An edition of Business Analytics, Global Edition (1920)

Business Analytics, Global Edition

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Cover of: Business Analytics, Global Edition by Evans, James R., Evans, James R.
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June 18, 2025 | History
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An edition of Business Analytics, Global Edition (1920)

Business Analytics, Global Edition

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Pearson Education, Limited
Language
English
Pages
704

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Subjects

Business intelligence, Strategic planning
Edition Availability
Cover of: Business Analytics, Global Edition
Business Analytics, Global Edition
2016, Pearson Higher Education & Professional Group
in English
Cover of: Business Analytics, Global Edition
Business Analytics, Global Edition
1920, Pearson Education, Limited
in English

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Book Details

Table of Contents

Preface
Page 17
About the Author
Page 25
Credits
Page 27
Part 1. Foundations of Business Analytics
Chapter 1. Introduction to Business Analytics
Page 29
Learning Objectives
Page 29
What Is Business Analytics?
Page 31
Using Business Analytics
Page 32
Impacts and Challenges
Page 33
Evolution of Business Analytics
Page 34
Analytic Foundations
Page 34
Modern Business Analytics
Page 35
Software Support and Spreadsheet Technology
Page 37
Analytics in Practice: Social Media Analytics
Page 38
Descriptive, Predictive, and Prescriptive Analytics
Page 39
Analytics in Practice: Analytics in the Home Lending and Mortgage Industry
Page 40
Data for Business Analytics
Page 42
Big Data
Page 44
Data Reliability and Validity
Page 44
Models in Business Analytics
Page 45
Descriptive Models
Page 47
Predictive Models
Page 49
Prescriptive Models
Page 50
Model Assumptions
Page 51
Uncertainty and Risk
Page 53
Problem Solving with Analytics
Page 54
Recognizing a Problem
Page 54
Defining the Problem
Page 54
Structuring the Problem
Page 55
Analyzing the Problem
Page 55
Interpreting Results and Making a Decision
Page 55
Implementing the Solution
Page 55
Analytics in Practice: Developing Effective Analytical Tools at Hewlett-Packard
Page 56
Key Terms
Page 57
Chapter 1 Technology Help
Page 57
Problems and Exercises
Page 57
Case: Performance Lawn Equipment
Page 59
Appendix A1. Basic Excel Skills
Page 61
Excel Formulas and Addressing
Page 62
Copying Formulas
Page 63
Useful Excel Tips
Page 63
Excel Functions
Page 64
Basic Excel Functions
Page 64
Functions for Specific Applications
Page 65
Insert Function
Page 66
Date and Time Functions
Page 67
Miscellaneous Excel Functions and Tools
Page 68
Range Names
Page 68
VALUE Function
Page 71
Paste Special
Page 71
Concatenation
Page 72
Error Values
Page 72
Problems and Exercises
Page 73
Chapter 2. Database Analytics
Page 75
Learning Objectives
Page 75
Data Sets and Databases
Page 77
Using Range Names in Databases
Page 78
Analytics in Practice: Using Big Data to Monitor Water Usage in Cary, North Carolina
Page 79
Data Queries: Tables, Sorting, and Filtering
Page 79
Sorting Data in Excel
Page 80
Pareto Analysis
Page 81
Filtering Data
Page 82
Database Functions
Page 84
Analytics in Practice: Discovering the Value of Database Analytics at Allders International
Page 86
Logical Functions
Page 87
Lookup Functions for Database Queries
Page 89
Excel Template Design
Page 92
Data Validation Tools
Page 93
Form Controls
Page 95
PivotTables
Page 98
PivotTable Customization
Page 100
Slicers
Page 103
Key Terms
Page 104
Chapter 2 Technology Help
Page 104
Problems and Exercises
Page 105
Case: People's Choice Bank
Page 109
Case: Drout Advertising Research Project
Page 110
Part 2. Descriptive Analytics
Chapter 3. Data Visualization
Page 113
Learning Objectives
Page 113
The Value of Data Visualization
Page 114
Tools and Software for Data Visualization
Page 116
Analytics in Practice: Data Visualization for the New York City Police Department's Domain Awareness System
Page 116
Creating Charts in Microsoft Excel
Page 116
Column and Bar Charts
Page 117
Data Label and Data Table Chart Options
Page 118
Line Charts
Page 119
Pie Charts
Page 120
Area Charts
Page 121
Scatter Charts and Orbit Charts
Page 122
Bubble Charts
Page 123
Combination Charts
Page 124
Radar Charts
Page 125
Stock Charts
Page 125
Charts from PivotTables
Page 125
Geographic Data
Page 126
Other Excel Data Visualization Tools
Page 126
Data Bars
Page 126
Color Scales
Page 127
Icon Sets
Page 128
Sparklines
Page 129
Dashboards
Page 131
Analytics in Practice: Driving Business Transformation with IBM Business Analytics
Page 132
Key Terms
Page 133
Chapter 3 Technology Help
Page 133
Problems and Exercises
Page 134
Case: Performance Lawn Equipment
Page 135
Appendix A3. Additional Tools for Data Visualization
Page 136
Hierarchy Charts
Page 136
Waterfall Charts
Page 136
PivotCharts
Page 138
Tableau
Page 139
Problems and Exercises
Page 141
Chapter 4. Descriptive Statistics
Page 143
Learning Objectives
Page 143
Analytics in Practice: Applications of Statistics in Health Care
Page 145
Metrics and Data Classification
Page 146
Frequency Distributions and Histograms
Page 148
Frequency Distributions for Categorical Data
Page 148
Relative Frequency Distributions
Page 149
Frequency Distributions for Numerical Data
Page 150
Grouped Frequency Distributions
Page 151
Cumulative Relative Frequency Distributions
Page 154
Constructing Frequency Distributions Using PivotTables
Page 155
Percentiles and Quartiles
Page 157
Cross-Tabulations
Page 158
Descriptive Statistical Measures
Page 160
Populations and Samples
Page 160
Statistical Notation
Page 161
Measures of Location: Mean, Median, Mode, and Midrange
Page 161
Using Measures of Location in Business Decisions
Page 163
Measures of Dispersion: Range, Interquartile Range, Variance, and Standard Deviation
Page 165
Chebyshev's Theorem and the Empirical Rules
Page 168
Standardized Values (Z-Scores)
Page 170
Coefficient of Variation
Page 171
Measures of Shape
Page 172
Excel Descriptive Statistics Tool
Page 174
Computing Descriptive Statistics for Frequency Distributions
Page 175
Descriptive Statistics for Categorical Data: The Proportion
Page 177
Statistics in PivotTables
Page 178
Measures of Association
Page 179
Covariance
Page 180
Correlation
Page 181
Excel Correlation Tool
Page 183
Outliers
Page 184
Using Descriptive Statistics to Analyze Survey Data
Page 186
Statistical Thinking in Business Decisions
Page 187
Variability in Samples
Page 188
Analytics in Practice: Applying Statistical Thinking to Detecting Financial Problems
Page 190
Key Terms
Page 191
Chapter 4 Technology Help
Page 192
Problems and Exercises
Page 193
Case: Drout Advertising Research Project
Page 198
Case: Performance Lawn Equipment
Page 198
Appendix A4. Additional Charts for Descriptive Statistics in Excel for Windows
Page 199
Problems and Exercises
Page 200
Chapter 5. Probability Distributions and Data Modeling
Page 201
Learning Objectives
Page 201
Basic Concepts of Probability
Page 203
Experiments and Sample Spaces
Page 203
Combinations and Permutations
Page 203
Probability Definitions
Page 205
Probability Rules and Formulas
Page 207
Joint and Marginal Probability
Page 208
Conditional Probability
Page 210
Random Variables and Probability Distributions
Page 213
Discrete Probability Distributions
Page 215
Expected Value of a Discrete Random Variable
Page 216
Using Expected Value in Making Decisions
Page 217
Variance of a Discrete Random Variable
Page 219
Bernoulli Distribution
Page 219
Binomial Distribution
Page 220
Poisson Distribution
Page 221
Analytics in Practice: Using the Poisson Distribution for Modeling Bids on Priceline
Page 223
Continuous Probability Distributions
Page 224
Properties of Probability Density Functions
Page 224
Uniform Distribution
Page 225
Normal Distribution
Page 227
The NORM.INV Function
Page 228
Standard Normal Distribution
Page 229
Using Standard Normal Distribution Tables
Page 230
Exponential Distribution
Page 231
Triangular Distribution
Page 232
Data Modeling and Distribution Fitting
Page 233
Goodness of Fit: Testing for Normality of an Empirical Distribution
Page 235
Analytics in Practice: The Value of Good Data Modeling in Advertising
Page 237
Key Terms
Page 238
Chapter 5 Technology Help
Page 238
Problems and Exercises
Page 239
Case: Performance Lawn Equipment
Page 245
Chapter 6. Sampling and Estimation
Page 247
Learning Objectives
Page 247
Statistical Sampling
Page 248
Sampling Methods
Page 249
Analytics in Practice: Using Sampling Techniques to Improve Distribution
Page 251
Estimating Population Parameters
Page 252
Unbiased Estimators
Page 252
Errors in Point Estimation
Page 253
Understanding Sampling Error
Page 254
Sampling Distributions
Page 256
Sampling Distribution of the Mean
Page 256
Applying the Sampling Distribution of the Mean
Page 257
Interval Estimates
Page 257
Confidence Intervals
Page 258
Confidence Interval for the Mean with Known Population Standard Deviation
Page 259
The t-Distribution
Page 260
Confidence Interval for the Mean with Unknown Population Standard Deviation
Page 261
Confidence Interval for a Proportion
Page 261
Additional Types of Confidence Intervals
Page 263
Using Confidence Intervals for Decision Making
Page 263
Data Visualization for Confidence Interval Comparison
Page 264
Prediction Intervals
Page 265
Confidence Intervals and Sample Size
Page 266
Key Terms
Page 268
Chapter 6 Technology Help
Page 268
Problems and Exercises
Page 269
Case: Drout Advertising Research Project
Page 272
Case: Performance Lawn Equipment
Page 273
Chapter 7. Statistical Inference
Page 275
Learning Objectives
Page 275
Hypothesis Testing
Page 276
Hypothesis-Testing Procedure
Page 276
One-Sample Hypothesis Tests
Page 277
Understanding Potential Errors in Hypothesis Testing
Page 278
Selecting the Test Statistic
Page 279
Finding Critical Values and Drawing a Conclusion
Page 280
Two-Tailed Test of Hypothesis for the Mean
Page 282
Summary of One-Sample Hypothesis Tests for the Mean
Page 283
p-Values
Page 284
One-Sample Tests for Proportions
Page 285
Confidence Intervals and Hypothesis Tests
Page 286
An Excel Template for One-Sample Hypothesis Tests
Page 286
Two-Sample Hypothesis Tests
Page 287
Two-Sample Tests for Differences in Means
Page 288
Two-Sample Test for Means with Paired Samples
Page 290
Two-Sample Test for Equality of Variances
Page 292
Analysis of Variance (ANOVA)
Page 294
Assumptions of ANOVA
Page 296
Chi-Square Test for Independence
Page 297
Cautions in Using the Chi-Square Test
Page 299
Chi-Square Goodness of Fit Test
Page 300
Analytics in Practice: Using Hypothesis Tests and Business Analytics in a Help Desk Service Improvement Project
Page 301
Key Terms
Page 302
Chapter 7 Technology Help
Page 302
Problems and Exercises
Page 304
Case: Drout Advertising Research Project
Page 309
Case: Performance Lawn Equipment
Page 309
Part 3. Predictive Analytics
Chapter 8. Trendlines and Regression Analysis
Page 311
Learning Objectives
Page 311
Modeling Relationships and Trends in Data
Page 313
Analytics in Practice: Using Predictive Trendline Models at Procter & Gamble
Page 317
Simple Linear Regression
Page 317
Finding the Best-Fitting Regression Line
Page 319
Using Regression Models for Prediction
Page 319
Least-Squares Regression
Page 320
Simple Linear Regression with Excel
Page 322
Regression as Analysis of Variance
Page 324
Testing Hypotheses for Regression Coefficients
Page 325
Confidence Intervals for Regression Coefficients
Page 325
Residual Analysis and Regression Assumptions
Page 326
Checking Assumptions
Page 327
Multiple Linear Regression
Page 329
Analytics in Practice: Using Linear Regression and Interactive Risk Simulators to Predict Performance at Aramark
Page 332
Building Good Regression Models
Page 334
Correlation and Multicollinearity
Page 336
Practical Issues in Trendline and Regression Modeling
Page 338
Regression with Categorical Independent Variables
Page 338
Categorical Variables with More Than Two Levels
Page 341
Regression Models with Nonlinear Terms
Page 343
Key Terms
Page 345
Chapter 8 Technology Help
Page 345
Problems and Exercises
Page 346
Case: Performance Lawn Equipment
Page 350
Chapter 9. Forecasting Techniques
Page 353
Learning Objectives
Page 353
Analytics in Practice: Forecasting Call-Center Demand at L.L.Bean
Page 354
Qualitative and Judgmental Forecasting
Page 355
Historical Analogy
Page 357
The Delphi Method
Page 355
Indicators and Indexes
Page 356
Statistical Forecasting Models
Page 357
Forecasting Models for Stationary Time Series
Page 359
Moving Average Models
Page 359
Error Metrics and Forecast Accuracy
Page 361
Exponential Smoothing Models
Page 363
Forecasting Models for Time Series with a Linear Trend
Page 366
Double Exponential Smoothing
Page 366
Regression-Based Forecasting for Time Series with a Linear Trend
Page 368
Forecasting Time Series with Seasonality
Page 369
Regression-Based Seasonal Forecasting Models
Page 369
Holt-Winters Models for Forecasting Time Series with Seasonality and No Trend
Page 371
Holt-Winters Models for Forecasting Time Series with Seasonality and Trend
Page 373
Selecting Appropriate Time-Series-Based Forecasting Models
Page 376
Regression Forecasting with Causal Variables
Page 376
The Practice of Forecasting
Page 377
Analytics in Practice: Forecasting at NBCUniversal
Page 378
Key Terms
Page 379
Chapter 9 Technology Help
Page 380
Problems and Exercises
Page 380
Case: Performance Lawn Equipment
Page 382
Chapter 10. Introduction to Data Mining
Page 383
Learning Objectives
Page 383
The Scope of Data Mining
Page 384
Cluster Analysis
Page 386
Measuring Distance Between Objects
Page 387
Normalizing Distance Measures
Page 388
Clustering Methods
Page 388
Classification
Page 390
An Intuitive Explanation of Classification
Page 391
Measuring Classification Performance
Page 392
Classification Techniques
Page 393
Association
Page 398
Cause-and-Effect Modeling
Page 400
Analytics in Practice: Successful Business Applications of Data Mining
Page 402
Key Terms
Page 402
Chapter 10 Technology Help
Page 403
Problems and Exercises
Page 403
Case: Performance Lawn Equipment
Page 404
Chapter 11. Spreadsheet Modeling and Analysis
Page 405
Learning Objectives
Page 405
Analytics in Practice: Using Spreadsheet Modeling and Analysis at Nestlé
Page 407
Model-Building Strategies
Page 407
Building Models Using Logic and Business Principles
Page 407
Building Models Using Influence Diagrams
Page 408
Building Models Using Historical Data
Page 409
Model Assumptions, Complexity, and Realism
Page 410
Implementing Models on Spreadsheets
Page 410
Spreadsheet Design
Page 411
Spreadsheet Quality
Page 412
Data Validation
Page 414
Analytics in Practice: Spreadsheet Engineering at Procter & Gamble
Page 416
Descriptive Spreadsheet Models
Page 416
Staffing Decisions
Page 417
Single-Period Purchase Decisions
Page 418
Overbooking Decisions
Page 420
Analytics in Practice: Using an Overbooking Model at a Student Health Clinic
Page 421
Retail Markdown Decisions
Page 421
Predictive Spreadsheet Models
Page 423
New Product Development Model
Page 423
Cash Budgeting
Page 425
Retirement Planning
Page 426
Project Management
Page 426
Prescriptive Spreadsheet Models
Page 429
Portfolio Allocation
Page 429
Locating Central Facilities
Page 430
Job Sequencing
Page 432
Analyzing Uncertainty and Model Assumptions
Page 434
What-If Analysis
Page 434
Data Tables
Page 434
Scenario Manager
Page 437
Goal Seek
Page 438
Key Terms
Page 440
Chapter 11 Technology Help
Page 441
Problems and Exercises
Page 442
Case: Performance Lawn Equipment
Page 449
Chapter 12. Simulation and Risk Analysis
Page 451
Learning Objectives
Page 451
Monte Carlo Simulation
Page 453
Random Sampling from Probability Distributions
Page 455
Generating Random Variates using Excel Functions
Page 457
Discrete Probability Distributions
Page 457
Uniform Distributions
Page 458
Exponential Distributions
Page 459
Normal Distributions
Page 459
Binomial Distributions
Page 461
Triangular Distributions
Page 461
Monte Carlo Simulation in Excel
Page 463
Profit Model Simulation
Page 463
New Product Development
Page 466
Retirement Planning
Page 468
Single-Period Purchase Decisions
Page 469
Overbooking Decisions
Page 472
Project Management
Page 472
Analytics in Practice: Implementing Large-Scale Monte Carlo Spreadsheet Models
Page 474
Dynamic Systems Simulation
Page 475
Simulating Waiting Lines
Page 477
Analytics in Practice: Using Systems Simulation for Agricultural Product Development
Page 480
Key Terms
Page 481
Chapter 12 Technology Help
Page 481
Problems and Exercises
Page 481
Case: Performance Lawn Equipment
Page 491
Part 4. Prescriptive Analytics
Chapter 13. Linear Optimization
Page 493
Learning Objectives
Page 493
Optimization Models
Page 494
Analytics in Practice: Using Optimization Models for Sales Planning at NBC
Page 496
Developing Linear Optimization Models
Page 497
Identifying Decision Variables, the Objective, and Constraints
Page 498
Developing a Mathematical Model
Page 499
More About Constraints
Page 500
Implementing Linear Optimization Models on Spreadsheets
Page 502
Excel Functions to Avoid in Linear Optimization
Page 503
Solving Linear Optimization Models
Page 504
Solver Answer Report
Page 506
Graphical Interpretation of Linear Optimization with Two Variables
Page 507
How Solver Works
Page 513
How Solver Creates Names in Reports
Page 514
Solver Outcomes and Solution Messages
Page 515
Unique Optimal Solution
Page 515
Alternative (Multiple) Optimal Solutions
Page 515
Unbounded Solution
Page 515
Infeasibility
Page 517
Applications of Linear Optimization
Page 519
Blending Models
Page 519
Dealing with Infeasibility
Page 520
Portfolio Investment Models
Page 521
Scaling Issues in Using Solver
Page 523
Transportation Models
Page 526
Multiperiod Production Planning Models
Page 529
Multiperiod Financial Planning Models
Page 533
Analytics in Practice: Linear Optimization in Bank Financial Planning
Page 536
Key Terms
Page 537
Chapter 13 Technology Help
Page 537
Problems and Exercises
Page 538
Case: Performance Lawn Equipment
Page 550
Chapter 14. Integer and Nonlinear Optimization
Page 551
Learning Objectives
Page 551
Integer Linear Optimization Models
Page 552
Models with General Integer Variables
Page 553
Workforce-Scheduling Models
Page 556
Alternative Optimal Solutions
Page 559
Models with Binary Variables
Page 561
Using Binary Variables to Model Logical Constraints
Page 562
Applications in Supply Chain Optimization
Page 563
Analytics in Practice: Supply Chain Optimization at Procter & Gamble
Page 567
Nonlinear Optimization Models
Page 567
A Nonlinear Pricing Decision Model
Page 567
Quadratic Optimization
Page 571
Practical Issues Using Solver for Nonlinear Optimization
Page 572
Analytics in Practice: Applying Nonlinear Optimization at Prudential Securities
Page 573
Non-Smooth Optimization
Page 574
Evolutionary Solver
Page 574
Evolutionary Solver for Sequencing and Scheduling Models
Page 577
The Traveling Salesperson Problem
Page 579
Key Terms
Page 581
Chapter 14 Technology Help
Page 581
Problems and Exercises
Page 582
Case: Performance Lawn Equipment
Page 591
Chapter 15. Optimization Analytics
Page 593
Learning Objectives
Page 593
What-If Analysis for Optimization Models
Page 594
Solver Sensitivity Report
Page 595
Using the Sensitivity Report
Page 600
Degeneracy
Page 601
Interpreting Solver Reports for Nonlinear Optimization Models
Page 601
Models with Bounded Variables
Page 603
Auxiliary Variables for Bound Constraints
Page 606
What-If Analysis for Integer Optimization Models
Page 609
Visualization of Solver Reports
Page 611
Using Sensitivity Information Correctly
Page 618
Key Terms
Page 622
Chapter 15 Technology Help
Page 622
Problems and Exercises
Page 622
Case: Performance Lawn Equipment
Page 629
Part 5. Making Decisions
Chapter 16. Decision Analysis
Page 631
Learning Objectives
Page 631
Formulating Decision Problems
Page 633
Decision Strategies Without Outcome Probabilities
Page 634
Decision Strategies for a Minimize Objective
Page 634
Decision Strategies for a Maximize Objective
Page 636
Decisions with Conflicting Objectives
Page 636
Decision Strategies with Outcome Probabilities
Page 638
Average Payoff Strategy
Page 638
Expected Value Strategy
Page 638
Evaluating Risk
Page 639
Decision Trees
Page 640
Decision Trees and Risk
Page 642
Sensitivity Analysis in Decision Trees
Page 645
The Value of Information
Page 646
Decisions with Sample Information
Page 647
Bayes's Rule
Page 648
Utility and Decision Making
Page 649
Constructing a Utility Function
Page 650
Exponential Utility Functions
Page 653
Analytics in Practice: Using Decision Analysis in Drug Development
Page 654
Key Terms
Page 655
Chapter 16 Technology Help
Page 655
Problems and Exercises
Page 656
Case: Performance Lawn Equipment
Page 660
Appendix A
Page 661
Glossary
Page 685
Index
Page 693

Classifications

Library of Congress
, HD38.7

Edition Identifiers

Open Library
OL29501395M
Internet Archive
businessanalytic0000evan_k8x7
ISBN 13
9781292339061

Work Identifiers

Work ID
OL21104365W

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