk 300 Chapter 11 Decisions, Uncertainty, and Risk 343 Chapter 12 Queues and Process Simulation Modeling 378 Chapter 13 Linear Optimization 411 Chapter 14 Integer, Nonlinear, and Advanced Optimization Methods 458 Appendix 509 Index 521 This page intentionally left blank CONTENTS Preface xxi Part I STATISTICS AND DATA ANALYSIS 1 Chapter 1 DATA AND BUSINESS DECISIONS 3 Introduction 4 Data in the Business Environment 4 Sources and Types of Data 6
- 12. Metrics and Data Classification 7 Statistical Thinking 11 Populations and Samples 12 Using Microsoft Excel 13 Basic Excel Skills 14 Skill‐Builder Exercise 1.1 14 Copying Formulas and Cell References 14 Skill‐Builder Exercise 1.2 15 Functions 16 Skill‐Builder Exercise 1.3 18 Other Useful Excel Tips 18 Excel Add‐Ins 19 Skill‐Builder Exercise 1.4 20 Displaying Data with Excel Charts 21 Column and Bar Charts 21 Skill‐Builder Exercise 1.5 22 Line Charts 23 Skill‐Builder Exercise 1.6 23
- 13. Pie Charts 23 Skill‐Builder Exercise 1.7 23 Area Charts 24 Scatter Diagrams 24 Skill‐Builder Exercise 1.8 24 Miscellaneous Excel Charts 25 Ethics and Data Presentation 25 Skill‐Builder Exercise 1.9 26 Basic Concepts Review Questions 27 Problems and Applications 27 Case: A Data Collection and Analysis Project 28 ix x Contents Chapter 2 DESCRIPTIVE STATISTICS AND DATA ANALYSIS 31 Introduction 32 Descriptive Statistics 32 Frequency Distributions, Histograms, and Data Profiles 33
- 14. Categorical Data 34 Numerical Data 34 Skill‐Builder Exercise 2.1 38 Skill‐Builder Exercise 2.2 38 Data Profiles 38 Descriptive Statistics for Numerical Data 39 Measures of Location 39 Measures of Dispersion 40 Skill‐Builder Exercise 2.3 42 Measures of Shape 43 Excel Descriptive Statistics Tool 44 Skill‐Builder Exercise 2.4 44 Measures of Association 45 Skill‐Builder Exercise 2.5 47 Descriptive Statistics for Categorical Data 47 Skill‐Builder Exercise 2.6 48 Visual Display of Statistical Measures 49 Box Plots 49
- 15. Dot‐Scale Diagrams 49 Skill‐Builder Exercise 2.7 49 Outliers 50 Data Analysis Using PivotTables 50 Skill‐Builder Exercise 2.8 53 Skill‐Builder Exercise 2.9 53 Basic Concepts Review Questions 54 Problems and Applications 54 Case: The Malcolm Baldrige Award 57 Skill‐Builder Exercise 2.10 59 Skill‐Builder Exercise 2.11 60 Chapter 3 PROBABILITY CONCEPTS AND DISTRIBUTIONS 65 Introduction 66 Basic Concepts of Probability 66 Basic Probability Rules and Formulas 67 Conditional Probability 68 Skill‐Builder Exercise 3.1 70 Random Variables and Probability Distributions 70
- 16. Discrete Probability Distributions 73 Expected Value and Variance of a Discrete Random Variable 74 Contents xi Skill‐Builder Exercise 3.2 75 Bernoulli Distribution 75 Binomial Distribution 75 Poisson Distribution 76 Skill‐Builder Exercise 3.3 78 Continuous Probability Distributions 78 Uniform Distribution 80 Normal Distribution 81 Skill‐Builder Exercise 3.4 84 Triangular Distribution 84 Exponential Distribution 85 Probability Distributions in PHStat 86 Other Useful Distributions 86
- 17. Joint and Marginal Probability Distributions 89 Basic Concepts Review Questions 90 Problems and Applications 90 Case: Probability Analysis for Quality Measurements 94 Chapter 4 SAMPLING AND ESTIMATION 99 Introduction 100 Statistical Sampling 100 Sample Design 100 Sampling Methods 101 Errors in Sampling 103 Random Sampling From Probability Distributions 103 Sampling From Discrete Probability Distributions 104 Skill‐Builder Exercise 4.1 105 Sampling From Common Probability Distributions 105 A Statistical Sampling Experiment in Finance 106 Skill‐Builder Exercise 4.2 106 Sampling Distributions and Sampling Error 107 Skill‐Builder Exercise 4.3 110 Applying the Sampling Distribution of the Mean 110
- 18. Sampling and Estimation 110 Point Estimates 111 Unbiased Estimators 112 Skill‐Builder Exercise 4.4 113 Interval Estimates 113 Confidence Intervals: Concepts and Applications 113 Confidence Interval for the Mean with Known Population Standard Deviation 114 Skill‐Builder Exercise 4.5 116 xii Contents Confidence Interval for the Mean with Unknown Population Standard Deviation 116 Confidence Interval for a Proportion 118 Confidence Intervals for the Variance and Standard Deviation 119 Confidence Interval for a Population Total 121 Using Confidence Intervals for Decision Making 122 Confidence Intervals and Sample Size 122
- 19. Prediction Intervals 124 Additional Types of Confidence Intervals 125 Differences Between Means, Independent Samples 125 Differences Between Means, Paired Samples 125 Differences Between Proportions 126 Basic Concepts Review Questions 126 Problems and Applications 126 Case: Analyzing a Customer Survey 129 Skill‐Builder Exercise 4.6 131 Skill‐Builder Exercise 4.7 132 Skill‐Builder Exercise 4.8 133 Skill‐Builder Exercise 4.9 133 Chapter 5 HYPOTHESIS TESTING AND STATISTICAL INFERENCE 138 Introduction 139 Basic Concepts of Hypothesis Testing 139 Hypothesis Formulation 140 Significance Level 141 Decision Rules 142 Spreadsheet Support for Hypothesis Testing 145 One‐Sample Hypothesis Tests 145 One‐Sample Tests for Means 145
- 20. Using p ‐Values 147 One‐Sample Tests for Proportions 148 One Sample Test for the Variance 150 Type II Errors and the Power of A Test 151 Skill‐Builder Exercise 5.1 153 Two‐Sample Hypothesis Tests 153 Two‐Sample Tests for Means 153 Two‐Sample Test for Means with Paired Samples 155 Two‐Sample Tests for Proportions 155 Hypothesis Tests and Confidence Intervals 156 Test for Equality of Variances 157 Skill‐Builder Exercise 5.2 158 Anova: Testing Differences of Several Means 158 Assumptions of ANOVA 160 Tukey–Kramer Multiple Comparison Procedure 160 Contents xiii Chi‐Square Test for Independence 162 Skill‐Builder Exercise 5.3 164
- 21. Basic Concepts Review Questions 164 Problems and Applications 164 Case: HATCO, Inc. 167 Skill‐Builder Exercise 5.4 169 Chapter 6 REGRESSION ANALYSIS 172 Introduction 173 Simple Linear Regression 174 Skill‐Builder Exercise 6.1 175 Least‐Squares Regression 176 Skill‐Builder Exercise 6.2 178 A Practical Application of Simple Regression to Investment Risk 178 Simple Linear Regression in Excel 179 Skill‐Builder Exercise 6.3 180 Regression Statistics 180 Regression as Analysis of Variance 181 Testing Hypotheses for Regression Coefficients 181 Confidence Intervals for Regression Coefficients 182 Confidence and Prediction Intervals for X ‐Values 182
- 22. Residual Analysis and Regression Assumptions 182 Standard Residuals 184 Skill‐Builder Exercise 6.4 184 Checking Assumptions 184 Multiple Linear Regression 186 Skill‐Builder Exercise 6.5 186 Interpreting Results from Multiple Linear Regression 188 Correlation and Multicollinearity 188 Building Good Regression Models 190 Stepwise Regression 193 Skill‐Builder Exercise 6.6 193 Best‐Subsets Regression 193 The Art of Model Building in Regression 194 Regression with Categorical Independent Variables 196 Categorical Variables with More Than Two Levels 199 Skill‐Builder Exercise 6.7 201 Regression Models with Nonlinear Terms 201 Skill‐Builder Exercise 6.8 202
- 23. Basic Concepts Review Questions 204 Problems and Applications 204 Case: Hatco 207 xiv Contents Chapter 7 FORECASTING 213 Introduction 214 Qualitative and Judgmental Methods 214 Historical Analogy 215 The Delphi Method 215 Indicators and Indexes for Forecasting 215 Statistical Forecasting Models 216 Forecasting Models for Stationary Time Series 218 Moving Average Models 218 Error Metrics and Forecast Accuracy 220 Skill‐Builder Exercise 7.1 222 Exponential Smoothing Models 222 Skill‐Builder Exercise 7.2 224
- 24. Forecasting Models for Time Series with a Linear Trend 224 Regression‐Based Forecasting 224 Advanced Forecasting Models 225 Autoregressive Forecasting Models 226 Skill‐Builder Exercise 7.3 228 Forecasting Models with Seasonality 228 Incorporating Seasonality in Regression Models 229 Skill‐Builder Exercise 7.4 231 Forecasting Models with Trend and Seasonality 231 Regression Forecasting with Causal Variables 231 Choosing and Optimizing Forecasting Models Using CB Predictor 233 Skill‐Builder Exercise 7.5 235 The Practice of Forecasting 238 Basic Concepts Review Questions 239 Problems and Applications 240 Case: Energy Forecasting 241 Chapter 8 INTRODUCTION TO STATISTICAL QUALITY CONTROL 248 Introduction 248
- 25. The Role of Statistics and Data Analysis in Quality Control 249 Statistical Process Control 250 Control Charts 250 x ‐ and R ‐Charts 251 Skill‐Builder Exercise 8.1 256 Analyzing Control Charts 256 Sudden Shift in the Process Average 257 Cycles 257 Trends 257 Contents xv Hugging the Center Line 257 Hugging the Control Limits 258 Skill‐Builder Exercise 8.2 258 Skill‐Builder Exercise 8.3 260 Control Charts for Attributes 260 Variable Sample Size 262
- 26. Skill‐Builder Exercise 8.4 264 Process Capability Analysis 264 Skill‐Builder Exercise 8.5 266 Basic Concepts Review Questions 266 Problems and Applications 266 Case: Quality Control Analysis 267 Part II Decision Modeling and Analysis 269 Chapter 9 BUILDING AND USING DECISION MODELS 271 Introduction 271 Decision Models 272 Model Analysis 275 What‐If Analysis 275 Skill‐Builder Exercise 9.1 277 Skill‐Builder Exercise 9.2 278 Skill‐Builder Exercise 9.3 278 Model Optimization 278 Tools for Model Building 280 Logic and Business Principles 280
- 27. Skill‐Builder Exercise 9.4 281 Common Mathematical Functions 281 Data Fitting 282 Skill‐Builder Exercise 9.5 284 Spreadsheet Engineering 284 Skill‐Builder Exercise 9.6 285 Spreadsheet Modeling Examples 285 New Product Development 285 Skill‐Builder Exercise 9.7 287 Single Period Purchase Decisions 287 Overbooking Decisions 288 Project Management 289 Model Assumptions, Complexity, and Realism 291 Skill‐Builder Exercise 9.8 293 Basic Concepts Review Questions 293 Problems and Applications 294 Case: An Inventory Management Decision Model 297
- 28. xvi Contents Chapter 10 DECISION MODELS WITH UNCERTAINTY AND RISK 300 Introduction 301 Spreadsheet Models with Random Variables 301 Monte Carlo Simulation 302 Skill‐Builder Exercise 10.1 303 Monte Carlo Simulation Using Crystal Ball 303 Defining Uncertain Model Inputs 304 Running a Simulation 308 Saving Crystal Ball Runs 310 Analyzing Results 310 Skill‐Builder Exercise 10.2 314 Crystal Ball Charts 315 Crystal Ball Reports and Data Extraction 318 Crystal Ball Functions and Tools 318 Applications of Monte Carlo Simulation and Crystal Ball Features 319 Newsvendor Model: Fitting Input Distributions, Decision Table Tool, and Custom Distribution 319
- 29. Skill‐Builder Exercise 10.3 323 Skill‐Builder Exercise 10.4 324 Overbooking Model: Crystal Ball Functions 324 Skill‐Builder Exercise 10.5 325 Cash Budgeting: Correlated Assumptions 325 New Product Introduction: Tornado Chart Tool 328 Skill‐Builder Exercise 10.6 329 Project Management: Alternate Input Parameters and the Bootstrap Tool 329 Skill‐Builder Exercise 10.7 334 Basic Concepts Review Questions 334 Problems and Applications 335 Case: J&G Bank 338 Chapter 11 DECISIONS, UNCERTAINTY, AND RISK 343 Introduction 344 Decision Making Under Certainty 344 Decisions Involving a Single Alternative 345 Skill‐Builder Exercise 11.1 345 Decisions Involving Non–mutually Exclusive Alternatives
- 30. 345 Decisions Involving Mutually Exclusive Alternatives 346 Decisions Involving Uncertainty and Risk 347 Making Decisions with Uncertain Information 347 Decision Strategies for a Minimize Objective 348 Contents xvii Skill‐Builder Exercise 11.2 350 Decision Strategies for a Maximize Objective 350 Risk and Variability 351 Expected Value Decision Making 353 Analysis of Portfolio Risk 354 Skill‐Builder Exercise 11.3 356 The “Flaw of Averages” 356 Skill‐Builder Exercise 11.4 356 Decision Trees 357 A Pharmaceutical R&D Model 357 Decision Trees and Risk 358
- 31. Sensitivity Analysis in Decision Trees 360 Skill‐Builder Exercise 11.5 360 The Value of Information 360 Decisions with Sample Information 362 Conditional Probabilities and Bayes’s Rule 363 Utility and Decision Making 365 Skill‐Builder Exercise 11.6 368 Exponential Utility Functions 369 Skill‐Builder Exercise 11.7 370 Basic Concepts Review Questions 370 Problems and Applications 371 Case: The Sandwich Decision 375 Chapter 12 QUEUES AND PROCESS SIMULATION MODELING 378 Introduction 378 Queues and Queuing Systems 379 Basic Concepts of Queuing Systems 379 Customer Characteristics 380 Service Characteristics 381
- 32. Queue Characteristics 381 System Configuration 381 Performance Measures 382 Analytical Queuing Models 382 Single‐Server Model 383 Skill‐Builder Exercise 12.1 384 Little’s Law 384 Process Simulation Concepts 385 Skill‐Builder Exercise 12.2 386 Process Simulation with SimQuick 386 Getting Started with SimQuick 387 A Queuing Simulation Model 388 xviii Contents Skill‐Builder Exercise 12.3 392 Queues in Series with Blocking 393 Grocery Store Checkout Model with Resources 394 Manufacturing Inspection Model with Decision Points 397
- 33. Pull System Supply Chain with Exit Schedules 400 Other SimQuick Features and Commercial Simulation Software 402 Continuous Simulation Modeling 403 Basic Concepts Review Questions 406 Problems and Applications 407 Case: Production/Inventory Planning 410 Chapter 13 LINEAR OPTIMIZATION 411 Introduction 411 Building Linear Optimization Models 412 Characteristics of Linear Optimization Models 415 Implementing Linear Optimization Models on Spreadsheets 416 Excel Functions to Avoid in Modeling Linear Programs 417 Solving Linear Optimization Models 418 Solving the SSC Model Using Standard Solver 418 Solving the SSC Model Using Premium Solver 420 Solver Outcomes and
- 34. Solution Messages 422 Interpreting Solver Reports 422 Skill‐Builder Exercise 13.1 426 How Solver Creates Names in Reports 427 Difficulties with Solver 427 Applications of Linear Optimization 427 Process Selection 429 Skill‐Builder Exercise 13.2 430 Blending 430 Skill‐Builder Exercise 13.3 432 Portfolio Investment 432
- 35. Skill‐Builder Exercise 13.4 433 Transportation Problem 433 Interpreting Reduced Costs 437 Multiperiod Production Planning 437 Skill‐Builder Exercise 13.5 439 Multiperiod Financial Planning 439 Skill‐Builder Exercise 13.6 440 A Model with Bounded Variables 440 A Production/Marketing Allocation Model 445 How Solver Works 449 Basic Concepts Review Questions 450
- 36. Contents xix Problems and Applications 450 Case: Haller’s Pub & Brewery 457 Chapter 14 INTEGER, NONLINEAR, AND ADVANCED OPTIMIZATION METHODS 458 Introduction 458 Integer Optimization Models 459 A Cutting Stock Problem 459 Solving Integer Optimization Models 460 Skill‐Builder Exercise 14.1 462 Integer Optimization Models with Binary Variables 463 Project Selection 463 Site Location Model 464
- 37. Skill‐Builder Exercise 14.2 467 Computer Configuration 467 Skill‐Builder Exercise 14.3 470 A Supply Chain Facility Location Model 470 Mixed Integer Optimization Models 471 Plant Location Model 471 A Model with Fixed Costs 473 Nonlinear Optimization 475 Hotel Pricing 475 Solving Nonlinear Optimization Models 477 Markowitz Portfolio Model 479 Skill‐Builder Exercise 14.4 482
- 38. Evolutionary Solver for Nonsmooth Optimization 482 Rectilinear Location Model 484 Skill‐Builder Exercise 14.5 484 Job Sequencing 485 Skill‐Builder Exercise 14.6 488 Risk Analysis and Optimization 488 Combining Optimization and Simulation 491 A Portfolio Allocation Model 491 Using OptQuest 492 Skill‐Builder Exercise 14.7 500 Basic Concepts Review Questions 500 Problems and Applications 500 Case: Tindall Bookstores 506
- 39. Appendix 509 Index 521 This page intentionally left blank xxi PREFACE INTENDED AUDIENCE Statistics, Data Analysis, and Decision Modeling was written to meet the need for an intro- ductory text that provides the fundamentals of business statistics and decision models/ optimization, focusing on practical applications of data analysis and decision modeling, all presented in a simple and straightforward fashion.
- 40. The text consists of 14 chapters in two distinct parts. The first eight chapters deal with statistical and data analysis topics, while the remaining chapters deal with decision models and applications. Thus, the text may be used for: • MBA or undergraduate business programs that combine topics in business sta- tistics and management science into a single, brief, quantitative methods • Business programs that teach statistics and management science in short, modular courses • Executive MBA programs • Graduate refresher courses for business statistics and management science NEW TO THIS EDITION The fifth edition of this text has been carefully revised to improve clarity and pedagogi- cal features, and incorporate new and revised topics. Many significant changes have
- 41. been made, which include the following: 1. Spreadsheet-based tools and applications are compatible with Microsoft Excel 2010 , which is used throughout this edition. 2. Every chapter has been carefully revised to improve clarity. Many explanations of critical concepts have been enhanced using new business examples and data sets. The sequencing of several topics have been reorganized to improve their flow within the book. 3. Excel, PHStat , and other software notes have been moved to chapter appendixes so as not to disrupt the flow of the text. 4. “Skill-Builder” exercises, designed to provide experience with applying Excel, have been located in the text to facilitate immediate application of new concepts. 5. Data used in many problems have been changed, and new problems have been added.
- 42. SUBSTANCE The danger in using quantitative methods does not generally lie in the inability to per- form the requisite calculations, but rather in the lack of a fundamental understanding of why to use a procedure, how to use it correctly, and how to properly interpret results. A key focus of this text is conceptual understanding using simple and practical examples rather than a plug-and-chug or point-and-click mentality, as are often done in other texts, supplemented by appropriate theory. On the other hand, the text does not attempt to be an encyclopedia of detailed quantitative procedures, but focuses on useful con- cepts and tools for today's managers. To support the presentation of topics in business statistics and decision model- ing, this text integrates fundamental theory and practical applications in a spreadsheet environment using Microsoft Excel 2010 and various spreadsheet add-ins, specifically:
- 43. • PHStat, a collection of statistical tools that enhance the capabilities of Excel; pub- lished by Pearson Education • Crystal Ball (including CBPredictor for forecasting and OptQuest for optimization), a powerful commercial package for risk analysis • TreePlan , a decision analysis add-in • SimQuick, an Excel-based application for process simulation, published by Pearson Education • Risk Solver Platform for Education, an Excel-based tool for risk analysis, simulation, and optimization These tools have been integrated throughout the text to simplify the presentations and implement tools and calculations so that more focus can be placed on interpretation
- 44. and understanding the managerial implications of results. TO THE STUDENTS The Companion Website for this text ( www.pearsonhighered.com/evans ) contains the following: • Data files —download the data and model files used throughout the text in exam- ples, problems, and exercises • PHStat —download of the software from Pearson • TreePlan —link to a free trial version • Risk Solver Platform for Education —link to a free trial version • Crystal Ball —link to a free trial version • SimQuick —link that will direct you to where you may purchase a standalone ver- sion of the software from Pearson • Subscription Content —a Companion Website Access Code is located on the back cover of this book. This code gives you access to the following
- 45. software: • Risk Solver Platform for Education —link that will direct students to an upgrade version • Crystal Ball —link that will direct students to an upgrade version • SimQuick —link that will allow you to download the software from Pearson To redeem the subscription content: • Visit www.pearsonhighered.com/evans. • Click on the Companion Website link. • Click on the Subscription Content link. • First-time users will need to register, while returning users may log-in. • Once you are logged in you will be brought to a page which will inform you how to download the software from the corresponding software company's Web site. TO THE INSTRUCTORS
- 46. To access instructor solutions files, please visit www.pearsonhighered.com/evans and choose the instructor resources option. A variety of instructor resources are available for instructors who register for our secure environment. The Instructor’s