Part 1 Getting, Describing, and Summarizing Data
2
Describing Data: Graphs and Tables
31
3 Describing Data: Summary Measures
89
4 Getting the Right Data
145
Part 2 Probability, Uncertainty, and Decision Making
5
Probability and Probability Distributions
205
6
Normal, Binomial, Poisson, and Exponential Distributions
7
Decision Making under Uncertainty
315
Part 3 Statistical Inference
8 Sampling and Sampling Distributions
9 Confidence Interval Estimation
10
Hypothesis Testing
387
431
497
Part 4 Regression, Forecasting, and Time Series
11 Regression Analysis: Estimating Relationships
12
Regression Analysis: Statistical Inference
13 Time Series Analysis and Forecasting
643
713
Part 5 Decision Modeling
14 Introduction to Optimization Modeling
15
Optimization Modeling: Applications
16 Introduction to Simulation Modeling
17
Simulation Models
1009
Appendix A Statistical Reporting
References
Index
1086
1083
1065
789
847
945
571
255
3.7 Measures of Association: Covariance
I
and Correlation
Introduction to Data Analysis and Decision
Making
3.8 Describing Data Sets with Boxplots
I
1.1 Introduction
3.9 Applying the Tools
2
1.2 An Overview of the Book
4
1.3 A Sampling of Examples
11
1.4 Modeling and Models
1.5 Conclusion
105
3.10 Conclusion
C A S E 3.1
134
The Dow Jones Averages
C A S E 3.2 Other Market Indexes
21
27
4
Getting the Right Data
4.1 Introduction
G E T T I N G , DESCRIBING,
SUMMARIZING DATA
Describing Data: Graphs and Tables
31
33
C A S E 2.1
158
4.7 Web Queries
4.9 Cleansing the Data
57
4.10 Conclusion
80
C A S E 4.1
Customer Arrivals at Bank98
164
175
177
4.8 Other Data Sources on the Web
52
2.6 Exploring Data with Pivot Tables
2.7 Conclusion
150
4.6 Creating Pivot Tables from External Data
48
2.5 Time Series Graphs
147
4.5 Importing External Data from Access
38
2.4 Analyzing Relationships
with Scatterplots
145
4.4 Complex Queries with the Advanced
Filter
2.3 Frequency Tables and Histograms
144
4.3 Excel Tables for Filtering, Sorting, and
29
33
2.2 Basic Concepts
143
146
4.2 Sources of Data
AND
Summarizing
2.1 Introduction
141
C A S E 3.3 Correct Interpretation of Means
26
C A S E I. I Entertainment on a Cruise Ship
2
109
114
184
189
196
EduToys, Inc.
201
86
C A S E 2.2 Automobile Production
and Purchases
87
PART 2
C A S E 2.3 Saving, Spending, and Social
Climbing
88
3
Describing Data: Summary Measures
3.1 Introduction
89
3.2 Measures of Central Location
95
with Add-lns
101
205
208
5.4 An Introduction to Simulation
215
220
5.5 Distribution of Two Random Variables: Scenario
Approach
3.6 Obtaining Summary Measures
203
206
5.3 Distribution of a Single Random Variable
3.5 Measures of Variability: Variance and Standard
96
MAKING
Probability and Probability Distributions
5.2 Probability Essentials
92
95
3.4 Minimum, Maximum, and Range
Deviation
AND DECISION
5.1 Introduction
91
3.3 Quartiles and Percentiles
5
PROBABILITY, UNCERTAINTY,
224
5.6 Distribution of Two Random Variables: Joint
Probability Approach
230
5.7 Independent Random Variables
8.5 Conclusion
236
5.8 Weighted Sums of Random Variables
5.9 Conclusion
420
C A S E 8.1 Sampling from Videocassette
240
Renters
247
C A S E 5.1 Simpson's Paradox
254
9
429
Confidence Interval Estimation
Normal, Binomial, Poisson, and Exponential
9.1 Introduction
Distributions
9.2 Sampling Distributions
255
6.1 Introduction
257
6.2 The Normal Distribution
257
6.3 Applications of the Normal Distribution
6.4 The Binomial Distribution
266
453
456
471
9.9 Controlling Confidence Interval Length
9.10 Conclusion
312
485
C A S E 9.2 Employee Retention at D&Y
315
Restaurant
317
494
C A S E 9.4 The Bodfish Lot Cruise
10
342
495
Hypothesis Testing 497
10.1 Introduction
347
498
10.2 Concepts in Hypothesis Testing
362
499
10.3 Hypothesis Tests for a Population Mean
368
C A S E 7.1 Jogger Shoe Company
10.4 Hypothesis Tests for Other Parameters
380
C A S E 7.2 Westhouser Paper Company
C A S E 7.3 Biotechnical Engineering
10.5 Tests for Normality
381
10.8 Conclusion
STATISTICAL INFERENCE
385
Sampling and Sampling Distributions
546
554
C A S E 10.2 Baseball Statistics
388
513
541
C A S E I O. I Regression Toward the Mean
387
506
535
10.6 Chi-Square Test for Independence
382
10.7 One-WayANOVA
8.2 Sampling Terminology
493
330
7.6 Incorporating Attitudes Toward Risk
8.1 Introduction
492
C A S E 9.3 Delivery Times at SnowPea
7.5 Multistage Decision Problems
8
477
C A S E 9.1 Harrigan University Admissions
313
316
7.2 Elements of a Decision Analysis
PART 3
448
9.8 Confidence Interval for the Difference Between
305
Decision Making under Uncertainty
7.7 Conclusion
445
Proportions
C A S E 6.2 Cashing in on the Lottery
7.4 Bayes' Rule
9.4 Confidence Interval for a Total
Means
299
7.3 The PrecisionTree Add-In
439
9.7 Confidence Interval for the Difference Between
294
C A S E 6.1 EuroWatch Company
7.1 Introduction
9.3 Confidence Interval for a Mean
Deviation
283
6.7 Fitting a Probability Distribution to Data
6.8 Conclusion
433
9.6 Confidence Interval for a Standard
6.6 The Poisson and Exponential
with @ RISK
432
9.5 Confidence Interval for a Proportion
278
6.5 Applications of the Binomial Distribution
Distributions
431
560
561
C A S E 10.3 The Wichita Anti-Drunk Driving
Advertising Campaign
562
C A S E 10.4 Deciding Whether to Switch to a New
388
Toothpaste Dispenser
8.3 Methods for Selecting Random Samples
8.4 An Introduction to Estimation
403
389
564
C A S E 10.5 Removing Vioxx from the
Market
567
REGRESSION,
PART 4
AND T I M E
C A S E 12.4 Forecasting Overhead at Wagner
FORECASTING,
SERIES
Printers
569
13
11
711
Time Series Analysis and Forecasting
Regression Analysis: Estimating
1 Introduction
Relationships
2 Forecasting Methods: An Overview
I I.I
571
Introduction
572
575
11.5 Multiple Regression
585
7 Moving Averages
596
11.6 Modeling Possibilities
602
11.7 Validation of the Fit
628
741
746
8 Exponential Smoothing
9 Seasonal Models
10 Conclusion
630
729
737
6 Autoregression Models
11.4 Simple Linear Regression
11.8 Conclusion
5 The Random Walk Model
583
715
721
4 Regression-Based Trend Models
11.3 Correlations: Indicators of Linear
Relationships
714
3 Testing for Randomness
11.2 Scatterplots: Graphing Relationships
713
752
763
778
C A S E 13.1 Arrivals at the Credit Union
C A S E 11.1 Quantity Discounts at the Firm Chair
Company
784
C A S E 13.2 Forecasting Weekly Sales at
638
Amanta
785
C A S E 11.2 Housing Price Structure in
Mid City
639
C A S E 11.3 Demand for French Bread at Howie's
Bakery
640
C A S E 11.4 Investing for Retirement
641
PART 5
14
DECISION
Regression Analysis: Statistical Inference
12.1 Introduction
643
4.3 A Two-Variable Model
12.2 The Statistical Model
645
4.4 Sensitivity Analysis
12.3 Inferences About the Regression
Coefficients
12.4 Multicollinearity
4.7 A Product Mix Model
12.5 Include/Exclude Decisions
12.6 Stepwise Regression
12.8 Outliers
803
810
4.6 Infeasibility and Unboundedness
659
12.7 The Partial f Test
662
815
Models
833
4.10 A Decision Support System
12.11 Conclusion
696
686
4.11 Conclusion
C A S E 14.1 Shelby Shelving
707
834
836
Appendix Information on Solvers
C A S E 12.1 The Artsy Corporation
824
4.9 A Comparison of Algebraic and Spreadsheet
672
12.9 Violations of Regression Assumptions
691
813
4.8 A Multiperiod Production Model
667
680
12.10 Prediction
790
792
4.5 Properties of Linear Models
649
842
843
C A S E 14.2 Sonoma Valley Wines
845
C A S E 12.2 Heating Oil at Dupree Fuels
Company
789
790
4.2 Introduction to Optimization
645
787
Introduction to Optimization Modeling
14.1 Introduction
12
MODELING
709
C A S E 12.3 Developing a Flexible Budget at the
Gunderson Plant
710
15
Optimization Modeling: Applications
15.1 Introduction
847
848
15.2 Workforce Scheduling Models
849
15.3 Blending Models
856
C A S E 16.1 Ski Jacket Production
15.4 Logistics Models
862
C A S E 16.2 Ebony Bath Soap
15.5 Aggregate Planning Models
15.6 Financial Models
880
17.1 Introduction
15.7 Integer Programming Models
15.8 Nonlinear Models
15.9 Conclusion
17 Simulation Models
889
899
17.4 Marketing Models
C A S E 15.1 Giant Motor Company
C A S E I5.2GMS Stock Hedging
938
16 Introduction to Simulation Modeling
17.6 Conclusion
942
945
946
16.2 Real Applications of Simulation
947
16.3 Probability Distributions for Input
Variables
948
16.4 Simulation with Built-in Excel Tools
16.5 Introduction to ©RISK
976
16.6 The Effects of Input Distributions
on Results
991
16.7 Conclusion
999
1010
1024
1038
17.5 Simulating Games of Chance
940
C A S E 15.3 Durham Asset Management
16.1 Introduction
1010
17.3 Financial Models
931
964
1007
1009
17.2 Operations Models
918
1006
1051
1057
C A S E 17.1 College Fund Investment
C A S E 17.2 Bond Investment Strategy
1063
1064