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Data Analysis and Decision Making 4th Revised edition [Multiple-component retail product]

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  • Formatas: Multiple-component retail product, 1080 pages, aukštis x plotis x storis: 261x211x44 mm, weight: 4600 g, Contains 1 Hardback and 1 Digital online
  • Išleidimo metai: 12-Oct-2010
  • Leidėjas: South-Western
  • ISBN-10: 0538476125
  • ISBN-13: 9780538476126
Kitos knygos pagal šią temą:
Data Analysis and Decision Making 4th Revised editionDidesnis paveikslėlis
  • Formatas: Multiple-component retail product, 1080 pages, aukštis x plotis x storis: 261x211x44 mm, weight: 4600 g, Contains 1 Hardback and 1 Digital online
  • Išleidimo metai: 12-Oct-2010
  • Leidėjas: South-Western
  • ISBN-10: 0538476125
  • ISBN-13: 9780538476126
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9780538476126.html
Raktažodžiai:
DATA ANALYSIS AND DECISION MAKING is a teach-by-example approach, learner-friendly writing style, and complete Excel integration focusing on data analysis, modeling, and spreadsheet use in statistics and management science. The Premium Online Content Website (accessed by a unique code with every new book) includes links to the following add-ins: the Palisade Decision Tools Suite (@RISK, StatTools, PrecisionTree, TopRank, RISKOptimizer, NeuralTools, and Evolver); and SolverTable, allowing users to do sensitivity analysis. All of the add-ins is revised for Excel 2007 and notes about Excel 2010 are added where applicable.
Preface xii
1 Introduction to Data Analysis and Decision Making
1(18)
1.1 Introduction
2(2)
1.2 An Overview of the Book
4(7)
1.2.1 The Methods
4(3)
1.2.2 The Software
7(4)
1.3 Modeling and Models
11(5)
1.3.1 Graphical Models
11(1)
1.3.2 Algebraic Models
12(1)
1.3.3 Spreadsheet Models
12(2)
1.3.4 A Seven-Step Modeling Process
14(2)
1.4 Conclusion
16(1)
Case 1.1 Entertainment on a Cruise Ship
17(2)
PART 1 Exploring Data
19(134)
2 Describing the Distribution of a Single Variable
21(64)
2.1 Introduction
23(1)
2.2 Basic Concepts
24(6)
2.2.1 Populations and Samples
24(1)
2.2.2 Data Sets, Variables, and Observations
25(2)
2.2.3 Types of Data
27(3)
2.3 Descriptive Measures for Categorical Variables
30(3)
2.4 Descriptive Measures for Numerical Variables
33(24)
2.4.1 Numerical Summary Measures
34(9)
2.4.2 Numerical Summary Measures with StatTools
43(5)
2.4.3 Charts for Numerical Variables
48(9)
2.5 Time Series Data
57(7)
2.6 Outliers and Missing Values
64(2)
2.6.1 Outliers
64(1)
2.6.2 Missing Values
65(1)
2.7 Excel Tables for Filtering, Sorting, and Summarizing
66(9)
2.7.1 Filtering
70(5)
2.8 Conclusion
75(6)
Case 2.1 Correct Interpretation of Means
81(1)
Case 2.2 The Dow Jones Industrial Average
82(1)
Case 2.3 Home and Condo Prices
83(2)
3 Finding Relationships among Variables
85(68)
3.1 Introduction
87(1)
3.2 Relationships among Categorical Variables
88(4)
3.3 Relationships among Categorical Variables and a Numerical Variable
92(9)
3.3.1 Stacked and Unstacked Formats
93(8)
3.4 Relationships among Numerical Variables
101(13)
3.4.1 Scatterplots
102(4)
3.4.2 Correlation and Covariance
106(8)
3.5 Pivot Tables
114(23)
3.6 An Extended Example
137(7)
3.7 Conclusion
144(5)
Case 3.1 Customer Arrivals at Bank98
149(1)
Case 3.2 Saving, Spending, and Social Climbing
150(1)
Case 3.3 Churn in the Cellular Phone Market
151(2)
PART 2 Probability And Decision Making Under Uncertainty
153(196)
4 Probability and Probability Distributions
155(54)
4.1 Introduction
156(2)
4.2 Probability Essentials
158(8)
4.2.1 Rule of Complements
159(1)
4.2.2 Addition Rule
159(1)
4.2.3 Conditional Probability and the Multiplication Rule
160(2)
4.2.4 Probabilistic Independence
162(1)
4.2.5 Equally Likely Events
163(1)
4.2.6 Subjective Versus Objective Probabilities
163(3)
4.3 Distribution of a Single Random Variable
166(7)
4.3.1 Conditional Mean and Variance
170(3)
4.4 An Introduction to Simulation
173(4)
4.5 Distribution of Two Random Variables: Scenario Approach
177(6)
4.6 Distribution of Two Random Variables: Joint Probability Approach
183(6)
4.6.1 How to Assess Joint Probability Distributions
187(2)
4.7 Independent Random Variables
189(4)
4.8 Weighted Sums of Random Variables
193(7)
4.9 Conclusion
200(8)
Case 4.1 Simpson's Paradox
208(1)
5 Normal, Binomial, Poisson, and Exponential Distributions
209(64)
5.1 Introduction
211(1)
5.2 The Normal Distribution
211(10)
5.2.1 Continuous Distributions and Density Functions
211(2)
5.2.2 The Normal Density
213(1)
5.2.3 Standardizing: Z-Values
214(2)
5.2.4 Normal Tables and Z-Values
216(1)
5.2.5 Normal Calculations in Excel
217(3)
5.2.6 Empirical Rules Revisited
220(1)
5.3 Applications of the Normal Distribution
221(12)
5.4 The Binomial Distribution
233(5)
5.4.1 Mean and Standard Deviation of the Binomial Distribution
236(1)
5.4.2 The Binomial Distribution in the Context of Sampling
236(1)
5.4.3 The Normal Approximation to the Binomial
237(1)
5.5 Applications of the Binomial Distribution
238(12)
5.6 The Poisson and Exponetial Distributions
250(5)
5.6.1 The Poisson Distribution
250(2)
5.6.2 The Exponential Distribution
252(3)
5.7 Fitting a Probability Distribution to Data with @RISK
255(6)
5.8 Conclusion
261(8)
Case 5.1 EuroWatch Company
269(1)
Case 5.2 Cashing in on the Lottery
270(3)
6 Decision Making under Uncertainty
273(76)
6.1 Introduction
274(2)
6.2 Elements of Decision Analysis
276(14)
6.2.1 Payoff Tables
276(1)
6.2.2 Possible Decision Criteria
277(1)
6.2.3 Expected Monetary Value (EMV)
278(2)
6.2.4 Sensitivity Analysis
280(1)
6.2.5 Decision Trees
280(2)
6.2.6 Risk Profiles
282(8)
6.3 The PrecisionTree Add-In
290(13)
6.4 Bayes' Rule
303(4)
6.5 Multistage Decision Problems
307(16)
6.5.1 The Value of Information
311(12)
6.6 Incorporating Attitudes Toward Risk
323(8)
6.6.1 Utility Functions
324(1)
6.6.2 Exponential Utility
324(4)
6.6.3 Certainty Equivalents
328(2)
6.6.4 Is Expected Utility Maximization Used?
330(1)
6.7 Conclusion
331(14)
Case 6.1 Jogger Shoe Company
345(1)
Case 6.2 Westhouser Parer Company
346(1)
Case 6.3 Biotechnical Engineering
347(2)
PART 3 Statistical Inference
349(178)
7 Sampling and Sampling Distributions
351(36)
7.1 Introduction
352(1)
7.2 Sampling Terminology
353(1)
7.3 Methods for Selecting Random Samples
354(12)
7.3.1 Simple Random Sampling
354(6)
7.3.2 Systematic Sampling
360(1)
7.3.3 Stratified Sampling
361(3)
7.3.4 Cluster Sampling
364(1)
7.3.5 Multistage Sampling Schemes
365(1)
7.4 An Introduction to Estimation
366(16)
7.4.1 Sources of Estimation Error
367(1)
7.4.2 Key Terms in Sampling
368(1)
7.4.3 Sampling Distribution of the Sample Mean
369(5)
7.4.4 The Central Limit Theorem
374(5)
7.4.5 Sample Size Determination
379(1)
7.4.6 Summary of Key Ideas for Simple Random Sampling
380(2)
7.5 Conclusion
382(4)
Case 7.1 Sampling from DVD Movie Renters
386(1)
8 Confidence Interval Estimation
387(68)
8.1 Introduction
388(2)
8.2 Sampling Distributions
390(4)
8.2.1 The t Distribution
390(3)
8.2.2 Other Sampling Distributions
393(1)
8.3 Confidence Interval for a Mean
394(6)
8.4 Confidence Interval for a Total
400(3)
8.5 Confidence Interval for a Proportion
403(6)
8.6 Confidence Interval for a Standard Deviation
409(3)
8.7 Confidence Interval for the Difference between Means
412(15)
8.7.1 Independent Samples
413(8)
8.7.2 Paired Samples
421(6)
8.8 Confidence Interval for the Difference between Proportions
427(6)
8.9 Controlling Confidence Interval Length
433(8)
8.9.1 Sample Size for Estimation of the Mean
434(2)
8.9.2 Sample Size for Estimation of Other Parameters
436(5)
8.10 Conclusion
441(8)
Case 8.1 Harrigan University Admissions
449(1)
Case 8.2 Employee Retention at D&Y
450(1)
Case 8.3 Delivery Times at SnowPea Restaurant
451(1)
Case 8.4 The Bodfish Lot Cruise
452(3)
9 Hypothesis Testing
455(72)
9.1 Introduction
456(1)
9.2 Concepts in Hypothesis Testing
457(7)
9.2.1 Null and Alternative Hypotheses
458(1)
9.2.2 One-Tailed Versus Two-Tailed Tests
459(1)
9.2.3 Types of Errors
459(1)
9.2.4 Significance Level and Rejection Region
460(1)
9.2.5 Significance from p-values
461(1)
9.2.6 Type II Errors and Power
462(1)
9.2.7 Hypothesis Tests and Confidence Intervals
463(1)
9.2.8 Practical Versus Significance
463(1)
9.3 Hypothesis Tests for a Population Mean
464(8)
9.4 Hypothesis Tests for Other Parameters
472(22)
9.4.1 Hypothesis Tests for a Population Proportion
472(3)
9.4.2 Hypothesis Tests for Differences between Population Means
475(10)
9.4.3 Hypothesis Tests for Equal Population Variances
485(1)
9.4.4 Hypothesis Tests for Differences between Population Proportions
486(8)
9.5 Tests for Normality
494(6)
9.6 Chi-Square Test for Indepedence
500(5)
9.7 One-Way ANOVA
505(8)
9.8 Conclusion
513(6)
Case 9.1 Regression Toward the Mean
519(1)
Case 9.2 Baseball Statistics
520(1)
Case 9.3 The Wichita Anti---Drunk Driving Advertising Campaign
521(2)
Case 9.4 Deciding Whether to Switch to a New Toothpaste Dispenser
523(3)
Case 9.5 Removing Vioxx from the Market
526(1)
PART 4 Regression Analysis And Time Series Forecasting
527(216)
10 Regression Analysis: Estimating Relationships
529(72)
10.1 Introduction
531(2)
10.2 Scatterplots: Graphing Relationships
533(7)
10.2.1 Linear Versus Nonlinear Relationships
538(1)
10.2.2 Outliers
538(1)
10.2.3 Unequal Variance
539(1)
10.2.4 No Relationship
540(1)
10.3 Correlations: Indicators of Linear Relationships
540(2)
10.4 Simple Linear Regression
542(11)
10.4.1 Least Squares Estimation
542(7)
10.4.2 Standard Error of Estimate
549(1)
10.4.3 The Percentage of Variation Explained: R2
550(3)
10.5 Multiple Regression
553(7)
10.5.1 Interpretation of Regression Coefficients
554(2)
10.5.2 Interpretation of Standard Error of Estimate and R2
556(4)
10.6 Modeling Possibilities
560(26)
10.6.1 Dummy Variables
560(6)
10.6.2 Interaction Variables
566(5)
10.6.3 Nonlinear Transformations
571(15)
10.7 Validation of the Fit
586(2)
10.8 Conclusion
588(8)
Case 10.1 Quantity Discounts at the Firm Chair Company
596(1)
Case 10.2 Housing Price Structure in Mid City
597(1)
Case 10.3 Demand for French Bread at Howie's Bakery
598(1)
Case 10.4 Investing for Retirement
599(2)
11 Regression Analysis: Statistical Inference
601(68)
11.1 Introduction
603(1)
11.2 The Statistical Model
603(4)
11.3 Inferences about the Regression Coefficients
607(9)
11.3.1 Sampling Distribution of the Regression Coefficients
608(2)
11.3.2 Hypothesis Tests for the Regression Coefficients and p-Values
610(1)
11.3.3 A Test for the Overall Fit: The ANOVA Table
611(5)
11.4 Multicollinearity
616(4)
11.5 Include/Exclude Decisions
620(5)
11.6 Stepwise Regression
625(5)
11.7 The Partial F Test
630(8)
11.8 Outliers
638(6)
11.9 Violations of Regression Assumptions
644(4)
11.9.1 Nonconstant Error Variance
644(1)
11.9.2 Nonnormality of Residuals
645(1)
11.9.3 Autocorrelated Residuals
645(3)
11.10 Prediction
648(5)
11.11 Conclusion
653(10)
Case 11.1 The Artsy Corporation
663(2)
Case 11.2 Heating Oil at Dupree Fuels Company
665(1)
Case 11.3 Developing a Flexible Budget at the Gunderson Plant
666(1)
Case 11.4 Forecasting Overhead at Wagner Printers
667(2)
12 Time Series Analysis and Forecasting
669(74)
12.1 Introduction
671(1)
12.2 Forecasting Methods: An Overview
671(7)
12.2.1 Extrapolation Methods
672(1)
12.2.2 Econometric Models
672(1)
12.2.3 Combining Forecasts
673(1)
12.2.4 Components of Time Series Data
673(3)
12.2.5 Measures of Accuracy
676(2)
12.3 Testing for Randomness
678(9)
12.3.1 The Runs Test
681(2)
12.3.2 Autocorrelation
683(4)
12.4 Regression-Based Trend Models
687(8)
12.4.1 Linear Trend
687(3)
12.4.2 Exponential Trend
690(5)
12.5 The Random Walk Model
695(4)
12.6 Autoregression Models
699(5)
12.7 Moving Averages
704(6)
12.8 Exponential Smoothing
710(10)
12.8.1 Simple Exponential Smoothing
710(5)
12.8.2 Holt's Model for Trend
715(5)
12.9 Seasonal Models
720(15)
12.9.1 Winters' Exponential Smoothing Model
721(4)
12.9.2 Deseasonalizing: The Ratio-to-Moving-Averages Method
725(4)
12.9.3 Estimating Seasonality with Regression
729(6)
12.10 Conclusion
735(5)
Case 12.1 Arrivals at the Credit Union
740(1)
Case 12.2 Forecasting Weekly Sales at Amanta
741(2)
PART 5 Optimization And Simulation Modeling
743
13 Introduction to Optimization Modeling
745(66)
13.1 Introduction
746(1)
13.2 Introduction to Optimization
747(1)
13.3 A Two-Variable Product Mix Model
748(13)
13.4 Sensitivity Analysis
761(11)
13.4.1 Solver's Sensitivity Report
761(4)
13.4.2 SolverTable Add-In
765(5)
13.4.3 Comparison of Solver's Sensitivity Report and SolverTable
770(2)
13.5 Properties of Linear Models
772(3)
13.5.1 Proportionality
773(1)
13.5.2 Additivity
773(1)
13.5.3 Divisibility
773(1)
13.5.4 Discussion of Linear Properties
773(1)
13.5.5 Linear Models and Scaling
774(1)
13.6 Infeasibility and Unboundedness
775(3)
13.6.1 Infeasibility
775(1)
13.6.2 Unboundedness
775(1)
13.6.3 Comparison of Infeasibility and Unboundedness
776(2)
13.7 A Larger Product Mix Model
778(8)
13.8 A Multiperiod Production Model
786(10)
13.9 A Comparison of Algebraic and Spreadsheet Models
796(1)
13.10 A Decision Support System
796(3)
13.11 Conclusion
799(8)
Case 13.1 Shelby Shelving
807(2)
Case 13.2 Sonoma Valley Wines
809(2)
14 Optimization Models
811(106)
14.1 Introduction
812(1)
14.2 Worker Scheduling Models
813(8)
14.3 Blending Models
821(7)
14.4 Logistics Models
828(20)
14.4.1 Transportation Models
828(9)
14.4.2 Other Logistics Models
837(11)
14.5 Aggregate Planning Models
848(9)
14.6 Financial Models
857(11)
14.7 Integer Programming Models
868(23)
14.7.1 Capital Budgeting Models
869(6)
14.7.2 Fixed-Cost Models
875(8)
14.7.3 Set-Covering Models
883(8)
14.8 Nonlinear Programming Models
891(14)
14.8.1 Basic Ideas of Nonlinear Optimization
891(1)
14.8.2 Managerial Economics Models
891(5)
14.8.3 Portfolio Optimization Models
896(9)
14.9 Conclusion
905(7)
Case 14.1 Giant Motor Company
912(2)
Case 14.2 GMS Stock Hedging
914(3)
15 Introduction to Simulation Modeling
917(70)
15.1 Introduction
918(2)
15.2 Probability Distributions for Input Variables
920(19)
15.2.1 Types of Probability Distributions
921(4)
15.2.2 Common Probability Distributions
925(4)
15.2.3 Using @RISK to Explore Probability Distributions
929(10)
15.3 Simulation and the Flaw of Averages
939(3)
15.4 Simulation with Built-In Excel Tools
942(11)
15.5 Introduction to the @RISK Add-in
953(16)
15.5.1 @RISK Features
953(1)
15.5.2 Loading @RISK
954(1)
15.5.3 @RISK Models with a Single Random Input Variable
954(9)
15.5.4 Some Limitations of @RISK
963(1)
15.5.5 @RISK Models with Several Random Input Variables
964(5)
15.6 The Effects of Input Distributions on Results
969(9)
15.6.1 Effect of the Shape of the Input Distribution(s)
969(3)
15.6.2 Effect of Correlated Input Variables
972(6)
15.7 Conclusion
978(7)
Case 15.1 Ski Jacket Production
985(1)
Case 15.2 Ebony Bath Soap
986(1)
16 Simulation Models
987
16.1 Introduction
989(1)
16.2 Operations Models
989(15)
16.2.1 Bidding for Contracts
989(4)
16.2.2 Warranty Costs
993(5)
16.2.3 Drug Production with Uncertain Yield
998(6)
16.3 Financial Models
1004(16)
16.3.1 Financial Planning Models
1004(5)
16.3.2 Cash Balance Models
1009(5)
16.3.3 Investment Models
1014(6)
16.4 Marketing Models
1020(16)
16.4.1 Models of Customer Loyalty
1020(10)
16.4.2 Marketing and Sales Models
1030(6)
16.5 Simulating Games of Chance
1036(8)
16.5.1 Simulating the Game of Craps
1036(3)
16.5.2 Simulating the NCAA Basketball Tournament
1039(5)
16.6 An Automated Template for @RISK Models
1044(1)
16.7 Conclusion
1045(8)
Case 16.1 College Fund Investment
1053(1)
Case 16.2 Bond Investment Strategy
1054
PART 6 Online Bonus Material
2 Using the Advanced Filter and Database Functions
1(1)
17 Importing Data into Excel
1(1)
17.1 Introduction
3(1)
17.2 Rearranging Excel Data
4(4)
17.3 Importing Text Data
8(6)
17.4 Importing Relational Database Data
14(16)
17.4.1 A Brief Introduction to Relational Databases
14(1)
17.4.2 Using Microsoft Query
15(13)
17.4.3 SQL Statements
28(2)
17.5 Web Queries
30(4)
17.6 Cleansing Data
34(8)
17.7 Conclusion
42(4)
Case 17.1 EduToys, Inc.
46
APPENDIX A Statistical Reporting
1
A.1 Introduction
1(1)
A.2 Suggestions for Good Statistical Reporting
2(4)
A.2.1 Planning
2(1)
A.2.2 Developing a Report
3(1)
A.2.3 Be Clear
4(1)
A.2.4 Be Concise
5(1)
A.2.5 Be Precise
5(1)
A.3 Examples of Statistical Reports
6(12)
A.4 Conclusion
18
References 1055(4)
Index 1059
Wayne L. Winston is Professor of Operations and Decision Technologies in the Kelley School of Business at Indiana University, where he has taught since 1975. Wayne received his B.S. degree in Mathematics from MIT and his Ph.D. degree in Operations Research from Yale. He has written the successful textbooks OPERATIONS RESEARCH: APPLICATIONS AND ALGORITHMS, MATHEMATICAL PROGRAMMING: APPLICATIONS AND ALGORITHMS, SIMULATION MODELING WITH @RISK, PRATICAL MANAGEMENT SCIENCE, DATA ANALYSIS FOR MANAGERS, SPREADSHEET MODELING AND APPLICATIONS, AND FINANCIAL MODELS USING SIMULATION AND OPTIMIZATION. Wayne has published over 20 articles in leading journals and has won many teaching awards, including the school-wide MBA award four times. His current interest is in showing how spreadsheet models can be used to solve business problems in all disciplines, particularly in finance and marketing. S. Christian Albright received his B.S. degree in mathematics from Stanford in 1968 and his Ph.D. in operations research from Stanford in 1972. Since then, he has been teaching in the Operations and Decision Technologies Department in the Kelley School of Business at Indiana University. He has taught courses in management science, computer simulation, and statistics to all levels of business students: undergraduates, MBAs, and doctoral students. His current interest is in spreadsheet modeling, including development of VBA applications in Excel(R). Dr. Albright has published more than 20 articles in leading operations research journals in the area of applied probability. He has also published a number of successful textbooks, including DATA ANALYSIS AND DECISION MAKING, DATA ANALYSIS FOR MANAGERS, and SPREADSHEET MODELING AND APPLICATIONS. Christopher J. Zappe earned his B.A. in Mathematics from DePauw University in 1983 and his M.B.A. and Ph.D. in Decision Sciences from Indiana University in 1987 and 1988, respectively. Since 1993, Professor Zappe has been serving as an associate professor in the decision sciences area of the Department of Management at Bucknell University (Lewisburg, PA). He has published articles in scholarly journals such as Managerial and Decision Economics, OMEGA, Naval Research Logistics, and Interfaces.