Spreadsheet Modeling and Decision Analysis 9th edition

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Cliff T. Ragsdale
Publisher: Cengage Learning

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  • Ragsdale Spreadsheet Modeling and Decision Analysis 9e - Homework and Quizzes from 8e MindTap

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  • Chapter 1: Introduction to Modeling and Decision Analysis
    • 1.0: Introduction
    • 1.1: The Modeling Approach to Decision Making
    • 1.2: Characteristics and Benefits of Modeling
    • 1.3: Mathematical Models
    • 1.4: Categories of Mathematical Models
    • 1.5: Business Analytics and the Problem-Solving Process
    • 1.6: Anchoring and Framing Effects
    • 1.7: Good Decisions vs. Good Outcomes
    • 1.8: Summary
    • 1.9: References
    • 1: Questions and Problems (13)
    • 1: Test Bank

  • Chapter 2: Introduction to Optimization and Linear Programming
    • 2.0: Introduction
    • 2.1: Applications of Mathematical Optimization
    • 2.2: Characteristics of Optimization Problems
    • 2.3: Expressing Optimization Problems Mathematically
    • 2.4: Mathematical Programming Techniques
    • 2.5: An Example LP Problem
    • 2.6: Formulating LP Models
    • 2.7: Summary of the LP Model for the Example Problem
    • 2.8: The General Form of an LP Model
    • 2.9: Solving LP Problems: An Intuitive Approach
    • 2.10: Solving LP Problems: A Graphical Approach
    • 2.11: Special Conditions in LP Models
    • 2.12: Summary
    • 2.13: References
    • 2: Questions and Problems (13)
    • 2: Test Bank

  • Chapter 3: Modeling and Solving LP Problems in a Spreadsheet
    • 3.0: Introduction
    • 3.1: Spreadsheet Solvers
    • 3.2: Solving LP Problems in a Spreadsheet
    • 3.3: The Steps in Implementing an LP Model in a Spreadsheet
    • 3.4: A Spreadsheet Model for the Blue Ridge Hot Tubs Problem
    • 3.5: How Solver Views the Model
    • 3.6: Using Analytic Solver
    • 3.7: Using Excel's Built-in Solver
    • 3.8: Goals and Guidelines for Spreadsheet Design
    • 3.9: Make vs. Buy Decisions
    • 3.10: An Investment Problem
    • 3.11: A Transportation Problem
    • 3.12: A Blending Problem
    • 3.13: A Production and Inventory Planning Problem
    • 3.14: A Multiperiod Cash Flow Problem
    • 3.15: Data Envelopment Analysis
    • 3.16: Summary
    • 3.17: References
    • 3: Questions and Problems (25)
    • 3: Test Bank

  • Chapter 4: Sensitivity Analysis and the Simplex Method
    • 4.0: Introduction
    • 4.1: The Purpose of Sensitivity Analysis
    • 4.2: Approaches to Sensitivity Analysis
    • 4.3: An Example Problem
    • 4.4: The Answer Report
    • 4.5: The Sensitivity Report
    • 4.6: Ad Hoc Sensitivity analysis
    • 4.7: Robust Optimization
    • 4.8: The Simplex Method
    • 4.9: Summary
    • 4.10: References
    • 4: Questions and Problems (16)
    • 4: Test Bank

  • Chapter 5: Network Modeling
    • 5.0: Introduction
    • 5.1: The Transshipment Problem
    • 5.2: The Shortest Path Problem
    • 5.3: The Equipment Replacement Problem
    • 5.4: Transportation/Assignment Problems
    • 5.5: Generalized Network Flow Problems
    • 5.6: Maximal Flow Problems
    • 5.7: Special Modeling Considerations
    • 5.8: Minimal Spanning Tree Problems
    • 5.9: Summary
    • 5.10: References
    • 5: Questions and Problems (17)
    • 5: Test Bank

  • Chapter 6: Integer Linear Programming
    • 6.0: Introduction
    • 6.1: Integrality Conditions
    • 6.2: Relaxation
    • 6.3: Solving the Relaxed Problem
    • 6.4: Bounds
    • 6.5 Rounding
    • 6.6: Stopping Rules
    • 6.7: Solving ILP Problems Using Solver
    • 6.8: Other ILP Problems
    • 6.9: An Employee Scheduling Problem
    • 6.10: Binary Variables
    • 6.11: A Capital Budgeting Problem
    • 6.12: Binary Variables and Logical Conditions
    • 6.13: The Line Balancing Problem
    • 6.14: The Fixed-Charge Problem
    • 6.15: Minimum Order/Purchase Size
    • 6.16: Quantity Discounts
    • 6.17: A Contract Award Problem
    • 6.18: The Branch-and-Bound Algorithm (Optional)
    • 6.19: Summary
    • 6.20: References
    • 6: Questions and Problems (22)
    • 6: Test Bank

  • Chapter 7: Goal Programming and Multiple Objective Optimization
    • 7.0: Introduction
    • 7.1: Goal Programming
    • 7.2: A Goal Programming Example
    • 7.3: Comments about Goal Programming
    • 7.4: Multiple Objective Optimization
    • 7.5: An MOLP Example
    • 7.6: Comments on MOLP
    • 7.7: Summary
    • 7.8: References
    • 7: Questions and Problems (15)
    • 7: Test Bank

  • Chapter 8: Nonlinear Programming and Evolutionary Optimization
    • 8.0: Introduction
    • 8.1: The Nature of NLP Problems
    • 8.2: Solution Strategies for NLP Problems
    • 8.3: Local vs. Global Optimal Solutions
    • 8.4: Economic Order Quantity Models
    • 8.5: Location Problems
    • 8.6: Nonlinear Network Flow Problem
    • 8.7: Project Selection Problems
    • 8.8: Optimizing Existing Financial Spreadsheet Models
    • 8.9: The Portfolio Selection Problems
    • 8.10: Sensitivity Analysis
    • 8.11: Solver Options for Solving NLP's
    • 8.12: Evolutionary Algorithms
    • 8.13: Forming Fair Teams
    • 8.14: The Traveling Salesperson Problem
    • 8.15: Summary
    • 8.16: References
    • 8: Questions and Problems (21)
    • 8: Test Bank

  • Chapter 9: Regression Analysis
    • 9.0: Introduction
    • 9.1: An Example
    • 9.2: Regression Models
    • 9.3: Simple Linear Regression Analysis
    • 9.4: Defining ""Best Fit""
    • 9.5: Solving the Problem Using Solver
    • 9.6: Solving the Problem Using the Regression Tool
    • 9.7: Evaluating the Fit
    • 9.8: The R2 Statistic
    • 9.9: Making Predictions
    • 9.10: Statistical Tests for Population Parameters
    • 9.11: Introduction to Multiple Regression
    • 9.12: A Multiple Regression Example
    • 9.13: Selecting the Model
    • 9.14: Making Predictions
    • 9.15: Other Model Selection Issues
    • 9.16: Binary Independent Variables
    • 9.17: Statistical Tests for the Population Parameters
    • 9.18: Polynomial Regression
    • 9.19: Summary
    • 9.20: References
    • 9: Questions and Problems (13)
    • 9: Test Bank

  • Chapter 10: Data Mining
    • 10.0: Introduction
    • 10.1: Data Mining Overview
    • 10.2: Classification
    • 10.3: Classification Data Partitioning
    • 10.4: Discriminant Analysis
    • 10.5: Logistic Regression
    • 10.6: k-Nearest Neighbor
    • 10.7: Classification Trees
    • 10.8: Neural Networks
    • 10.9: Naïve Bayes
    • 10.10: Comments on Classification
    • 10.11: Prediction
    • 10.12: Association Rules (Affinity Analysis)
    • 10.13: Cluster Analysis
    • 10.14: Time Series
    • 10.15: Summary
    • 10.16: References
    • 10: Questions and Problems (5)
    • 10: Test Bank

  • Chapter 11: Time Series Forecasting
    • 11.0: Introduction
    • 11.1: Time Series Methods
    • 11.2: Measuring Accuracy
    • 11.3: Stationary Models
    • 11.4: Moving Averages
    • 11.5: Weighted Moving Averages
    • 11.6: Exponential Smoothing
    • 11.7: Seasonality
    • 11.8: Stationary Data with Additive Seasonal Effects
    • 11.9: Stationary Data with Multiplicative Seasonal Effects
    • 11.10: Trend Models
    • 11.11: Double Moving Average
    • 11.12: Double Exponential Smoothing (Holt's Method)
    • 11.13: Holt-Winter's Method for Additive Seasonal Effects
    • 11.14: Holt-Winter's Method for Multiplicative Seasonal Effects
    • 11.15: Modeling Time Series Trends Using Regression
    • 11.16: Linear Trend Model
    • 11.17: Quadratic Trend Model
    • 11.18: Modeling Seasonality with Regression Models
    • 11.19: Adjusting Trend Predictions with Seasonal Indices
    • 11.20: Seasonal Regression Models
    • 11.21: Combining Forecasts
    • 11.22: Summary
    • 11.23: References
    • 11: Questions and Problems (32)
    • 11: Test Bank

  • Chapter 12: Introduction to Simulation Using Analytic Solver
    • 12.0: Introduction
    • 12.1: Introduction
    • 12.2: Why Analyze Risk?
    • 12.3: Methods of Risk Analysis
    • 12.4: A Corporate health Insurance Example
    • 12.5: Spreadsheet Simulation Using Analytic Solver
    • 12.6: Random Number Generators
    • 12.7: Preparing the Model for Simulation
    • 12.8: Running the Simulation
    • 12.9: Data Analysis
    • 12.10: The Uncertainty of Sampling
    • 12.11: Interactive Simulation
    • 12.12: the Benefits of Simulation
    • 12.13: Additional Uses of Simulation
    • 12.14: A Reservation Management Example
    • 12.15: An Inventory Control Example
    • 12.16: A Project Selection Example
    • 12.17: A Portfolio Optimization Example
    • 12.18: Summary
    • 12.19: References
    • 12: Questions and Problems (19)
    • 12: Test Bank

  • Chapter 13: Queuing Theory
    • 13.0: Introduction
    • 13.1: The Purpose of Queuing Models
    • 13.2: Queuing System Configurations
    • 13.3: Characteristics of Queuing Systems
    • 13.4: Kendall Notation
    • 13.5: Queuing Models
    • 13.6: The M/M/s Model
    • 13.7: The M/M/s Model with Finite Queue Length
    • 13.8: The M/M/s Model with Finite Population
    • 13.9: The M/G/1 Model
    • 13.10: The M/D/1 Model
    • 13.11: Simulating Queues and the Steady-State Assumption
    • 13.12: Summary
    • 13.13: References
    • 13: Questions and Problems (13)
    • 13: Test Bank

  • Chapter 14: Decision Analysis
    • 14.0: Introduction
    • 14.1: Good Decisions vs. Good Outcomes
    • 14.2: Characteristics of Decision Problems
    • 14.3: An Example
    • 14.4: The Payoff Matrix
    • 14.5: Decision Rules
    • 14.6: Nonprobabilistic Methods
    • 14.7: Probabilistic Methods
    • 14.8: The Expected Value of Perfect Information
    • 14.9: Decision Trees
    • 14.10: Creating Decision Trees with Analytic Solver
    • 14.11: Multistage Decision Problems
    • 14.12: Sensitivity Analysis
    • 14.13: Using Sample Information in Decision Making
    • 14.14: Computing Conditional Probabilities
    • 14.15: Utility Theory
    • 14.16: Multicriteria Decision Making
    • 14.17: The Multicriteria Scoring Model
    • 14.18: The Analytic Hierarchy Process
    • 14.19: Summary
    • 14.20: References
    • 14: Questions and Problems (16)
    • 14: Test Bank

  • Chapter 15: Project Management
    • 15.0: Introduction
    • 15.1: An Example
    • 15.2: Creating the Project Network
    • 15.3: CPM: An Overview
    • 15.4: The Forward Pass
    • 15.5: The Backward Pass
    • 15.6: Determining the Critical Path
    • 15.7: Project Management Using Spreadsheets
    • 15.8: Gantt Charts
    • 15.9: Project Crashing
    • 15.10: Pert: An Overview
    • 15.11: Simulating Project Networks
    • 15.12: Microsoft Project
    • 15.13: Summary
    • 15.14: References
    • 15: Questions and Problems (13)
    • 15: Test Bank

  • Chapter A: Appendix
    • A: Appendix A: Basics of Excel (11)


Clearly present important spreadsheet and business analytics skills with Spreadsheet Modeling and Decision Analysis: A Pratical Introduction to Business Analytics, 9e, written by spreadsheet instructional innovator and business analytics leader Cliff Ragsdale. Your students master today's most widely used business analytics techniques as they become proficient with the latest Excel® capabilities and functions in Microsoft® Office 365 or Office 2019. A new full-color presentation and succinct instructions highlight commonly used business analytics techniques and demonstrate how to implement these tools using the latest version of Excel®. Students develop both algebraic and spreadsheet modeling skills. This edition features Frontline Systems' Analytic Solver® and Data Mining add-ins for performing optimization, simulation and decision analysis, data mining and predictive analytics in Excel®. WebAssign digital resources provide customizable teaching tools and author-created videos.


Instructor Product Features

  • A Course Pack with ready-to-use assignments was built by subject matter experts specifically for this textbook. It is designed to save you time and can be easily customized to meet your teaching goals.
  • Instructor Resources include Lecture PowerPoint slides and a full Instructor Solutions Manual.
  • Test Bank questions (TB) - Coming Summer 2022! can either be used for additional practice or as a way to conduct formative or summative assessments of student performance.

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  • Excel Tutorial questions are available to help students practice Excel skills.
  • All datasets are available for download for further analysis.


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Chapter A: Appendix
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Chapter 1: Introduction to Modeling and Decision Analysis
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Chapter 2: Introduction to Optimization and Linear Programming
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Chapter 3: Modeling and Solving LP Problems in a Spreadsheet
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Chapter 4: Sensitivity Analysis and the Simplex Method
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Chapter 5: Network Modeling
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Chapter 6: Integer Linear Programming
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Chapter 7: Goal Programming and Multiple Objective Optimization
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Chapter 8: Nonlinear Programming and Evolutionary Optimization
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Chapter 9: Regression Analysis
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Chapter 10: Data Mining
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Chapter 11: Time Series Forecasting
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Chapter 12: Introduction to Simulation Using Analytic Solver
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Chapter 13: Queuing Theory
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Chapter 14: Decision Analysis
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Chapter 15: Project Management
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