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Data Analysis and Decision Making with Microsoft Excel (with CD-ROM, InfoTrac, and Decision Tools and Statistic Tools Suite),9780324400823
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Data Analysis and Decision Making with Microsoft Excel (with CD-ROM, InfoTrac, and Decision Tools and Statistic Tools Suite)

by ; ;
Edition:
3rd
ISBN13:

9780324400823

ISBN10:
0324400829
Format:
Hardcover
Pub. Date:
8/1/2005
Publisher(s):
South-Western College Pub

Questions About This Book?

What version or edition is this?
This is the 3rd edition with a publication date of 8/1/2005.
What is included with this book?
  • The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any CDs, lab manuals, study guides, etc.

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Summary

Master data analysis, modeling, and spreadsheet use with DATA ANALYSIS AND DECISION MAKING WITH MICROSOFT EXCEL! With a teach-by-example approach, student-friendly writing style, and complete Excel integration, this business statistics text provides you with the tools you need to succeed. Margin notes, boxed-in definitions and formulas in the text, enhanced explanations in the text itself, and stated objectives for the examples found throughout the text make studying easy. Problem sets and cases provide realistic examples that enable you to see the relevance of the material to your future as a business leader.

Table of Contents

Introduction to Data Analysis and Decision Making
1(28)
Introduction
2(2)
An Overview of the Book
4(7)
A Sampling of Examples
11(10)
Modeling and Models
21(5)
Conclusion
26(3)
Case 1.1 Entertainment on a Cruise Ship
27(2)
PART 1 Getting, Describing, and Summarizing Data
29(164)
Describing Data: Graphs and Tables
31(48)
Introduction
33(1)
Basic Concepts
33(5)
Frequency Tables and Histograms
38(10)
Analyzing Relationships with Scatterplots
48(4)
Time Series Graphs
52(5)
Exploring Data with Pivot Tables
57(11)
Conclusion
68(11)
Case 2.1 Customer Arrivals at Bank98
75(1)
Case 2.2 Automobile Production and Purchases
76(1)
Case 2.3 Saving, Spending, and Social Climbing
77(2)
Describing Data: Summary Measures
79(56)
Introduction
81(1)
Measures of Central Location
82(3)
Quartiles and Percentiles
85(1)
Minimum, Maximum, and Range
85(1)
Measures of Variability: Variance and Standard Deviation
86(5)
Obtaining Summary Measures with Add-Ins
91(4)
Measures of Association: Covariance and Correlation
95(4)
Describing Data Sets with Boxplots
99(5)
Applying the Tools
104(20)
Conclusion
124(11)
Case 3.1 The Dow Jones Averages
131(2)
Case 3.2 Other Market Indexes
133(1)
Case 3.3 Correct Interpretation of Means
134(1)
Getting the Right Data
135(58)
Introduction
136(1)
Sources of Data
137(3)
Using Excel's AutoFilter
140(6)
Complex Queries with the Advanced Filter
146(6)
Importing External Data from Access
152(11)
Creating Pivot Tables from External Data
163(2)
Web Queries
165(8)
Other Data Sources on the Web
173(6)
Cleansing the Data
179(7)
Conclusion
186(7)
Case 4.1 EduToys, Inc.
191(2)
PART 2 Probability, Uncertainty, and Decision Making
193(182)
Probability and Probability Distributions
195(50)
Introduction
196(1)
Probability Essentials
197(7)
Distribution of a Single Random Variable
204(5)
An Introduction to Simulation
209(4)
Distribution of Two Random Variables: Scenario Approach
213(6)
Distribution of Two Random Variables: Joint Probability Approach
219(6)
Independent Random Variables
225(4)
Weighted Sums of Random Variables
229(7)
Conclusion
236(9)
Case 5.1 Simpson's Paradox
243(2)
Normal, Binomial, Poisson, and Exponential Distributions
245(60)
Introduction
247(1)
The Normal Distribution
247(9)
Applications of the Normal Distribution
256(12)
The Binomial Distribution
268(5)
Applications of the Binomial Distribution
273(11)
The Poisson and Exponential Distributions
284(5)
Fitting a Probability Distribution to Data: BestFit
289(5)
Conclusion
294(11)
Case 6.1 EuroWatch Company
301(1)
Case 6.2 Cashing in on the Lottery
302(3)
Decision Making under Uncertainty
305(70)
Introduction
306(1)
Elements of a Decision Analysis
307(13)
The PrecisionTree Add-In
320(12)
Bayes' Rule
332(5)
Multistage Decision Problems
337(15)
Incorporating Attitudes Toward Risk
352(6)
Conclusion
358(17)
Case 7.1 Jogger Shoe Company
370(1)
Case 7.2 Westhouser Paper Company
371(1)
Case 7.3 Biotechnical Engineering
372(3)
PART 3 Statistical Inference
375(184)
Sampling and Sampling Distributions
377(44)
Introduction
378(1)
Sampling Terminology
378(1)
Methods for Selecting Random Samples
379(14)
An Introduction to Estimation
393(18)
Conclusion
411(10)
Case 8.1 Sampling from Videocassette Renters
419(2)
Confidence Interval Estimation
421(66)
Introduction
422(1)
Sampling Distributions
423(6)
Confidence Interval for a Mean
429(6)
Confidence Interval for a Total
435(3)
Confidence Interval for a Proportion
438(5)
Confidence Interval for a Standard Deviation
443(3)
Confidence Interval for the Difference Between Means
446(15)
Confidence Interval for the Difference Between Proportions
461(6)
Controlling Confidence Interval Length
467(8)
Conclusion
475(12)
Case 9.1 Harrigan University Admissions
482(1)
Case 9.2 Employee Retention at D&Y
483(1)
Case 9.3 Delivery Times at SnowPea Restaurant
484(1)
Case 9.4 The Bodfish Lot Cruise
485(2)
Hypothesis Testing
487(72)
Introduction
488(1)
Concepts in Hypothesis Testing
489(7)
Hypothesis Tests for a Population Mean
496(7)
Hypothesis Tests for Other Parameters
503(22)
Tests for Normality
525(6)
Chi-Square Test for Independence
531(6)
One-Way ANOVA
537(7)
Conclusion
544(15)
Case 10.1 Regression Toward the Mean
551(1)
Case 10.2 Baseball Statistics
552(1)
Case 10.3 The Wichita Anti-Drunk Driving Advertising Campaign
553(2)
Case 10.4 Deciding Whether to Switch to a New Toothpaste Dispenser
555(3)
Case 10.5 Removing Vioxx from the Market
558(1)
PART 4 Regression, Forecasting, and Time Series
559(218)
Regression Analysis: Estimating Relationships
561(72)
Introduction
562(3)
Scatterplots: Graphing Relationships
565(8)
Correlations: Indicators of Linear Relationships
573(2)
Simple Linear Regression
575(11)
Multiple Regression
586(6)
Modeling Possibilities
592(26)
Validation of the Fit
618(2)
Conclusion
620(13)
Case 11.1 Quantity Discounts at the FirmChair Company
628(1)
Case 11.2 Housing Price Structure in MidCity
629(1)
Case 11.3 Demand for French Bread at Howie's
630(1)
Case 11.4 Investing for Retirement
631(2)
Regression Analysis: Statistical Inference
633(70)
Introduction
635(1)
The Statistical Model
635(4)
Inferences About the Regression Coefficients
639(10)
Multicollinearity
649(3)
Include/Exclude Decisions
652(5)
Stepwise Regression
657(5)
The Partial F Test
662(8)
Outliers
670(6)
Violations of Regression Assumptions
676(5)
Prediction
681(5)
Conclusion
686(17)
Case 12.1 The Artsy Corporation
697(2)
Case 12.2 Heating Oil at Dupree Fuels Company
699(1)
Case 12.3 Developing a Flexible Budget at the Gunderson Plant
700(1)
Case 12.4 Forecasting Overhead at Wagner Printers
701(2)
Time Series Analysis and Forecasting
703(74)
Introduction
704(1)
Forecasting Methods: An Overview
705(6)
Testing for Randomness
711(8)
Regression-Based Trend Models
719(8)
The Random Walk Model
727(4)
Autoregression Models
731(5)
Moving Averages
736(6)
Exponential Smoothing
742(11)
Seasonal Models
753(15)
Conclusion
768(9)
Case 13.1 Arrivals at the Credit Union
774(1)
Case 13.2 Forecasting Weekly Sales at Amanta
775(2)
PART 5 Decision Modeling
777(278)
Introduction to Optimization Modeling
779(58)
Introduction
780(1)
Introduction to Optimization
780(2)
A Two-Variable Model
782(11)
Sensitivity Analysis
793(7)
Properties of Linear Models
800(3)
Infeasibility and Unboundedness
803(2)
A Product Mix Model
805(9)
A Multiperiod Production Model
814(9)
A Comparison of Algebraic and Spreadsheet Models
823(1)
A Decision Support System
824(2)
Conclusion
826(11)
Appendix Information on Solvers
832(1)
Case 14.1 Shelby Shelving
833(2)
Case 14.2 Sonoma Valley Wines
835(2)
Optimization Modeling: Applications
837(98)
Introduction
838(1)
Workforce Scheduling Models
839(7)
Blending Models
846(6)
Logistics Models
852(18)
Aggregate Planning Models
870(9)
Financial Models
879(10)
Integer Programming Models
889(19)
Nonlinear Models
908(13)
Conclusion
921(14)
Case 15.1 Giant Motor Company
928(2)
Case 15.2 GMS Stock Hedging
930(2)
Case 15.3 Durham Asset Management
932(3)
Introduction to Simulation Modeling
935(64)
Introduction
936(1)
Real Applications of Simulation
937(1)
Probability Distributions for Input Variables
938(16)
Simulation with Built-In Excel Tools
954(12)
Introduction to @ RISK
966(15)
The Effects of Input Distributions on Results
981(8)
Conclusion
989(10)
Case 16.1 Ski Jacket Production
996(1)
Case 16.2 Ebony Bath Soap
997(2)
Simulation Models
999(56)
Introduction
1000(1)
Operations Models
1000(14)
Financial Models
1014(14)
Marketing Models
1028(13)
Simulating Games of Chance
1041(6)
Conclusion
1047(8)
Case 17.1 College Fund Investment
1053(1)
Case 17.2 Bond Investment Strategy
1054(1)
Appendix A Statistical Reporting
1055(19)
Introduction
1055(1)
Suggestions for Good Statistical Reporting
1056(5)
Examples of Statistical Reports
1061(12)
Conclusion
1073(1)
References 1074(3)
Index 1077


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