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Quantitative Analysis for Management, Global Edition.

For courses in management science and decision modeling.Foundational understanding of management science through real-world problems and solutions Quantitative Analysis for Management helps students to develop a real-world understanding of business analytics, quantitative methods, and management sci...

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Bibliographic Details
Main Author: Render, Barry.
Other Authors: Stair, Ralph M., Jr., Hanna, Michael E., Hale, Trevor S.
Format: eBook
Language:English
Published: Harlow, United Kingdom : Pearson Education, Limited, 2018.
Edition:13th ed.
Subjects:
Management science-Case studies..
Operations research-Case studies..
Management science.
Electronic books.
Online Access:View fulltext via EzAccess
Table of Contents:
  • Cover
  • Title Page
  • Copyright Page
  • About the Authors
  • Brief Contents
  • Contents
  • Preface
  • Acknowledgments
  • Chapter 1: Introduction to Quantitative Analysis
  • 1.1. What Is Quantitative Analysis?
  • 1.2. Business Analytics
  • 1.3. The Quantitative Analysis Approach
  • Defining the Problem
  • Developing a Model
  • Acquiring Input Data
  • Developing a Solution
  • Testing the Solution
  • Analyzing the Results and Sensitivity Analysis
  • Implementing the Results
  • The Quantitative Analysis Approach and Modeling in the Real World
  • 1.4. How to Develop a Quantitative Analysis Model
  • The Advantages of Mathematical Modeling
  • Mathematical Models Categorized by Risk
  • 1.5. The Role of Computers and Spreadsheet Models in the Quantitative Analysis Approach
  • 1.6. Possible Problems in the Quantitative Analysis Approach
  • Defining the Problem
  • Developing a Model
  • Acquiring Input Data
  • Developing a Solution
  • Testing the Solution
  • Analyzing the Results
  • 1.7. Implementation-Not Just the Final Step
  • Lack of Commitment and Resistance to Change
  • Lack of Commitment by Quantitative Analysts
  • Summary
  • Glossary
  • Key Equations
  • Self-Test
  • Discussion Questions and Problems
  • Case Study: Food and Beverages at Southwestern University Football Games
  • Bibliography
  • Chapter 2: Probability Concepts and Applications
  • 2.1. Fundamental Concepts
  • Two Basic Rules of Probability
  • Types of Probability
  • Mutually Exclusive and Collectively Exhaustive Events
  • Unions and Intersections of Events
  • Probability Rules for Unions, Intersections, and Conditional Probabilities
  • 2.2. Revising Probabilities with Bayes' Theorem
  • General Form of Bayes' Theorem
  • 2.3. Further Probability Revisions
  • 2.4. Random Variables
  • 2.5. Probability Distributions
  • Probability Distribution of a Discrete Random Variable.
  • Expected Value of a Discrete Probability Distribution
  • Variance of a Discrete Probability Distribution
  • Probability Distribution of a Continuous Random Variable
  • 2.6. The Binomial Distribution
  • Solving Problems with the Binomial Formula
  • Solving Problems with Binomial Tables
  • 2.7. The Normal Distribution
  • Area Under the Normal Curve
  • Using the Standard Normal Table
  • Haynes Construction Company Example
  • The Empirical Rule
  • 2.8. The F Distribution
  • 2.9. The Exponential Distribution
  • Arnold's Muffler Example
  • 2.10. The Poisson Distribution
  • Summary
  • Glossary
  • Key Equations
  • Solved Problems
  • Self-Test
  • Discussion Questions and Problems
  • Case Study: WTVX
  • Bibliography
  • Appendix 2.1: Derivation of Bayes' Theorem
  • Chapter 3: Decision Analysis
  • 3.1. The Six Steps in Decision Making
  • 3.2. Types of Decision-Making Environments
  • 3.3. Decision Making Under Uncertainty
  • Optimistic
  • Pessimistic
  • Criterion of Realism (Hurwicz Criterion)
  • Equally Likely (Laplace)
  • Minimax Regret
  • 3.4. Decision Making Under Risk
  • Expected Monetary Value
  • Expected Value of Perfect Information
  • Expected Opportunity Loss
  • Sensitivity Analysis
  • A Minimization Example
  • 3.5. Using Software for Payoff Table Problems
  • QM for Windows
  • Excel QM
  • 3.6. Decision Trees
  • Efficiency of Sample Information
  • Sensitivity Analysis
  • 3.7. How Probability Values Are Estimated by Bayesian Analysis
  • Calculating Revised Probabilities
  • Potential Problem in Using Survey Results
  • 3.8. Utility Theory
  • Measuring Utility and Constructing a Utility Curve
  • Utility as a Decision-Making Criterion
  • Summary
  • Glossary
  • Key Equations
  • Solved Problems
  • Self-Test
  • Discussion Questions and Problems
  • Case Study: Starting Right Corporation
  • Case Study: Toledo Leather Company
  • Case Study: Blake Electronics.
  • Bibliography
  • Chapter 4: Regression Models
  • 4.1. Scatter Diagrams
  • 4.2. Simple Linear Regression
  • 4.3. Measuring the Fit of the Regression Model
  • Coefficient of Determination
  • Correlation Coefficient
  • 4.4. Assumptions of the Regression Model
  • Estimating the Variance
  • 4.5. Testing the Model for Significance
  • Triple A Construction Example
  • The Analysis of Variance (ANOVA) Table
  • Triple A Construction ANOVA Example
  • 4.6. Using Computer Software for Regression
  • Excel 2016
  • Excel QM
  • QM for Windows
  • 4.7. Multiple Regression Analysis
  • Evaluating the Multiple Regression Model
  • Jenny Wilson Realty Example
  • 4.8. Binary or Dummy Variables
  • 4.9. Model Building
  • Stepwise Regression
  • Multicollinearity
  • 4.10. Nonlinear Regression
  • 4.11. Cautions and Pitfalls in Regression Analysis
  • Summary
  • Glossary
  • Key Equations
  • Solved Problems
  • Self-Test
  • Discussion Questions and Problems
  • Case Study: North-South Airline
  • Bibliography
  • Appendix 4.1: Formulas for Regression Calculations
  • Chapter 5: Forecasting
  • 5.1. Types of Forecasting Models
  • Qualitative Models
  • Causal Models
  • Time-Series Models
  • 5.2. Components of a Time-Series
  • 5.3. Measures of Forecast Accuracy
  • 5.4. Forecasting Models-Random Variations Only
  • Moving Averages
  • Weighted Moving Averages
  • Exponential Smoothing
  • Using Software for Forecasting Time Series
  • 5.5. Forecasting Models-Trend and Random Variations
  • Exponential Smoothing with Trend
  • Trend Projections
  • 5.6. Adjusting for Seasonal Variations
  • Seasonal Indices
  • Calculating Seasonal Indices with No Trend
  • Calculating Seasonal Indices with Trend
  • 5.7. Forecasting Models-Trend, Seasonal, and Random Variations
  • The Decomposition Method
  • Software for Decomposition
  • Using Regression with Trend and Seasonal Components.
  • 5.8. Monitoring and Controlling Forecasts
  • Adaptive Smoothing
  • Summary
  • Glossary
  • Key Equations
  • Solved Problems
  • Self-Test
  • Discussion Questions and Problems
  • Case Study: Forecasting Attendance at SWU Football Games
  • Case Study: Forecasting Monthly Sales
  • Bibliography
  • Chapter 6: Inventory Control Models
  • 6.1. Importance of Inventory Control
  • Decoupling Function
  • Storing Resources
  • Irregular Supply and Demand
  • Quantity Discounts
  • Avoiding Stockouts and Shortages
  • 6.2. Inventory Decisions
  • 6.3. Economic Order Quantity: Determining How Much to Order
  • Inventory Costs in the EOQ Situation
  • Finding the EOQ
  • Sumco Pump Company Example
  • Purchase Cost of Inventory Items
  • Sensitivity Analysis with the EOQ Model
  • 6.4. Reorder Point: Determining When to Order
  • 6.5. EOQ Without the Instantaneous Receipt Assumption
  • Annual Carrying Cost for Production Run Model
  • Annual Setup Cost or Annual Ordering Cost
  • Determining the Optimal Production Quantity
  • Brown Manufacturing Example
  • 6.6. Quantity Discount Models
  • Brass Department Store Example
  • 6.7. Use of Safety Stock
  • 6.8. Single-Period Inventory Models
  • Marginal Analysis with Discrete Distributions
  • Café du Donut Example
  • Marginal Analysis with the Normal Distribution
  • Newspaper Example
  • 6.9. ABC Analysis
  • 6.10. Dependent Demand: The Case for Material Requirements Planning
  • Material Structure Tree
  • Gross and Net Material Requirements Plans
  • Two or More End Products
  • 6.11. Just-In-Time Inventory Control
  • 6.12. Enterprise Resource Planning
  • Summary
  • Glossary
  • Key Equations
  • Solved Problems
  • Self-Test
  • Discussion Questions and Problems
  • Case Study: Martin-Pullin Bicycle Corporation
  • Bibliography
  • Appendix 6.1: Inventory Control with QM for Windows.
  • Chapter 7: Linear Programming Models: Graphical and Computer Methods
  • 7.1. Requirements of a Linear Programming Problem
  • 7.2. Formulating LP Problems
  • Flair Furniture Company
  • 7.3. Graphical Solution to an LP Problem
  • Graphical Representation of Constraints
  • Isoprofit Line Solution Method
  • Corner Point Solution Method
  • Slack and Surplus
  • 7.4. Solving Flair Furniture's LP Problem Using QM for Windows, Excel 2016, and Excel QM
  • Using QM for Windows
  • Using Excel's Solver Command to Solve LP Problems
  • Using Excel QM
  • 7.5. Solving Minimization Problems
  • Holiday Meal Turkey Ranch
  • 7.6. Four Special Cases in LP
  • No Feasible Solution
  • Unboundedness
  • Redundancy
  • Alternate Optimal Solutions
  • 7.7. Sensitivity Analysis
  • High Note Sound Company
  • Changes in the Objective Function Coefficient
  • QM for Windows and Changes in Objective Function Coefficients
  • Excel Solver and Changes in Objective Function Coefficients
  • Changes in the Technological Coefficients
  • Changes in the Resources or Right-Hand-Side Values
  • QM for Windows and Changes in Right-Hand- Side Values
  • Excel Solver and Changes in Right-Hand-Side Values
  • Summary
  • Glossary
  • Solved Problems
  • Self-Test
  • Discussion Questions and Problems
  • Case Study: Mexicana Wire Winding, Inc.
  • Bibliography
  • Chapter 8: Linear Programming Applications
  • 8.1. Marketing Applications
  • Media Selection
  • Marketing Research
  • 8.2. Manufacturing Applications
  • Production Mix
  • Production Scheduling
  • 8.3. Employee Scheduling Applications
  • Labor Planning
  • 8.4. Financial Applications
  • Portfolio Selection
  • Truck Loading Problem
  • 8.5. Ingredient Blending Applications
  • Diet Problems
  • Ingredient Mix and Blending Problems
  • 8.6. Other Linear Programming Applications
  • Summary
  • Self-Test
  • Problems
  • Case Study: Cable &amp
  • Moore.
  • Bibliography.

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