# Brief Contents

```Brief Contents
1
Introduction to Data A n a l y s i s and D e c i s i o n Making
I
Part 1 Getting, Describing, and Summarizing Data
2 D e s c r i b i n g Data: Graphs and Tables
31
3 Describing Data: Summary M e a s u r e s
79
4 G e t t i n g the Right Data
135
Part 2 Probability, Uncertainty, and Decision Making
5 Probability and Probability Distributions
6
195
Normal, Binomial, P o i s s o n , and Exponential Distributions
7 Decision Making under U n c e r t a i n t y
305
Part 3 Statistical Inference
8 Sampling and Sampling Distributions
9 C o n f i d e n c e Interval Estimation
10 Hypothesis Testing
377
421
487
Part 4 Regression, Forecasting, and Time Series
11
Regression Analysis: Estimating Relationships
12 R e g r e s s i o n Analysis: Statistical Inference
13
Time Series Analysis and Forecasting
633
703
Part 5 Decision Modeling
14 Introduction to Optimization Modeling
779
15 Optimization Modeling: A p p l i c a t i o n s
837
16 Introduction to Simulation Modeling
935
17 Simulation M o d e l s
999
Appendix A Statistical Reporting
References
Index
1077
1074
1055
561
245
Contents
Introduction to Data Analysis and Decision Making
1.1 Introduction
2
1.2 An Overview of the Book
4
1.3 A Sampling of Examples
11
1.4 Modeling and Models
1.5 Conclusion
21
26
CASE I.I Entertainment on a Cruise Ship
RT I
27
GETTING, DESCRIBING, AND SUMMARIZING DATA 29
2
Describing Data: Graphs and Tables 31
2.1 Introduction 33
2.2 Basic Concepts
33
2.3 Frequency Tables and Histograms
38
2.4 Analyzing Relationships with Scatterplots
2.5 Time Series Graphs
52
2.6 Exploring Data with Pivot Tables
2.7 Conclusion
57
68
CASE 2.1 Customer Arrivals at Banl&lt;98
3
48
75
CASE 2.2 Automobile Production and Purchases
76
CASE 2.3 Saving, Spending, and Social Climbing
77
Describing Data: Summary Measures
3.1 Introduction
79
81
3.2 Measures of Central Location
3.3 Quartiles and Percentiles
82
85
3.4 Minimum, Maximum, and Range
85
3.5 Measures of Variability: Variance and Standard Deviation
3.6 Obtaining Summary Measures with Add-lns
91
3.7 Measures of Association: Covariance and Correlation
3.8 Describing Data Sets with Boxplots
3.9 Applying the Tools
3.10 Conclusion
99
104
124
CASE 3.1 The Dow Jones Averages
CASE 3.2 Other Market Indexes
131
133
CASE 3.3 Correct Interpretation of Means
134
95
Getting the Right Data
4.1 Introduction
135
136
4.2 Sources of Data
137
4.3 Using Excels AutoFilter
140
4.4 Complex Queries with the Advanced Filter
4.5 Importing External Data from Access
146
152
4.6 Creating Pivot Tables from External Data
4.7 Web Queries
165
4.8 Other Data Sources on the Web
4.9 Cleansing the Data
4.10 Conclusion
173
179
186
CASE 4.1 EduToys, Inc.
PART 2
163
191
PROBABILITY, UNCERTAINTY, AND DECISION MAKING
5
Probability and Probability Distributions
5.1 Introduction
193
195
196
5.2 Probability Essentials
197
5.3 Distribution of a Single Random Variable
5.4 An Introduction to Simulation
204
209
5.5 Distribution of Two Random Variables: Scenario Approach 213
5.6 Distribution of Two Random Variables: Joint Probability Approach
219
5.7 Independent Random Variables 225
5.8 Weighted Sums of Random Variables
5.9 Conclusion
236
CASE 5.1 Simpson's Paradox
6
229
243
Normal, Binomial, Poisson, and Exponential Distributions
6.1 Introduction
247
6.2 The Normal Distribution
247
6.3 Applications of the Normal Distribution
6.4 The Binomial Distribution
256
268
6.5 Applications of the Binomial Distribution
6.6 The Poisson and Exponential Distributions
273
284
6.7 Fitting a Probability Distribution to Data: BestFit
6.8 Conclusion
245
289
294
CASE 6.1 EuroWatch Company
301
CASE 6.2 Cashing in on the Lottery
302
VII
Decision Making under Uncertainty
7.1 Introduction
305
306
7.2 Elements of a Decision Analysis
7.3 The PrecisionTree Add-In
7.4 Bayes'Rule
307
320
332
7.5 Multistage Decision Problems
337
7.6 Incorporating Attitudes Toward Risk
7.7 Conclusion
352
358
CASE 7.1 Jogger Shoe Company
370
CASE 7.2 Westhouser Paper Company
CASE 7,3 Biotechnital Engineering
PART 3
STATISTICAL INFERENCE
8
371
372
375
Sampling and Sampling Distributions
8.1 Introduction
377
378
8.2 Sampling Terminology
378
8.3 Methods for Selecting Random Samples
8.4 An Introduction to Estimation
8.5 Conclusion
379
393
411
CASE 8.1 Sampling from Videocassette Renters
9
Confidence Interval Estimation
9.1 Introduction
419
421
422
9.2 Sampling Distributions
423
9.3 Confidence Interval for a Mean
429
9.4 Confidence Interval for a Total
435
9.5 Confidence Interval for a Proportion
438
9.6 Confidence Interval for a Standard Deviation
443
9.7 Confidence Interval for the Difference Between Means
446
9.8 Confidence Interval for the Difference Between Proportions
9.9 Controlling Confidence Interval Length
9.10 Conclusion
467
475
CASE 9.1 Harrigan University Admissions
CASE 9.2 Employee Retention at D&amp;Y
482
483
CASE 9.3 Delivery Times at SnowPea Restaurant
CASE 9.4 The Bodfish Lot Cruise
485
10 Hypothesis Testing 487
I O.I Introduction
488
10.2 Concepts in Hypothesis Testing
VIII
489
484
461
10.3 Hypothesis Tests for a Population Mean
496
10.4 Hypothesis Tests for Other Parameters
10.5 Tests for Normality
525
10.6 Chi-Square Test for Independence
10.7 One-Way ANOVA
10.8 Conclusion
503
531
537
544
CASE 10.1 Regression Toward the Mean
CASE 10.2 Baseball Statistics
551
552
CASE 10.3 The Wichita Anti-Drunk Driving Advertising Campaign
553
CASE 10.4 Deciding Whether to Switch to a New Toothpaste Dispenser
CASE 10.5 Removing Vioxx from the Market
PART 4
558
REGRESSION, FORECASTING, AND TIME SERIES
1 1 Regression Analysis: Estimating Relationships
1 1 . 1 Introduction
561
562
11.2 Scatterplots: Graphing Relationships
565
11.3 Correlations: Indicators of Linear Relationships
11.4 Simple Linear Regression
11.5 Multiple Regression
573
575
586
11.6 Modeling Possibilities
592
11.7 Validation of the Fit
618
11.8 Conclusion
620
CASE I I. I Quantity Discounts at the FirmChair Company
CASE I 1.2 Housing Price Structure in MidCity
CASE 11.3 Demand for French Bread at Howie's
CASE I 1.4 Investing for Retirement
12 Regression Analysis: Statistical Inference
12.1 Introduction
629
630
631
633
635
12.2 The Statistical Model
635
12.3 Inferences About the Regression Coefficients
12.4 Multicollinearity
649
12.5 Include/Exclude Decisions
12.6 Stepwise Regression
12.7 The Partial F Test
12.8 Outliers
559
652
657
662
670
12.9 Violations of Regression Assumptions
12.10 Prediction
681
12.11 Conclusion
686
676
639
628
555
CASE 12.1 The Artsy Corporation
697
CASE 12.2 Heating Oil at Dupree Fuels Company
699
CASE 12.3 Developing a Flexible Budget at the Gunderson Plant
CASE 12.4 Forecasting Overhead at Wagner Printers
701
13 Time Series Analysis and Forecasting 703
13.1 Introduction
704
13.2 Forecasting Methods: An Overview
13.3 Testing for Randomness
711
13.4 Regression-Based Trend Models
13.5 The Random Walk Model
13.6 Autoregression Models
13.7 Moving Averages
719
727
731
736
13.8 Exponential Smoothing
13.9 Seasonal Models
13.10 Conclusion
705
742
753
768
CASE 13.1 Arrivals at the Credit Union
774
CASE 13.2 Forecasting Weekly Sales at Amanta
PART 5
775
DECISION MODELING 777
14 Introduction to Optimization Modeling 779
14.1 Introduction
780
14.2 Introduction to Optimization
14.3 A Two-Variable Model
14.4 Sensitivity Analysis
780
782
793
14.5 Properties of Linear Models
800
14.6 Infeasibility and Unboundedness
14.7 A Product Mix Model
803
805
14.8 A Multiperiod Production Model
814
14.9 A Comparison of Algebraic and Spreadsheet Models
14.10 A Decision Support System
1 4 . 1 1 Conclusion
824
826
Appendix Information on Solvers
CASE 14.1 Shelby Shelving
832
833
CASE 14.2 Sonoma Valley Wines
835
15 Optimization Modeling: Applications 837
15.1 Introduction
838
15.2 Workforce Scheduling Models
839
823
700
15.3 Blending Models
846
15.4 Logistics Models
852
15.5 Aggregate Planning Models
15.6 Financial Models
870
879
15.7 Integer Programming Models
15.8 Nonlinear Models
15.9 Conclusion
889
908
921
CASE 15.1 Giant Motor Company
CASE I5.2GMS Stock Hedging
928
930
CASE 15.3 Durham Asset Management
932
16 Introduction to Simulation Modeling
16.1 Introduction
935
936
16.2 Real Applications of Simulation
937
16.3 Probability Distributions for Input Variables
16.4 Simulation with Built-in Excel Tools
16.5 Introduction to @RISK
938
954
966
16.6 The Effects of Input Distributions on Results
16.7 Conclusion
989
CASE 16.1 Ski Jacket Production
CASE 16.2 Ebony Bath Soap
17 Simulation Models
17.1
981
996
997
999
Introduction
17.2 Operations Models
17.3 Financial Models
17.4 Marketing Models
1014
1028
17.5 Simulating Games of Chance
17.6 Conclusion
1041
1047
CASE 17.1 College Fund Investment
1053
CASE 17.2 Bond Investment Strategy
Appendix A
Statistical Reporting
A.I Introduction
1054
1055
1055
A.2 Suggestions for Good Statistical Reporting
A.3 Examples of Statistical Reports
A.4 Conclusion
References
Index
1056
1061
1073
1074
1077
XI
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