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Useful Statistical Functions in Excel 2010

Description

AVERAGE(data range)

BINOM.DIST(number_s, trials, probability_s, cumulative)

BINOM.INV(trials, probability_s, alpha)

Computes the average value (arithmetic mean) of a set of data.

Returns the individual term binomial distribution.

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.

Returns the confidence interval for a population mean using a

normal distribution.

Returns the confidence interval for a population mean using a

t-distribution.

Computes the correlation coefficient between two data sets.

Returns the exponential distribution.

Returns the left-tailed F-probability distribution value.

Returns the left-tailed F-probability distribution value.

Calculates a future value along a linear trend.

Calculates predicted exponential growth.

Returns an array that describes a straight line that best fits the data.

Returns the cumulative lognormal distribution of x, where ln

(x) is normally distributed with parameters mean and

standard deviation.

Computes the median (middle value) of a set of data.

Computes the modes (most frequently occurring values) of a

set of data.

Computes the mode of a set of data.

Returns the normal cumulative distribution for the specified

mean and standard deviation.

Returns the inverse of the cumulative normal distribution.

Returns the standard normal cumulative distribution (mean = 0,

standard deviation = 1).

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.

Returns the Poisson distribution.

Computes the quartile of a distribution.

Computes the skewness, a measure of the degree to which a

distribution is not symmetric around its mean.

Returns a normalized value for a distribution characterized by

a mean and standard deviation.

Computes the standard deviation of a set of data, assumed to

be a sample.

Computes the standard deviation of a set of data, assumed to

be an entire population.

Returns values along a linear trend line.

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.

Computes the variance of a set of data, assumed to be a sample.

Computes the variance of a set of data, assumed to be an entire

population.

Returns the two-tailed p-value of a z-test.

CHISQ.DIST(x, deg_freedom, cumulative)

CHISQ.DIST.RT(x, deg_freedom, cumulative)

CHISQ.TEST(actual_range, expected_range)

CONFIDENCE.NORM(alpha, standard_dev, size)

CONFIDENCE.T(alpha, standard_dev, size)

CORREL(arrayl, array2)

EXPON.DIST(x, lambda, cumulative)

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)

GROWTH(known_y's, known_x's, new_x's, constant)

LINEST(known_y's, known_x's, new_x's, constant, stats)

LOGNORM.DIST(x, mean, standard_deviation)

MEDIAN(data range)

MODE.MULT(data range)

MODE.SNGL(data range)

NORM.DIST(x, mean, standard_dev, cumulative)

NORM.INV(probability, mean, standard_dev)

NORM.S.DIST(z)

NORM.S.INV(probability)

PERCENTILE.EXC(array, k)

PERCENTILE.INC(array, k)

POISSON.DIST(x, mean, cumulative)

QUARTILE(array, quart)

SKEW(data range)

STANDARDIZE(x, mean, standard_deviation)

STDEV.S(data range)

STDEV.P(data range)

TREND(known_y's, known_x's, new_x's, constant)

T.DIST(x, deg_freedom, cumulative)

T.DIST.2T(x, deg_freedom)

T.DIST.RT(x, deg_freedom)

T.INV(probability, deg_freedom)

T.INV.2T(probability, deg_freedom)

T.TEST(arrayl, array2, tails, type)

VAR.S(data range)

VAR.P(data range)

Z.TEST(array, x, sigma)

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Fifth Edition

STATISTICS, DATA ANALYSIS,

AND DECISION MODELING

James R. Evans

University of Cincinnati

International Edition contributions by

Ayanendranath Basu

Indian Statistical Institute, Kolkata

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© Pearson Education Limited 2013

The right of James R. Evans to be identified as author of this work has been asserted by him in accordance with the

Copyright, Designs and Patents Act 1988.

Authorized adaptation from the United States edition, entitled Statistics, Data Analysis and Decision Modeling, 5th edition,

ISBN 978-0-13-274428-7 by James R. Evans published by Pearson Education © 2013.

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To Beverly, Kristin, and Lauren, the three special women in my life.

—James R. Evans

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BRIEF CONTENTS

PART I Statistics and Data Analysis 25

Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

Chapter 6

Chapter 7

Chapter 8

Data and Business Decisions 27

Descriptive Statistics and Data Analysis 55

Probability Concepts and Distributions 89

Sampling and Estimation 123

Hypothesis Testing and Statistical Inference 162

Regression Analysis 196

Forecasting 237

Introduction to Statistical Quality Control 272

PART II Decision Modeling and Analysis 293

Chapter 9

Chapter 10

Chapter 11

Chapter 12

Chapter 13

Chapter 14

Building and Using Decision Models 295

Decision Models with Uncertainty and Risk 324

Decisions, Uncertainty, and Risk 367

Queues and Process Simulation Modeling 402

Linear Optimization 435

Integer, Nonlinear, and Advanced Optimization

Methods 482

Appendix 533

Index 545

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CONTENTS

Preface 21

Part I

STATISTICS AND DATA ANALYSIS 25

Chapter 1 DATA AND BUSINESS DECISIONS 27

Introduction 28

Data in the Business Environment 28

Sources and Types of Data 30

Metrics and Data Classification 31

Statistical Thinking 35

Populations and Samples 36

Using Microsoft Excel 37

Basic Excel Skills 38

Skill‐Builder Exercise 1.1 38

Copying Formulas and Cell References 38

Skill‐Builder Exercise 1.2 39

Functions 40

Skill‐Builder Exercise 1.3 42

Other Useful Excel Tips 42

Excel Add‐Ins 43

Skill‐Builder Exercise 1.4 44

Displaying Data with Excel Charts 45

Column and Bar Charts 45

Skill‐Builder Exercise 1.5 46

Line Charts 47

Skill‐Builder Exercise 1.6 47

Pie Charts 47

Skill‐Builder Exercise 1.7 47

Area Charts 48

Scatter Diagrams 48

Skill‐Builder Exercise 1.8 48

Miscellaneous Excel Charts 49

Ethics and Data Presentation 49

Skill‐Builder Exercise 1.9 50

Basic Concepts Review Questions 51

Problems and Applications 51

Case: A Data Collection and Analysis Project 52

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Contents

Chapter 2 DESCRIPTIVE STATISTICS AND DATA ANALYSIS 55

Introduction 56

Descriptive Statistics 56

Frequency Distributions, Histograms, and Data Profiles 57

Categorical Data 58

Numerical Data 58

Skill‐Builder Exercise 2.1 62

Skill‐Builder Exercise 2.2 62

Data Profiles 62

Descriptive Statistics for Numerical Data 63

Measures of Location 63

Measures of Dispersion 64

Skill‐Builder Exercise 2.3 66

Measures of Shape 67

Excel Descriptive Statistics Tool 68

Skill‐Builder Exercise 2.4 68

Measures of Association 69

Skill‐Builder Exercise 2.5 71

Descriptive Statistics for Categorical Data 71

Skill‐Builder Exercise 2.6 72

Visual Display of Statistical Measures 73

Box Plots 73

Dot‐Scale Diagrams 73

Skill‐Builder Exercise 2.7 73

Outliers 74

Data Analysis Using PivotTables 74

Skill‐Builder Exercise 2.8 77

Skill‐Builder Exercise 2.9 77

Basic Concepts Review Questions 78

Problems and Applications 78

Case: The Malcolm Baldrige Award 81

Skill‐Builder Exercise 2.10 83

Skill‐Builder Exercise 2.11 84

Chapter 3 PROBABILITY CONCEPTS AND DISTRIBUTIONS 89

Introduction 90

Basic Concepts of Probability 90

Basic Probability Rules and Formulas 91

Conditional Probability 92

Skill‐Builder Exercise 3.1 94

Random Variables and Probability Distributions 94

Discrete Probability Distributions 97

Expected Value and Variance of a Discrete Random Variable 98

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Contents

Skill‐Builder Exercise 3.2 99

Bernoulli Distribution 99

Binomial Distribution 99

Poisson Distribution 100

Skill‐Builder Exercise 3.3 102

Continuous Probability Distributions 102

Uniform Distribution 104

Normal Distribution 105

Skill‐Builder Exercise 3.4 108

Triangular Distribution 108

Exponential Distribution 109

Probability Distributions in PHStat 110

Other Useful Distributions 110

Joint and Marginal Probability Distributions 113

Basic Concepts Review Questions 114

Problems and Applications 114

Case: Probability Analysis for Quality Measurements 118

Chapter 4 SAMPLING AND ESTIMATION

123

Introduction 124

Statistical Sampling 124

Sample Design 124

Sampling Methods 125

Errors in Sampling 127

Random Sampling From Probability Distributions 127

Sampling From Discrete Probability Distributions 128

Skill‐Builder Exercise 4.1 129

Sampling From Common Probability Distributions 129

A Statistical Sampling Experiment in Finance 130

Skill‐Builder Exercise 4.2 130

Sampling Distributions and Sampling Error 131

Skill‐Builder Exercise 4.3 134

Applying the Sampling Distribution of the Mean 134

Sampling and Estimation 134

Point Estimates 135

Unbiased Estimators 136

Skill‐Builder Exercise 4.4 137

Interval Estimates 137

Confidence Intervals: Concepts and Applications 137

Confidence Interval for the Mean with Known Population Standard

Deviation 138

Skill‐Builder Exercise 4.5 140

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Confidence Interval for the Mean with Unknown Population Standard

Deviation 140

Confidence Interval for a Proportion 142

Confidence Intervals for the Variance and Standard Deviation 143

Confidence Interval for a Population Total 145

Using Confidence Intervals for Decision Making 146

Confidence Intervals and Sample Size 146

Prediction Intervals 148

Additional Types of Confidence Intervals 149

Differences Between Means, Independent Samples 149

Differences Between Means, Paired Samples 149

Differences Between Proportions 150

Basic Concepts Review Questions 150

Problems and Applications 150

Case: Analyzing a Customer Survey 153

Skill‐Builder Exercise 4.6 155

Skill‐Builder Exercise 4.7 156

Skill‐Builder Exercise 4.8 157

Skill‐Builder Exercise 4.9 157

Chapter 5 HYPOTHESIS TESTING AND STATISTICAL INFERENCE

Introduction 163

Basic Concepts of Hypothesis Testing 163

Hypothesis Formulation 164

Significance Level 165

Decision Rules 166

Spreadsheet Support for Hypothesis Testing 169

One‐Sample Hypothesis Tests 169

One‐Sample Tests for Means 169

Using p‐Values 171

One‐Sample Tests for Proportions 172

One Sample Test for the Variance 174

Type II Errors and the Power of A Test 175

Skill‐Builder Exercise 5.1 177

Two‐Sample Hypothesis Tests 177

Two‐Sample Tests for Means 177

Two‐Sample Test for Means with Paired Samples 179

Two‐Sample Tests for Proportions 179

Hypothesis Tests and Confidence Intervals 180

Test for Equality of Variances 181

Skill‐Builder Exercise 5.2 182

Anova: Testing Differences of Several Means 182

Assumptions of ANOVA 184

Tukey–Kramer Multiple Comparison Procedure 184

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Chi‐Square Test for Independence 186

Skill‐Builder Exercise 5.3 188

Basic Concepts Review Questions 188

Problems and Applications 188

Case: HATCO, Inc. 191

Skill‐Builder Exercise 5.4 193

Chapter 6 REGRESSION ANALYSIS

196

Introduction 197

Simple Linear Regression 198

Skill‐Builder Exercise 6.1 199

Least‐Squares Regression 200

Skill‐Builder Exercise 6.2 202

A Practical Application of Simple Regression to Investment

Risk 202

Simple Linear Regression in Excel 203

Skill‐Builder Exercise 6.3 204

Regression Statistics 204

Regression as Analysis of Variance 205

Testing Hypotheses for Regression Coefficients 205

Confidence Intervals for Regression Coefficients 206

Confidence and Prediction Intervals for X‐Values 206

Residual Analysis and Regression Assumptions 206

Standard Residuals 208

Skill‐Builder Exercise 6.4 208

Checking Assumptions 208

Multiple Linear Regression 210

Skill‐Builder Exercise 6.5 210

Interpreting Results from Multiple Linear Regression 212

Correlation and Multicollinearity 212

Building Good Regression Models 214

Stepwise Regression 217

Skill‐Builder Exercise 6.6 217

Best‐Subsets Regression 217

The Art of Model Building in Regression 218

Regression with Categorical Independent Variables 220

Categorical Variables with More Than Two Levels 223

Skill‐Builder Exercise 6.7 225

Regression Models with Nonlinear Terms 225

Skill‐Builder Exercise 6.8 226

Basic Concepts Review Questions 228

Problems and Applications 228

Case: Hatco 231

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Contents

Chapter 7 FORECASTING

237

Introduction 238

Qualitative and Judgmental Methods 238

Historical Analogy 239

The Delphi Method 239

Indicators and Indexes for Forecasting 239

Statistical Forecasting Models 240

Forecasting Models for Stationary Time Series 242

Moving Average Models 242

Error Metrics and Forecast Accuracy 244

Skill‐Builder Exercise 7.1 246

Exponential Smoothing Models 246

Skill‐Builder Exercise 7.2 248

Forecasting Models for Time Series with a Linear Trend 248

Regression‐Based Forecasting 248

Advanced Forecasting Models 249

Autoregressive Forecasting Models 250

Skill‐Builder Exercise 7.3 252

Forecasting Models with Seasonality 252

Incorporating Seasonality in Regression Models 253

Skill‐Builder Exercise 7.4 255

Forecasting Models with Trend and Seasonality 255

Regression Forecasting with Causal Variables 255

Choosing and Optimizing Forecasting Models Using

CB Predictor 257

Skill‐Builder Exercise 7.5 259

The Practice of Forecasting 262

Basic Concepts Review Questions 263

Problems and Applications 264

Case: Energy Forecasting 265

Chapter 8 INTRODUCTION TO STATISTICAL QUALITY CONTROL

Introduction 272

The Role of Statistics and Data Analysis in Quality

Control 273

Statistical Process Control 274

Control Charts 274

x ‐ and R‐Charts 275

Skill‐Builder Exercise 8.1 280

Analyzing Control Charts 280

Sudden Shift in the Process Average 281

Cycles 281

Trends 281

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Hugging the Center Line 281

Hugging the Control Limits 282

Skill‐Builder Exercise 8.2 282

Skill‐Builder Exercise 8.3 284

Control Charts for Attributes 284

Variable Sample Size 286

Skill‐Builder Exercise 8.4 288

Process Capability Analysis 288

Skill‐Builder Exercise 8.5 290

Basic Concepts Review Questions 290

Problems and Applications 290

Case: Quality Control Analysis 291

Part II Decision Modeling and Analysis 293

Chapter 9 BUILDING AND USING DECISION MODELS 295

Introduction 295

Decision Models 296

Model Analysis 299

What‐If Analysis 299

Skill‐Builder Exercise 9.1 301

Skill‐Builder Exercise 9.2 302

Skill‐Builder Exercise 9.3 302

Model Optimization 302

Tools for Model Building 304

Logic and Business Principles 304

Skill‐Builder Exercise 9.4 305

Common Mathematical Functions 305

Data Fitting 306

Skill‐Builder Exercise 9.5 308

Spreadsheet Engineering 308

Skill‐Builder Exercise 9.6 309

Spreadsheet Modeling Examples 309

New Product Development 309

Skill‐Builder Exercise 9.7 311

Single Period Purchase Decisions 311

Overbooking Decisions 312

Project Management 313

Model Assumptions, Complexity, and Realism 315

Skill‐Builder Exercise 9.8 317

Basic Concepts Review Questions 317

Problems and Applications 318

Case: An Inventory Management Decision Model 321

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Contents

Chapter 10 DECISION MODELS WITH UNCERTAINTY AND RISK

324

Introduction 325

Spreadsheet Models with Random Variables 325

Monte Carlo Simulation 326

Skill‐Builder Exercise 10.1 327

Monte Carlo Simulation Using Crystal Ball 327

Defining Uncertain Model Inputs 328

Running a Simulation 332

Saving Crystal Ball Runs 334

Analyzing Results 334

Skill‐Builder Exercise 10.2 338

Crystal Ball Charts 339

Crystal Ball Reports and Data Extraction 342

Crystal Ball Functions and Tools 342

Applications of Monte Carlo Simulation and Crystal Ball

Features 343

Newsvendor Model: Fitting Input Distributions, Decision Table Tool,

and Custom Distribution 343

Skill‐Builder Exercise 10.3 347

Skill‐Builder Exercise 10.4 348

Overbooking Model: Crystal Ball Functions 348

Skill‐Builder Exercise 10.5 349

Cash Budgeting: Correlated Assumptions 349

New Product Introduction: Tornado Chart Tool 352

Skill‐Builder Exercise 10.6 353

Project Management: Alternate Input Parameters and the

Bootstrap Tool 353

Skill‐Builder Exercise 10.7 358

Basic Concepts Review Questions 358

Problems and Applications 359

Case: J&G Bank 362

Chapter 11 DECISIONS, UNCERTAINTY, AND RISK

367

Introduction 368

Decision Making Under Certainty 368

Decisions Involving a Single Alternative 369

Skill‐Builder Exercise 11.1 369

Decisions Involving Non–mutually Exclusive Alternatives 369

Decisions Involving Mutually Exclusive Alternatives 370

Decisions Involving Uncertainty and Risk 371

Making Decisions with Uncertain Information 371

Decision Strategies for a Minimize Objective 372

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Contents

Skill‐Builder Exercise 11.2 374

Decision Strategies for a Maximize Objective 374

Risk and Variability 375

Expected Value Decision Making 377

Analysis of Portfolio Risk 378

Skill‐Builder Exercise 11.3 380

The “Flaw of Averages” 380

Skill‐Builder Exercise 11.4 380

Decision Trees 381

A Pharmaceutical R&D Model 381

Decision Trees and Risk 382

Sensitivity Analysis in Decision Trees 384

Skill‐Builder Exercise 11.5 384

The Value of Information 384

Decisions with Sample Information 386

Conditional Probabilities and Bayes’s Rule 387

Utility and Decision Making 389

Skill‐Builder Exercise 11.6 392

Exponential Utility Functions 393

Skill‐Builder Exercise 11.7 394

Basic Concepts Review Questions 394

Problems and Applications 395

Case: The Sandwich Decision 399

Chapter 12 QUEUES AND PROCESS SIMULATION MODELING

Introduction 402

Queues and Queuing Systems 403

Basic Concepts of Queuing Systems 403

Customer Characteristics 404

Service Characteristics 405

Queue Characteristics 405

System Configuration 405

Performance Measures 406

Analytical Queuing Models 406

Single‐Server Model 407

Skill‐Builder Exercise 12.1 408

Little’s Law 408

Process Simulation Concepts 409

Skill‐Builder Exercise 12.2 410

Process Simulation with SimQuick 410

Getting Started with SimQuick 411

A Queuing Simulation Model 412

402

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Contents

Skill‐Builder Exercise 12.3 416

Queues in Series with Blocking 417

Grocery Store Checkout Model with Resources 418

Manufacturing Inspection Model with Decision Points 421

Pull System Supply Chain with Exit Schedules 424

Other SimQuick Features and Commercial Simulation Software 426

Continuous Simulation Modeling 427

Basic Concepts Review Questions 430

Problems and Applications 431

Case: Production/Inventory Planning 434

Chapter 13 LINEAR OPTIMIZATION

435

Introduction 435

Building Linear Optimization Models 436

Characteristics of Linear Optimization Models 439

Implementing Linear Optimization Models on Spreadsheets 440

Excel Functions to Avoid in Modeling Linear Programs 441

Solving Linear Optimization Models 442

Solving the SSC Model Using Standard Solver 442

Solving the SSC Model Using Premium Solver 444

Solver Outcomes and Solution Messages 446

Interpreting Solver Reports 446

Skill‐Builder Exercise 13.1 450

How Solver Creates Names in Reports 451

Difficulties with Solver 451

Applications of Linear Optimization 451

Process Selection 453

Skill‐Builder Exercise 13.2 454

Blending 454

Skill‐Builder Exercise 13.3 456

Portfolio Investment 456

Skill‐Builder Exercise 13.4 457

Transportation Problem 457

Interpreting Reduced Costs 461

Multiperiod Production Planning 461

Skill‐Builder Exercise 13.5 463

Multiperiod Financial Planning 463

Skill‐Builder Exercise 13.6 464

A Model with Bounded Variables 464

A Production/Marketing Allocation Model 469

How Solver Works 473

Basic Concepts Review Questions 474

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Contents

Problems and Applications 474

Case: Haller’s Pub & Brewery 481

Chapter 14 INTEGER, NONLINEAR, AND ADVANCED OPTIMIZATION

METHODS 482

Introduction 482

Integer Optimization Models 483

A Cutting Stock Problem 483

Solving Integer Optimization Models 484

Skill‐Builder Exercise 14.1 486

Integer Optimization Models with Binary Variables 487

Project Selection 487

Site Location Model 488

Skill‐Builder Exercise 14.2 491

Computer Configuration 491

Skill‐Builder Exercise 14.3 494

A Supply Chain Facility Location Model 494

Mixed Integer Optimization Models 495

Plant Location Model 495

A Model with Fixed Costs 497

Nonlinear Optimization 499

Hotel Pricing 499

Solving Nonlinear Optimization Models 501

Markowitz Portfolio Model 503

Skill‐Builder Exercise 14.4 506

Evolutionary Solver for Nonsmooth Optimization 506

Rectilinear Location Model 508

Skill‐Builder Exercise 14.5 508

Job Sequencing 509

Skill‐Builder Exercise 14.6 512

Risk Analysis and Optimization 512

Combining Optimization and Simulation 515

A Portfolio Allocation Model 515

Using OptQuest 516

Skill‐Builder Exercise 14.7 524

Basic Concepts Review Questions 524

Problems and Applications 524

Case: Tindall Bookstores 530

Appendix 533

Index

545

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PREFACE

INTENDED AUDIENCE

Statistics, Data Analysis, and Decision Modeling was written to meet the need for an introductory 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.

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 statistics 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 pedagogical features, and incorporate new and revised topics. Many significant changes have

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.

SUBSTANCE

The danger in using quantitative methods does not generally lie in the inability to perform 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 concepts and tools for today's managers.

To support the presentation of topics in business statistics and decision modeling, this text integrates fundamental theory and practical applications in a spreadsheet

environment using Microsoft Excel 2010 and various spreadsheet add-ins, specifically:

• PHStat, a collection of statistical tools that enhance the capabilities of Excel; published by Pearson Education

21

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22

Preface

• 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

and understanding the managerial implications of results.

TO THE STUDENTS

The Companion Website for this text (www.pearsoninternationaleditions.com/evans)

contains the following:

• Data files—download the data and model files used throughout the text in examples, 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 version of the software from Pearson

• Subscription Content—a Companion Website Access Code accompanies this book.

This code gives you access to the following 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.pearsoninternationaleditions.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

To access instructor solutions files, please visit www.pearsoninternationaleditions.

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

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Preface

ACKNOWLEDGMENTS

I would like to thank the following individuals who have provided reviews and insightful suggestions for this edition: Ardith Baker (Oral Roberts University), Geoffrey Barnes

(University of Iowa), David H. Hartmann (University of Central Oklahoma), Anthony

Narsing (Macon State College), Tony Zawilski (The George Washington University), and

Dr. J. H. Sullivan (Mississippi State University).

In addition, I thank the many students who over the years provided numerous

suggestions, data sets and problem ideas, and insights into how to better present the

material. Finally, appreciation goes to my editor Chuck Synovec; Mary Kate Murray,

Editorial Project Manager; Ashlee Bradbury, Editorial Assistant; and the entire production staff at Pearson Education for their dedication in developing and producing this

text. If you have any suggestions or corrections, please contact me via email at james.

evans@uc.edu.

James R. Evans

University of Cincinnati

The publishers wish to thank Asis Kumar Chattopadhyay and Uttam Bandyopadhyay,

both of the University of Calcutta, for reviewing the content of the International Edition.

23

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Useful Statistical Functions in Excel 2010

Description

AVERAGE(data range)

BINOM.DIST(number_s, trials, probability_s, cumulative)

BINOM.INV(trials, probability_s, alpha)

Computes the average value (arithmetic mean) of a set of data.

Returns the individual term binomial distribution.

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.

Returns the confidence interval for a population mean using a

normal distribution.

Returns the confidence interval for a population mean using a

t-distribution.

Computes the correlation coefficient between two data sets.

Returns the exponential distribution.

Returns the left-tailed F-probability distribution value.

Returns the left-tailed F-probability distribution value.

Calculates a future value along a linear trend.

Calculates predicted exponential growth.

Returns an array that describes a straight line that best fits the data.

Returns the cumulative lognormal distribution of x, where ln

(x) is normally distributed with parameters mean and

standard deviation.

Computes the median (middle value) of a set of data.

Computes the modes (most frequently occurring values) of a

set of data.

Computes the mode of a set of data.

Returns the normal cumulative distribution for the specified

mean and standard deviation.

Returns the inverse of the cumulative normal distribution.

Returns the standard normal cumulative distribution (mean = 0,

standard deviation = 1).

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.

Returns the Poisson distribution.

Computes the quartile of a distribution.

Computes the skewness, a measure of the degree to which a

distribution is not symmetric around its mean.

Returns a normalized value for a distribution characterized by

a mean and standard deviation.

Computes the standard deviation of a set of data, assumed to

be a sample.

Computes the standard deviation of a set of data, assumed to

be an entire population.

Returns values along a linear trend line.

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.

Computes the variance of a set of data, assumed to be a sample.

Computes the variance of a set of data, assumed to be an entire

population.

Returns the two-tailed p-value of a z-test.

CHISQ.DIST(x, deg_freedom, cumulative)

CHISQ.DIST.RT(x, deg_freedom, cumulative)

CHISQ.TEST(actual_range, expected_range)

CONFIDENCE.NORM(alpha, standard_dev, size)

CONFIDENCE.T(alpha, standard_dev, size)

CORREL(arrayl, array2)

EXPON.DIST(x, lambda, cumulative)

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)

GROWTH(known_y's, known_x's, new_x's, constant)

LINEST(known_y's, known_x's, new_x's, constant, stats)

LOGNORM.DIST(x, mean, standard_deviation)

MEDIAN(data range)

MODE.MULT(data range)

MODE.SNGL(data range)

NORM.DIST(x, mean, standard_dev, cumulative)

NORM.INV(probability, mean, standard_dev)

NORM.S.DIST(z)

NORM.S.INV(probability)

PERCENTILE.EXC(array, k)

PERCENTILE.INC(array, k)

POISSON.DIST(x, mean, cumulative)

QUARTILE(array, quart)

SKEW(data range)

STANDARDIZE(x, mean, standard_deviation)

STDEV.S(data range)

STDEV.P(data range)

TREND(known_y's, known_x's, new_x's, constant)

T.DIST(x, deg_freedom, cumulative)

T.DIST.2T(x, deg_freedom)

T.DIST.RT(x, deg_freedom)

T.INV(probability, deg_freedom)

T.INV.2T(probability, deg_freedom)

T.TEST(arrayl, array2, tails, type)

VAR.S(data range)

VAR.P(data range)

Z.TEST(array, x, sigma)

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Fifth Edition

STATISTICS, DATA ANALYSIS,

AND DECISION MODELING

James R. Evans

University of Cincinnati

International Edition contributions by

Ayanendranath Basu

Indian Statistical Institute, Kolkata

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© Pearson Education Limited 2013

The right of James R. Evans to be identified as author of this work has been asserted by him in accordance with the

Copyright, Designs and Patents Act 1988.

Authorized adaptation from the United States edition, entitled Statistics, Data Analysis and Decision Modeling, 5th edition,

ISBN 978-0-13-274428-7 by James R. Evans published by Pearson Education © 2013.

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To Beverly, Kristin, and Lauren, the three special women in my life.

—James R. Evans

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BRIEF CONTENTS

PART I Statistics and Data Analysis 25

Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

Chapter 6

Chapter 7

Chapter 8

Data and Business Decisions 27

Descriptive Statistics and Data Analysis 55

Probability Concepts and Distributions 89

Sampling and Estimation 123

Hypothesis Testing and Statistical Inference 162

Regression Analysis 196

Forecasting 237

Introduction to Statistical Quality Control 272

PART II Decision Modeling and Analysis 293

Chapter 9

Chapter 10

Chapter 11

Chapter 12

Chapter 13

Chapter 14

Building and Using Decision Models 295

Decision Models with Uncertainty and Risk 324

Decisions, Uncertainty, and Risk 367

Queues and Process Simulation Modeling 402

Linear Optimization 435

Integer, Nonlinear, and Advanced Optimization

Methods 482

Appendix 533

Index 545

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CONTENTS

Preface 21

Part I

STATISTICS AND DATA ANALYSIS 25

Chapter 1 DATA AND BUSINESS DECISIONS 27

Introduction 28

Data in the Business Environment 28

Sources and Types of Data 30

Metrics and Data Classification 31

Statistical Thinking 35

Populations and Samples 36

Using Microsoft Excel 37

Basic Excel Skills 38

Skill‐Builder Exercise 1.1 38

Copying Formulas and Cell References 38

Skill‐Builder Exercise 1.2 39

Functions 40

Skill‐Builder Exercise 1.3 42

Other Useful Excel Tips 42

Excel Add‐Ins 43

Skill‐Builder Exercise 1.4 44

Displaying Data with Excel Charts 45

Column and Bar Charts 45

Skill‐Builder Exercise 1.5 46

Line Charts 47

Skill‐Builder Exercise 1.6 47

Pie Charts 47

Skill‐Builder Exercise 1.7 47

Area Charts 48

Scatter Diagrams 48

Skill‐Builder Exercise 1.8 48

Miscellaneous Excel Charts 49

Ethics and Data Presentation 49

Skill‐Builder Exercise 1.9 50

Basic Concepts Review Questions 51

Problems and Applications 51

Case: A Data Collection and Analysis Project 52

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Contents

Chapter 2 DESCRIPTIVE STATISTICS AND DATA ANALYSIS 55

Introduction 56

Descriptive Statistics 56

Frequency Distributions, Histograms, and Data Profiles 57

Categorical Data 58

Numerical Data 58

Skill‐Builder Exercise 2.1 62

Skill‐Builder Exercise 2.2 62

Data Profiles 62

Descriptive Statistics for Numerical Data 63

Measures of Location 63

Measures of Dispersion 64

Skill‐Builder Exercise 2.3 66

Measures of Shape 67

Excel Descriptive Statistics Tool 68

Skill‐Builder Exercise 2.4 68

Measures of Association 69

Skill‐Builder Exercise 2.5 71

Descriptive Statistics for Categorical Data 71

Skill‐Builder Exercise 2.6 72

Visual Display of Statistical Measures 73

Box Plots 73

Dot‐Scale Diagrams 73

Skill‐Builder Exercise 2.7 73

Outliers 74

Data Analysis Using PivotTables 74

Skill‐Builder Exercise 2.8 77

Skill‐Builder Exercise 2.9 77

Basic Concepts Review Questions 78

Problems and Applications 78

Case: The Malcolm Baldrige Award 81

Skill‐Builder Exercise 2.10 83

Skill‐Builder Exercise 2.11 84

Chapter 3 PROBABILITY CONCEPTS AND DISTRIBUTIONS 89

Introduction 90

Basic Concepts of Probability 90

Basic Probability Rules and Formulas 91

Conditional Probability 92

Skill‐Builder Exercise 3.1 94

Random Variables and Probability Distributions 94

Discrete Probability Distributions 97

Expected Value and Variance of a Discrete Random Variable 98

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Contents

Skill‐Builder Exercise 3.2 99

Bernoulli Distribution 99

Binomial Distribution 99

Poisson Distribution 100

Skill‐Builder Exercise 3.3 102

Continuous Probability Distributions 102

Uniform Distribution 104

Normal Distribution 105

Skill‐Builder Exercise 3.4 108

Triangular Distribution 108

Exponential Distribution 109

Probability Distributions in PHStat 110

Other Useful Distributions 110

Joint and Marginal Probability Distributions 113

Basic Concepts Review Questions 114

Problems and Applications 114

Case: Probability Analysis for Quality Measurements 118

Chapter 4 SAMPLING AND ESTIMATION

123

Introduction 124

Statistical Sampling 124

Sample Design 124

Sampling Methods 125

Errors in Sampling 127

Random Sampling From Probability Distributions 127

Sampling From Discrete Probability Distributions 128

Skill‐Builder Exercise 4.1 129

Sampling From Common Probability Distributions 129

A Statistical Sampling Experiment in Finance 130

Skill‐Builder Exercise 4.2 130

Sampling Distributions and Sampling Error 131

Skill‐Builder Exercise 4.3 134

Applying the Sampling Distribution of the Mean 134

Sampling and Estimation 134

Point Estimates 135

Unbiased Estimators 136

Skill‐Builder Exercise 4.4 137

Interval Estimates 137

Confidence Intervals: Concepts and Applications 137

Confidence Interval for the Mean with Known Population Standard

Deviation 138

Skill‐Builder Exercise 4.5 140

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Confidence Interval for the Mean with Unknown Population Standard

Deviation 140

Confidence Interval for a Proportion 142

Confidence Intervals for the Variance and Standard Deviation 143

Confidence Interval for a Population Total 145

Using Confidence Intervals for Decision Making 146

Confidence Intervals and Sample Size 146

Prediction Intervals 148

Additional Types of Confidence Intervals 149

Differences Between Means, Independent Samples 149

Differences Between Means, Paired Samples 149

Differences Between Proportions 150

Basic Concepts Review Questions 150

Problems and Applications 150

Case: Analyzing a Customer Survey 153

Skill‐Builder Exercise 4.6 155

Skill‐Builder Exercise 4.7 156

Skill‐Builder Exercise 4.8 157

Skill‐Builder Exercise 4.9 157

Chapter 5 HYPOTHESIS TESTING AND STATISTICAL INFERENCE

Introduction 163

Basic Concepts of Hypothesis Testing 163

Hypothesis Formulation 164

Significance Level 165

Decision Rules 166

Spreadsheet Support for Hypothesis Testing 169

One‐Sample Hypothesis Tests 169

One‐Sample Tests for Means 169

Using p‐Values 171

One‐Sample Tests for Proportions 172

One Sample Test for the Variance 174

Type II Errors and the Power of A Test 175

Skill‐Builder Exercise 5.1 177

Two‐Sample Hypothesis Tests 177

Two‐Sample Tests for Means 177

Two‐Sample Test for Means with Paired Samples 179

Two‐Sample Tests for Proportions 179

Hypothesis Tests and Confidence Intervals 180

Test for Equality of Variances 181

Skill‐Builder Exercise 5.2 182

Anova: Testing Differences of Several Means 182

Assumptions of ANOVA 184

Tukey–Kramer Multiple Comparison Procedure 184

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Contents

Chi‐Square Test for Independence 186

Skill‐Builder Exercise 5.3 188

Basic Concepts Review Questions 188

Problems and Applications 188

Case: HATCO, Inc. 191

Skill‐Builder Exercise 5.4 193

Chapter 6 REGRESSION ANALYSIS

196

Introduction 197

Simple Linear Regression 198

Skill‐Builder Exercise 6.1 199

Least‐Squares Regression 200

Skill‐Builder Exercise 6.2 202

A Practical Application of Simple Regression to Investment

Risk 202

Simple Linear Regression in Excel 203

Skill‐Builder Exercise 6.3 204

Regression Statistics 204

Regression as Analysis of Variance 205

Testing Hypotheses for Regression Coefficients 205

Confidence Intervals for Regression Coefficients 206

Confidence and Prediction Intervals for X‐Values 206

Residual Analysis and Regression Assumptions 206

Standard Residuals 208

Skill‐Builder Exercise 6.4 208

Checking Assumptions 208

Multiple Linear Regression 210

Skill‐Builder Exercise 6.5 210

Interpreting Results from Multiple Linear Regression 212

Correlation and Multicollinearity 212

Building Good Regression Models 214

Stepwise Regression 217

Skill‐Builder Exercise 6.6 217

Best‐Subsets Regression 217

The Art of Model Building in Regression 218

Regression with Categorical Independent Variables 220

Categorical Variables with More Than Two Levels 223

Skill‐Builder Exercise 6.7 225

Regression Models with Nonlinear Terms 225

Skill‐Builder Exercise 6.8 226

Basic Concepts Review Questions 228

Problems and Applications 228

Case: Hatco 231

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Contents

Chapter 7 FORECASTING

237

Introduction 238

Qualitative and Judgmental Methods 238

Historical Analogy 239

The Delphi Method 239

Indicators and Indexes for Forecasting 239

Statistical Forecasting Models 240

Forecasting Models for Stationary Time Series 242

Moving Average Models 242

Error Metrics and Forecast Accuracy 244

Skill‐Builder Exercise 7.1 246

Exponential Smoothing Models 246

Skill‐Builder Exercise 7.2 248

Forecasting Models for Time Series with a Linear Trend 248

Regression‐Based Forecasting 248

Advanced Forecasting Models 249

Autoregressive Forecasting Models 250

Skill‐Builder Exercise 7.3 252

Forecasting Models with Seasonality 252

Incorporating Seasonality in Regression Models 253

Skill‐Builder Exercise 7.4 255

Forecasting Models with Trend and Seasonality 255

Regression Forecasting with Causal Variables 255

Choosing and Optimizing Forecasting Models Using

CB Predictor 257

Skill‐Builder Exercise 7.5 259

The Practice of Forecasting 262

Basic Concepts Review Questions 263

Problems and Applications 264

Case: Energy Forecasting 265

Chapter 8 INTRODUCTION TO STATISTICAL QUALITY CONTROL

Introduction 272

The Role of Statistics and Data Analysis in Quality

Control 273

Statistical Process Control 274

Control Charts 274

x ‐ and R‐Charts 275

Skill‐Builder Exercise 8.1 280

Analyzing Control Charts 280

Sudden Shift in the Process Average 281

Cycles 281

Trends 281

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Contents

Hugging the Center Line 281

Hugging the Control Limits 282

Skill‐Builder Exercise 8.2 282

Skill‐Builder Exercise 8.3 284

Control Charts for Attributes 284

Variable Sample Size 286

Skill‐Builder Exercise 8.4 288

Process Capability Analysis 288

Skill‐Builder Exercise 8.5 290

Basic Concepts Review Questions 290

Problems and Applications 290

Case: Quality Control Analysis 291

Part II Decision Modeling and Analysis 293

Chapter 9 BUILDING AND USING DECISION MODELS 295

Introduction 295

Decision Models 296

Model Analysis 299

What‐If Analysis 299

Skill‐Builder Exercise 9.1 301

Skill‐Builder Exercise 9.2 302

Skill‐Builder Exercise 9.3 302

Model Optimization 302

Tools for Model Building 304

Logic and Business Principles 304

Skill‐Builder Exercise 9.4 305

Common Mathematical Functions 305

Data Fitting 306

Skill‐Builder Exercise 9.5 308

Spreadsheet Engineering 308

Skill‐Builder Exercise 9.6 309

Spreadsheet Modeling Examples 309

New Product Development 309

Skill‐Builder Exercise 9.7 311

Single Period Purchase Decisions 311

Overbooking Decisions 312

Project Management 313

Model Assumptions, Complexity, and Realism 315

Skill‐Builder Exercise 9.8 317

Basic Concepts Review Questions 317

Problems and Applications 318

Case: An Inventory Management Decision Model 321

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Contents

Chapter 10 DECISION MODELS WITH UNCERTAINTY AND RISK

324

Introduction 325

Spreadsheet Models with Random Variables 325

Monte Carlo Simulation 326

Skill‐Builder Exercise 10.1 327

Monte Carlo Simulation Using Crystal Ball 327

Defining Uncertain Model Inputs 328

Running a Simulation 332

Saving Crystal Ball Runs 334

Analyzing Results 334

Skill‐Builder Exercise 10.2 338

Crystal Ball Charts 339

Crystal Ball Reports and Data Extraction 342

Crystal Ball Functions and Tools 342

Applications of Monte Carlo Simulation and Crystal Ball

Features 343

Newsvendor Model: Fitting Input Distributions, Decision Table Tool,

and Custom Distribution 343

Skill‐Builder Exercise 10.3 347

Skill‐Builder Exercise 10.4 348

Overbooking Model: Crystal Ball Functions 348

Skill‐Builder Exercise 10.5 349

Cash Budgeting: Correlated Assumptions 349

New Product Introduction: Tornado Chart Tool 352

Skill‐Builder Exercise 10.6 353

Project Management: Alternate Input Parameters and the

Bootstrap Tool 353

Skill‐Builder Exercise 10.7 358

Basic Concepts Review Questions 358

Problems and Applications 359

Case: J&G Bank 362

Chapter 11 DECISIONS, UNCERTAINTY, AND RISK

367

Introduction 368

Decision Making Under Certainty 368

Decisions Involving a Single Alternative 369

Skill‐Builder Exercise 11.1 369

Decisions Involving Non–mutually Exclusive Alternatives 369

Decisions Involving Mutually Exclusive Alternatives 370

Decisions Involving Uncertainty and Risk 371

Making Decisions with Uncertain Information 371

Decision Strategies for a Minimize Objective 372

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Contents

Skill‐Builder Exercise 11.2 374

Decision Strategies for a Maximize Objective 374

Risk and Variability 375

Expected Value Decision Making 377

Analysis of Portfolio Risk 378

Skill‐Builder Exercise 11.3 380

The “Flaw of Averages” 380

Skill‐Builder Exercise 11.4 380

Decision Trees 381

A Pharmaceutical R&D Model 381

Decision Trees and Risk 382

Sensitivity Analysis in Decision Trees 384

Skill‐Builder Exercise 11.5 384

The Value of Information 384

Decisions with Sample Information 386

Conditional Probabilities and Bayes’s Rule 387

Utility and Decision Making 389

Skill‐Builder Exercise 11.6 392

Exponential Utility Functions 393

Skill‐Builder Exercise 11.7 394

Basic Concepts Review Questions 394

Problems and Applications 395

Case: The Sandwich Decision 399

Chapter 12 QUEUES AND PROCESS SIMULATION MODELING

Introduction 402

Queues and Queuing Systems 403

Basic Concepts of Queuing Systems 403

Customer Characteristics 404

Service Characteristics 405

Queue Characteristics 405

System Configuration 405

Performance Measures 406

Analytical Queuing Models 406

Single‐Server Model 407

Skill‐Builder Exercise 12.1 408

Little’s Law 408

Process Simulation Concepts 409

Skill‐Builder Exercise 12.2 410

Process Simulation with SimQuick 410

Getting Started with SimQuick 411

A Queuing Simulation Model 412

402

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Contents

Skill‐Builder Exercise 12.3 416

Queues in Series with Blocking 417

Grocery Store Checkout Model with Resources 418

Manufacturing Inspection Model with Decision Points 421

Pull System Supply Chain with Exit Schedules 424

Other SimQuick Features and Commercial Simulation Software 426

Continuous Simulation Modeling 427

Basic Concepts Review Questions 430

Problems and Applications 431

Case: Production/Inventory Planning 434

Chapter 13 LINEAR OPTIMIZATION

435

Introduction 435

Building Linear Optimization Models 436

Characteristics of Linear Optimization Models 439

Implementing Linear Optimization Models on Spreadsheets 440

Excel Functions to Avoid in Modeling Linear Programs 441

Solving Linear Optimization Models 442

Solving the SSC Model Using Standard Solver 442

Solving the SSC Model Using Premium Solver 444

Solver Outcomes and Solution Messages 446

Interpreting Solver Reports 446

Skill‐Builder Exercise 13.1 450

How Solver Creates Names in Reports 451

Difficulties with Solver 451

Applications of Linear Optimization 451

Process Selection 453

Skill‐Builder Exercise 13.2 454

Blending 454

Skill‐Builder Exercise 13.3 456

Portfolio Investment 456

Skill‐Builder Exercise 13.4 457

Transportation Problem 457

Interpreting Reduced Costs 461

Multiperiod Production Planning 461

Skill‐Builder Exercise 13.5 463

Multiperiod Financial Planning 463

Skill‐Builder Exercise 13.6 464

A Model with Bounded Variables 464

A Production/Marketing Allocation Model 469

How Solver Works 473

Basic Concepts Review Questions 474

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Contents

Problems and Applications 474

Case: Haller’s Pub & Brewery 481

Chapter 14 INTEGER, NONLINEAR, AND ADVANCED OPTIMIZATION

METHODS 482

Introduction 482

Integer Optimization Models 483

A Cutting Stock Problem 483

Solving Integer Optimization Models 484

Skill‐Builder Exercise 14.1 486

Integer Optimization Models with Binary Variables 487

Project Selection 487

Site Location Model 488

Skill‐Builder Exercise 14.2 491

Computer Configuration 491

Skill‐Builder Exercise 14.3 494

A Supply Chain Facility Location Model 494

Mixed Integer Optimization Models 495

Plant Location Model 495

A Model with Fixed Costs 497

Nonlinear Optimization 499

Hotel Pricing 499

Solving Nonlinear Optimization Models 501

Markowitz Portfolio Model 503

Skill‐Builder Exercise 14.4 506

Evolutionary Solver for Nonsmooth Optimization 506

Rectilinear Location Model 508

Skill‐Builder Exercise 14.5 508

Job Sequencing 509

Skill‐Builder Exercise 14.6 512

Risk Analysis and Optimization 512

Combining Optimization and Simulation 515

A Portfolio Allocation Model 515

Using OptQuest 516

Skill‐Builder Exercise 14.7 524

Basic Concepts Review Questions 524

Problems and Applications 524

Case: Tindall Bookstores 530

Appendix 533

Index

545

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PREFACE

INTENDED AUDIENCE

Statistics, Data Analysis, and Decision Modeling was written to meet the need for an introductory 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.

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 statistics 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 pedagogical features, and incorporate new and revised topics. Many significant changes have

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.

SUBSTANCE

The danger in using quantitative methods does not generally lie in the inability to perform 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 concepts and tools for today's managers.

To support the presentation of topics in business statistics and decision modeling, this text integrates fundamental theory and practical applications in a spreadsheet

environment using Microsoft Excel 2010 and various spreadsheet add-ins, specifically:

• PHStat, a collection of statistical tools that enhance the capabilities of Excel; published by Pearson Education

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Preface

• 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

and understanding the managerial implications of results.

TO THE STUDENTS

The Companion Website for this text (www.pearsoninternationaleditions.com/evans)

contains the following:

• Data files—download the data and model files used throughout the text in examples, 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 version of the software from Pearson

• Subscription Content—a Companion Website Access Code accompanies this book.

This code gives you access to the following 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.pearsoninternationaleditions.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

To access instructor solutions files, please visit www.pearsoninternationaleditions.

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

Solutions Manual files and PowerPoint presentation files for each chapter are available

for download.

As a registered faculty member, you can login directly to download resource files,

and receive immediate access and instructions for installing Course Management content to your campus server.

Need help? Our dedicated Technical Support team is ready to assist instructors with questions about the media supplements that accompany this text. Visit

http://247.pearsoned.com/ for answers to frequently asked questions and toll-free user

support phone numbers.

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Preface

ACKNOWLEDGMENTS

I would like to thank the following individuals who have provided reviews and insightful suggestions for this edition: Ardith Baker (Oral Roberts University), Geoffrey Barnes

(University of Iowa), David H. Hartmann (University of Central Oklahoma), Anthony

Narsing (Macon State College), Tony Zawilski (The George Washington University), and

Dr. J. H. Sullivan (Mississippi State University).

In addition, I thank the many students who over the years provided numerous

suggestions, data sets and problem ideas, and insights into how to better present the

material. Finally, appreciation goes to my editor Chuck Synovec; Mary Kate Murray,

Editorial Project Manager; Ashlee Bradbury, Editorial Assistant; and the entire production staff at Pearson Education for their dedication in developing and producing this

text. If you have any suggestions or corrections, please contact me via email at james.

evans@uc.edu.

James R. Evans

University of Cincinnati

The publishers wish to thank Asis Kumar Chattopadhyay and Uttam Bandyopadhyay,

both of the University of Calcutta, for reviewing the content of the International Edition.

23

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