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Data Analysis and Decision Making with Microsoft Excel / With CD-ROM

Data Analysis and Decision Making with Microsoft Excel / With CD-ROM - 99 edition

Data Analysis and Decision Making with Microsoft Excel / With CD-ROM - 99 edition

ISBN13: 9780534261245

ISBN10: 0534261248

Data Analysis and Decision Making with Microsoft Excel / With CD-ROM by S. Christian Albright, Wayne L. Winston and Christopher Zappe - ISBN 9780534261245
Cover type: Hardback
Edition: 99
Copyright: 1999
Publisher: Duxbury Press
Published: 1999
International: No
Data Analysis and Decision Making with Microsoft Excel / With CD-ROM by S. Christian Albright, Wayne L. Winston and Christopher Zappe - ISBN 9780534261245

ISBN13: 9780534261245

ISBN10: 0534261248

Cover type: Hardback
Edition: 99

List price: $325.00

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Summary

In response to the growing market trend in quantitative education, Albright, Winston, and Zappe's integrated business-statistics and management-science text presents core statistics and management-science methods in a modern, unified spreadsheet-oriented approach. With a focus on analyzing, not on techniques, the book covers business statistics with some essential managerial-science topics included. The example-based, Excel spreadsheet approach is useful in courses that combine traditional statistics and management-science topics though can be easily used for a one-term business statistics only course. The modeling and application focus, together with the Excel spreadsheet add-ins, provides a complete learning source for both students and practicing managers.

  • Outstanding commercial Excel software add-ins are included for statistics, decision analysis, and simulation. Includes Palisade Corporations' Decision Tools Suite (@Risk, Precision Tree, Best Fit, DataPro {for Statistics}, and SolverTable).
  • Over 800 real data-based exercises and case studies are included.
  • More than 300 practical examples are included, from finance, marketing, and operations.
  • The book offers comprehensive coverage for two major subjects (business statistics and management science) in a unified manner. The text-features an example-based spreadsheet approach to data analysis and modeling, a unique and timely change for many instructors.
  • Spreadsheet simulations are used to demonstrate statistical concepts visually throughout the book.
  • A pragmatic approach to data analysis is used, with emphasis on only those aspects that will help make students more valuable employees. Each chapter features a decision-making framework with examples designed to help students learn to analyze data with an eye toward effective decision-making as the end result.
  • The book shows students how to translate real business problems-expressed in words-into spreadsheets that list the inputs and decision variables. The authors then relate these to outputs by means of appropriate formulas.

Table of Contents

Table of Contents

PREFACE. 1. INTRODUCTION TO DATA ANALYSIS & DECISION MAKING.

Introduction.
An Overview of the Book.
A Sampling of Examples.
Modeling and Models.

Conclusion.
Case Study: Entertainment on a Cruise Ship.


2. DESCRIBING DATA: GRAPHS AND TABLES.

Introduction.
Basic Concepts.
Frequency Tables and Histograms.
Analyzing Relationships with Scatterplots.
Time Series Plots.
Exploring Data with Pivot Tables.

Conclusion.
Case Study: Customer Arrivals at Bank98.
Case Study: Automobile Production and Purchases.
Case Study: Saving, Spending, and Social Climbing.


3. DESCRIBING DATA: SUMMARY MEASURES.

Introduction.
Measures of Central Location.
Quartiles and Percentiles.
Minimum, Maximum, and Range.
Measures of Variability: Variance and Standard Deviation.
Obtaining Summary Measures with Add-Ins.
Measures of Association: Covariance and Correlation.
Describing Data Sets with Boxplots.
Applying the Tools.

Conclusion. Case Study: The Dow Jones Averages.
Case Study: Other Market Indexes.


4. PROBABILITY AND PROBABILITY DISTRIBUTIONS.

Introduction.
Probability Essentials.
Distribution of a Single Random Variable.
An Introduction to Simulation.
Subjective Versus Objective Probabilities.
Derived Probability Distributions.
Distribution of Two Random Variables: Scenario Approach.
Distribution of Two Random Variables: Joint Probability Approach.
Independent Random Variables.
Weighted Sums of Random Variables.

Conclusion. Case Study: Simpson's Paradox.


5. NORMAL, BINOMIAL, AND POISSON DISTRIBUTIONS.

Introduction.
The Normal Distribution.
Applications of the Normal Distribution.
The Binomial Distribution.
Applications of the Binomial Distribution.
The Poisson Distribution.
Fitting a Probability Distribution to Data: BestFit.

Conclusion.
Case Study: EuroWatch Company.
Case Study: Cashing in on the Lottery.


6. DECISION MAKING UNDER UNCERTAINTY.

Introduction.
Elements of a Decision Analysis.
The PrecisionTree Add-In.
Introduction to Influence Diagrams.
More Single-Stage Examples.
Multistage Decision Problems.
Bayes' Rule.
Incorporating Attitudes Toward Risk.

Conclusion.
Case Study: Jogger Shoe Company.


7. SAMPLING AND SAMPLING DISTRIBUTIONS.

Introduction.
Sampling Terminology.
Methods for Selecting Random Samples.
An Introduction to Estimation. Conclusion.

Case Study: Sampling from Videocassette Renters.


8. CONFIDENCE INTERVAL ESTIMATION.

Introduction. Sampling Distributions.
Confidence Interval for a Mean.
Confidence Interval for a Total.
Confidence Interval for a Proportion.
Confidence Interval for a Standard Deviation.
Confidence Interval for a Difference Between Means.
Confidence Interval for the Difference Between Proportions.
Controlling Confidence Interval Length.

Conclusion.
Case Study: Harrigan University Admissions.
Case Study: Employee Retention at D & Y. Delivery Times at SnowPea Restaurant.
Case Study: The Bodfish Lot Cruise.


9. HYPOTHESIS TESTING.

Introduction.
Concepts in Hypothesis Testing.
Hypothesis Tests for a Population Mean.
Hypothesis Tests for Other Parameters.
One-Way ANOVA.
Tests for Normality.

Conclusion.
Case Study: Regression Toward the Mean.
Case Study: Baseball Statistics.
Case Study: The Wichita Anti-Drunk Driving Advertising Campaign.


10. STATISTICAL PROCESS CONTROL.

Introduction.
Deming's 14 Points.
Basic Ideas Behind Control Charts.
Control Charts for Variables.
Control Charts for Attributes.
Process Capability.

Conclusion.
Case Study: The Lamination Process at Intergalactica.
Case Study: Paper Production for Fornax at the Pluto Mill.


11. REGRESSION ANALYSIS: ESTIMATING RELATIONSHIPS.

Introduction.
Scatterplots: Graphing Relationships.
Correlations: Indicators of Linear Relationships.
Simple Linear Regression.
Multiple Regression.
Modeling Possibilities.
Validation of the Fit.

Conclusion.
Case Study: Quantity Discounts at the FirmChair Company.
Case Study: Housing Price Structure in MidCity.
Case Study: Demand for French Bread at Howie's.
Case Study: Investing for Retirement.


12. REGRESSION ANALYSIS: STATISTICAL INFERENCE.

Introduction.
The Statistical Model.
Inferences About the Regression Coefficients.
Multicollinearity.
Include/Exclude Decisions.
Stepwise Regression.
A Test for the Overall Fit: The ANOVA Table.
The Partial F Test.
Outliers.
Violations of Regression Assumptions.
Prediction.

Conclusion.
Case Study: The Artsy Corporation.
Case Study: Heating Oil at Dupree Fuels Company.
Case Study: Developing a Flexible Budget at the Gunderson Plant.
Case Study: Forecasting Overhead at Wagner Printers.

13. TIME SERIES ANALYSIS AND FORECASTING.

Introduction.
Forecasting Methods: An Overview.
Random Series.
The Random Walk Model.
Autoregression Models.
Regression-Based Trend Models.
Moving Averages.
Exponential Smoothing.
Deseasonalizing: The Ratio-to-Moving-Averages Method.
Estimating Seasonality with Regression.
Econometric Models.

Conclusion.
Case Study: Arrivals at the Credit Union.
Case Study: Forecasting Weekly Sales at Amanta.


14. INTRODUCTION TO OPTIMIZATION MODELING.

Introduction.
A Brief History of Linear Programming.
Introduction to LP Modeling.
Sensitivity Analysis and the SolverTable Add-In.
The Linear Assumptions.
Graphical Solution Method.
Infeasibility and Unboundedness.
A Multiperiod Production Problem.
A Decision Support System.

Conclusion.
Case Study: Shelby Shelving.


15. OPTIMIZATION MODELING: APPLICATIONS.

Introduction.
Static Workforce Scheduling.
Blending Models.
Logistics Models.
Aggregate Planning Models.
A Dynamic Financial Model.
Integer Programming Models.
Nonlinear Models.

Conclusion.
Case Study: Giant Motor Company.
Case Study: GMS Stock Hedging.
Case Study: Durham Asset Management.


16. SIMULATION MODELS.

Introduction.
Random Numbers.
Introduction to Spreadsheet Simulation.
Simulating from Other Probability Distributions.
Simulating with @Risk.
A Financial Planning Model.
A Cash Balance Model.
Simulating Stock Prices and Options.
A Market Share Model.
Simulating Correlated Values.
Using TopRank with @Risk for Powerful Modeling.

Conclusion.
Case Study: Ski Jacket Production.
Case Study: The College Fund Investment Decision.
Case Study: Ebony Bath Soap.
Case Study: Bond Investment Strategy.


REFERENCES.
INDEX.

Other Editions of Data Analysis and Decision Making with Microsoft Excel / With CD-ROM

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