| Description |
xvi, 1061 pages : illustrations ; 26 cm |
| Contents |
Contents note continued: 14.1.Introduction -- 14.2.Worker Scheduling Models -- 14.3.Blending Models -- 14.4.Logistics Models -- 14.4.1.Transportation Models -- 14.4.2.Other Logistics Models -- 14.5.Aggregate Planning Models -- 14.6.Financial Models -- 14.7.Integer Programming Models -- 14.7.1.Capital Budgeting Models -- 14.7.2.Fixed-Cost Models -- 14.7.3.Set-Covering Models -- 14.8.Nonlinear Programming Models -- 14.8.1.Basic Ideas of Nonlinear Optimization -- 14.8.2.Managerial Economics Models -- 14.8.3.Portfolio Optimization Models -- 14.9.Conclusion -- Case 14.1 Giant Motor Company -- Case 14.2 GMS Stock Hedging -- 15.Introduction to Simulation Modeling -- 15.1.Introduction -- 15.2.Probability Distributions for Input Variables -- 15.2.1.Types of Probability Distributions -- 15.2.2.Common Probability Distributions -- 15.2.3.Using @RISK to Explore Probability Distributions -- 15.3.Simulation and the Flaw of Averages -- 15.4.Simulation with Built-In Excel Tools -- |
|
Contents note continued: 15.5.Introduction to the @RISK Add-in -- 15.5.1.@RISK Features -- 15.5.2.Loading @RISK -- 15.5.3.@RISK Models with a Single Random Input Variable -- 15.5.4.Some Limitations of @RISK -- 15.5.5.@RISK Models with Several Random Input Variables -- 15.6.The Effects of Input Distributions on Results -- 15.6.1.Effect of the Shape of the Input Distribution(s) -- 15.6.2.Effect of Correlated Input Variables -- 15.7.Conclusion -- Case 15.1 Ski Jacket Production -- Case 15.2 Ebony Bath Soap -- 16.Simulation Models -- 16.1.Introduction -- 16.2.Operations Models -- 16.2.1.Bidding for Contracts -- 16.2.2.Warranty Costs -- 16.2.3.Drug Production with Uncertain Yield -- 16.3.Financial Models -- 16.3.1.Financial Planning Models -- 16.3.2.Cash Balance Models -- 16.3.3.Investment Models -- 16.4.Marketing Models -- 16.4.1.Models of Customer Loyalty -- 16.4.2.Marketing and Sales Models -- 16.5.Simulating Games of Chance -- 16.5.1.Simulating the Game of Craps -- |
|
Contents note continued: 16.5.2.Simulating the NCAA Basketball Tournament -- 16.6.An Automated Template for @RISK Models -- 16.7.Conclusion -- Case 16.1 College Fund Investment -- Case 16.2 Bond Investment Strategy -- pt. 6 Online Bonus Material -- 2.Using the Advanced Filter and Database Functions -- 17.Importing Data into Excel -- 17.1.Introduction -- 17.2.Rearranging Excel Data -- 17.3.Importing Text Data -- 17.4.Importing Relational Database Data -- 17.4.1.A Brief Introduction to Relational Databases -- 17.4.2.Using Microsoft Query -- 17.4.3.SQL Statements -- 17.5.Web Queries -- 17.6.Cleansing Data -- 17.7.Conclusion -- Case 17.1 EduToys, Inc. -- APPENDIX A Statistical Reporting -- A.1.Introduction -- A.2.Suggestions for Good Statistical Reporting -- A.2.1.Planning -- A.2.2.Developing a Report -- A.2.3.Be Clear -- A.2.4.Be Concise -- A.2.5.Be Precise -- A.3.Examples of Statistical Reports -- A.4.Conclusion |
|
Contents note continued: 2.7.Excel Tables for Filtering, Sorting, and Summarizing -- 2.7.1.Filtering -- 2.8.Conclusion -- Case 2.1 Correct Interpretation of Means -- Case 2.2 The Dow Jones Industrial Average -- Case 2.3 Home and Condo Prices -- 3.Finding Relationships among Variables -- 3.1.Introduction -- 3.2.Relationships among Categorical Variables -- 3.3.Relationships among Categorical Variables and a Numerical Variable -- 3.3.1.Stacked and Unstacked Formats -- 3.4.Relationships among Numerical Variables -- 3.4.1.Scatterplots -- 3.4.2.Correlation and Covariance -- 3.5.Pivot Tables -- 3.6.An Extended Example -- 3.7.Conclusion -- Case 3.1 Customer Arrivals at Bank98 -- Case 3.2 Saving, Spending, and Social Climbing -- Case 3.3 Churn in the Cellular Phone Market -- pt. 2 Probability And Decision Making Under Uncertainty -- 4.Probability and Probability Distributions -- 4.1.Introduction -- 4.2.Probability Essentials -- 4.2.1.Rule of Complements -- 4.2.2.Addition Rule -- |
|
Contents note continued: 4.2.3.Conditional Probability and the Multiplication Rule -- 4.2.4.Probabilistic Independence -- 4.2.5.Equally Likely Events -- 4.2.6.Subjective Versus Objective Probabilities -- 4.3.Distribution of a Single Random Variable -- 4.3.1.Conditional Mean and Variance -- 4.4.An Introduction to Simulation -- 4.5.Distribution of Two Random Variables: Scenario Approach -- 4.6.Distribution of Two Random Variables: Joint Probability Approach -- 4.6.1.How to Assess Joint Probability Distributions -- 4.7.Independent Random Variables -- 4.8.Weighted Sums of Random Variables -- 4.9.Conclusion -- Case 4.1 Simpson's Paradox -- 5.Normal, Binomial, Poisson, and Exponential Distributions -- 5.1.Introduction -- 5.2.The Normal Distribution -- 5.2.1.Continuous Distributions and Density Functions -- 5.2.2.The Normal Density -- 5.2.3.Standardizing: Z-Values -- 5.2.4.Normal Tables and Z-Values -- 5.2.5.Normal Calculations in Excel -- 5.2.6.Empirical Rules Revisited -- |
|
Contents note continued: 5.3.Applications of the Normal Distribution -- 5.4.The Binomial Distribution -- 5.4.1.Mean and Standard Deviation of the Binomial Distribution -- 5.4.2.The Binomial Distribution in the Context of Sampling -- 5.4.3.The Normal Approximation to the Binomial -- 5.5.Applications of the Binomial Distribution -- 5.6.The Poisson and Exponetial Distributions -- 5.6.1.The Poisson Distribution -- 5.6.2.The Exponential Distribution -- 5.7.Fitting a Probability Distribution to Data with @RISK -- 5.8.Conclusion -- Case 5.1 EuroWatch Company -- Case 5.2 Cashing in on the Lottery -- 6.Decision Making under Uncertainty -- 6.1.Introduction -- 6.2.Elements of Decision Analysis -- 6.2.1.Payoff Tables -- 6.2.2.Possible Decision Criteria -- 6.2.3.Expected Monetary Value (EMV) -- 6.2.4.Sensitivity Analysis -- 6.2.5.Decision Trees -- 6.2.6.Risk Profiles -- 6.3.The PrecisionTree Add-In -- 6.4.Bayes' Rule -- 6.5.Multistage Decision Problems -- 6.5.1.The Value of Information -- |
|
Contents note continued: 6.6.Incorporating Attitudes Toward Risk -- 6.6.1.Utility Functions -- 6.6.2.Exponential Utility -- 6.6.3.Certainty Equivalents -- 6.6.4.Is Expected Utility Maximization Used? -- 6.7.Conclusion -- Case 6.1 Jogger Shoe Company -- Case 6.2 Westhouser Parer Company -- Case 6.3 Biotechnical Engineering -- pt. 3 Statistical Inference -- 7.Sampling and Sampling Distributions -- 7.1.Introduction -- 7.2.Sampling Terminology -- 7.3.Methods for Selecting Random Samples -- 7.3.1.Simple Random Sampling -- 7.3.2.Systematic Sampling -- 7.3.3.Stratified Sampling -- 7.3.4.Cluster Sampling -- 7.3.5.Multistage Sampling Schemes -- 7.4.An Introduction to Estimation -- 7.4.1.Sources of Estimation Error -- 7.4.2.Key Terms in Sampling -- 7.4.3.Sampling Distribution of the Sample Mean -- 7.4.4.The Central Limit Theorem -- 7.4.5.Sample Size Determination -- 7.4.6.Summary of Key Ideas for Simple Random Sampling -- 7.5.Conclusion -- Case 7.1 Sampling from DVD Movie Renters -- |
|
Contents note continued: 8.Confidence Interval Estimation -- 8.1.Introduction -- 8.2.Sampling Distributions -- 8.2.1.The t Distribution -- 8.2.2.Other Sampling Distributions -- 8.3.Confidence Interval for a Mean -- 8.4.Confidence Interval for a Total -- 8.5.Confidence Interval for a Proportion -- 8.6.Confidence Interval for a Standard Deviation -- 8.7.Confidence Interval for the Difference between Means -- 8.7.1.Independent Samples -- 8.7.2.Paired Samples -- 8.8.Confidence Interval for the Difference between Proportions -- 8.9.Controlling Confidence Interval Length -- 8.9.1.Sample Size for Estimation of the Mean -- 8.9.2.Sample Size for Estimation of Other Parameters -- 8.10.Conclusion -- Case 8.1 Harrigan University Admissions -- Case 8.2 Employee Retention at D&Y -- Case 8.3 Delivery Times at SnowPea Restaurant -- Case 8.4 The Bodfish Lot Cruise -- 9.Hypothesis Testing -- 9.1.Introduction -- 9.2.Concepts in Hypothesis Testing -- 9.2.1.Null and Alternative Hypotheses -- |
|
Contents note continued: 9.2.2.One-Tailed Versus Two-Tailed Tests -- 9.2.3.Types of Errors -- 9.2.4.Significance Level and Rejection Region -- 9.2.5.Significance from p-values -- 9.2.6.Type II Errors and Power -- 9.2.7.Hypothesis Tests and Confidence Intervals -- 9.2.8.Practical Versus Significance -- 9.3.Hypothesis Tests for a Population Mean -- 9.4.Hypothesis Tests for Other Parameters -- 9.4.1.Hypothesis Tests for a Population Proportion -- 9.4.2.Hypothesis Tests for Differences between Population Means -- 9.4.3.Hypothesis Tests for Equal Population Variances -- 9.4.4.Hypothesis Tests for Differences between Population Proportions -- 9.5.Tests for Normality -- 9.6.Chi-Square Test for Indepedence -- 9.7.One-Way ANOVA -- 9.8.Conclusion -- Case 9.1 Regression Toward the Mean -- Case 9.2 Baseball Statistics -- Case 9.3 The Wichita Anti---Drunk Driving Advertising Campaign -- Case 9.4 Deciding Whether to Switch to a New Toothpaste Dispenser -- |
|
Contents note continued: Case 10.2 Housing Price Structure in Mid City -- Case 10.3 Demand for French Bread at Howie's Bakery -- Case 10.4 Investing for Retirement -- 11.Regression Analysis: Statistical Inference -- 11.1.Introduction -- 11.2.The Statistical Model -- 11.3.Inferences about the Regression Coefficients -- 11.3.1.Sampling Distribution of the Regression Coefficients -- 11.3.2.Hypothesis Tests for the Regression Coefficients and p-Values -- 11.3.3.A Test for the Overall Fit: The ANOVA Table -- 11.4.Multicollinearity -- 11.5.Include/Exclude Decisions -- 11.6.Stepwise Regression -- 11.7.The Partial F Test -- 11.8.Outliers -- 11.9.Violations of Regression Assumptions -- 11.9.1.Nonconstant Error Variance -- 11.9.2.Nonnormality of Residuals -- 11.9.3.Autocorrelated Residuals -- 11.10.Prediction -- 11.11.Conclusion -- Case 11.1 The Artsy Corporation -- Case 11.2 Heating Oil at Dupree Fuels Company -- Case 11.3 Developing a Flexible Budget at the Gunderson Plant -- |
|
Contents note continued: Case 11.4 Forecasting Overhead at Wagner Printers -- 12.Time Series Analysis and Forecasting -- 12.1.Introduction -- 12.2.Forecasting Methods: An Overview -- 12.2.1.Extrapolation Methods -- 12.2.2.Econometric Models -- 12.2.3.Combining Forecasts -- 12.2.4.Components of Time Series Data -- 12.2.5.Measures of Accuracy -- 12.3.Testing for Randomness -- 12.3.1.The Runs Test -- 12.3.2.Autocorrelation -- 12.4.Regression-Based Trend Models -- 12.4.1.Linear Trend -- 12.4.2.Exponential Trend -- 12.5.The Random Walk Model -- 12.6.Autoregression Models -- 12.7.Moving Averages -- 12.8.Exponential Smoothing -- 12.8.1.Simple Exponential Smoothing -- 12.8.2.Holt's Model for Trend -- 12.9.Seasonal Models -- 12.9.1.Winters' Exponential Smoothing Model -- 12.9.2.Deseasonalizing: The Ratio-to-Moving-Averages Method -- 12.9.3.Estimating Seasonality with Regression -- 12.10.Conclusion -- Case 12.1 Arrivals at the Credit Union -- Case 12.2 Forecasting Weekly Sales at Amanta -- |
|
Contents note continued: Case 9.5 Removing Vioxx from the Market -- pt. 4 Regression Analysis And Time Series Forecasting -- 10.Regression Analysis: Estimating Relationships -- 10.1.Introduction -- 10.2.Scatterplots: Graphing Relationships -- 10.2.1.Linear Versus Nonlinear Relationships -- 10.2.2.Outliers -- 10.2.3.Unequal Variance -- 10.2.4.No Relationship -- 10.3.Correlations: Indicators of Linear Relationships -- 10.4.Simple Linear Regression -- 10.4.1.Least Squares Estimation -- 10.4.2.Standard Error of Estimate -- 10.4.3.The Percentage of Variation Explained: R2 -- 10.5.Multiple Regression -- 10.5.1.Interpretation of Regression Coefficients -- 10.5.2.Interpretation of Standard Error of Estimate and R2 -- 10.6.Modeling Possibilities -- 10.6.1.Dummy Variables -- 10.6.2.Interaction Variables -- 10.6.3.Nonlinear Transformations -- 10.7.Validation of the Fit -- 10.8.Conclusion -- Case 10.1 Quantity Discounts at the Firm Chair Company -- |
|
Contents note continued: pt. 5 Optimization And Simulation Modeling -- 13.Introduction to Optimization Modeling -- 13.1.Introduction -- 13.2.Introduction to Optimization -- 13.3.A Two-Variable Product Mix Model -- 13.4.Sensitivity Analysis -- 13.4.1.Solver's Sensitivity Report -- 13.4.2.SolverTable Add-In -- 13.4.3.Comparison of Solver's Sensitivity Report and SolverTable -- 13.5.Properties of Linear Models -- 13.5.1.Proportionality -- 13.5.2.Additivity -- 13.5.3.Divisibility -- 13.5.4.Discussion of Linear Properties -- 13.5.5.Linear Models and Scaling -- 13.6.Infeasibility and Unboundedness -- 13.6.1.Infeasibility -- 13.6.2.Unboundedness -- 13.6.3.Comparison of Infeasibility and Unboundedness -- 13.7.A Larger Product Mix Model -- 13.8.A Multiperiod Production Model -- 13.9.A Comparison of Algebraic and Spreadsheet Models -- 13.10.A Decision Support System -- 13.11.Conclusion -- Case 13.1 Shelby Shelving -- Case 13.2 Sonoma Valley Wines -- 14.Optimization Models -- |
|
Machine generated contents note: 1.Introduction to Data Analysis and Decision Making -- 1.1.Introduction -- 1.2.An Overview of the Book -- 1.2.1.The Methods -- 1.2.2.The Software -- 1.3.Modeling and Models -- 1.3.1.Graphical Models -- 1.3.2.Algebraic Models -- 1.3.3.Spreadsheet Models -- 1.3.4.A Seven-Step Modeling Process -- 1.4.Conclusion -- Case 1.1 Entertainment on a Cruise Ship -- pt. 1 Exploring Data -- 2.Describing the Distribution of a Single Variable -- 2.1.Introduction -- 2.2.Basic Concepts -- 2.2.1.Populations and Samples -- 2.2.2.Data Sets, Variables, and Observations -- 2.2.3.Types of Data -- 2.3.Descriptive Measures for Categorical Variables -- 2.4.Descriptive Measures for Numerical Variables -- 2.4.1.Numerical Summary Measures -- 2.4.2.Numerical Summary Measures with StatTools -- 2.4.3.Charts for Numerical Variables -- 2.5.Time Series Data -- 2.6.Outliers and Missing Values -- 2.6.1.Outliers -- 2.6.2.Missing Values -- |
| Notes |
Previous ed. published in 2009 as "Data analysis & decision making with Microsoft® Excel" |
| Bibliography |
Includes bibliographical references (pages 1055-1057) and index |
| SUBJECT |
Microsoft Excel (Computer file) http://id.loc.gov/authorities/names/n86025775
|
| Subject |
Decision making -- Computer programs.
|
|
Industrial management -- Statistical methods -- Computer programs.
|
| Author |
Broadie, Mark Nathan.
|
|
Winston, Wayne L.
|
|
Zappe, Christopher J. (Christopher James), 1961-
|
| LC no. |
2010930495 |
| ISBN |
0538476125 |
|
9780538476126 |
