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Useful Statistical Functions in Excel 2010


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
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
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
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)
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

James R. Evans
University of Cincinnati
International Edition contributions by

Ayanendranath Basu
Indian Statistical Institute, Kolkata

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To Beverly, Kristin, and Lauren, the three special women in my life.
—James R. Evans


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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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Preface 21

Part I


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




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

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


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



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




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

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



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



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






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

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



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
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






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



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


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

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





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



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


Problems and Applications 474
Case: Haller’s Pub & Brewery 481

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




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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
• Executive MBA programs
• Graduate refresher courses for business statistics and management science

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.

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



• 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
• 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.

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:
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com/evans and choose the instructor resources option. A variety of instructor resources
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Need help? Our dedicated Technical Support team is ready to assist instructors with questions about the media supplements that accompany this text. Visit
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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.
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.



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