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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

ISBN13: 978-0534261245

Cover of Data Analysis and Decision Making with Microsoft Excel / With CD-ROM 99 (ISBN 978-0534261245)
ISBN13: 978-0534261245
ISBN10: 0534261248
Cover type:
Edition/Copyright: 99
Publisher: Duxbury Press
Published: 1999
International: No

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

ISBN13: 978-0534261245

S. Christian Albright, Wayne L. Winston and Christopher Zappe

ISBN13: 978-0534261245
ISBN10: 0534261248
Cover type:
Edition/Copyright: 99
Publisher: Duxbury Press

Published: 1999
International: No
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

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.

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