Business Analytics Principles, Concepts, and Applications with SAS: What, Why, and How

Business Analytics Principles, Concepts, and Applications with SAS: What, Why, and How

by Marc J. Schniederjans, Dara G. Schniederjans, Christopher M. Starkey
Released October 2014
Publisher(s): Pearson
ISBN: 9780133989588

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Book description

Learn everything you need to know to start using business analytics and integrating it throughout your organization.

Business Analytics Principles, Concepts, and Applications with SAS brings together a complete, integrated package of knowledge for newcomers to the subject. The authors present an up-to-date view of what business analytics is, why it is so valuable, and most importantly, how it is used. They combine essential conceptual content with clear explanations of the tools, techniques, and methodologies actually used to implement modern business analytics initiatives.

They offer a proven step-wise approach to designing an analytics program, and successfully integrating it into your organization, so it effectively provides intelligence for competitive advantage in decision making.

Using step-by-step examples, the authors identify common challenges that can be addressed by business analytics, illustrate each type of analytics (descriptive, prescriptive, and predictive), and guide users in undertaking their own projects. Illustrating the real-world use of statistical, information systems, and management science methodologies, these examples help readers successfully apply the methods they are learning.

Unlike most competitive guides, this text demonstrates the use of SAS software, permitting instructors to spend less time teaching software and more time focusing on business analytics itself.

Business Analytics Principles, Concepts, and Applications with SAS will be a valuable resource for all beginning-to-intermediate level business analysts and business analytics managers; for MBA/Masters' degree students in the field; and for advanced undergraduates majoring in statistics, applied mathematics, or engineering/operations research.

Table of contents

  1. About This eBook
  2. Title Page
  3. Copyright Page
  4. Dedication Page
  5. Contents-at-a-Glance
  6. Table of Contents
  7. About the Authors
  8. Preface
    1. Conceptual Content
    2. Software
    3. Analytic Tools
  9. Part I: What Is Business Analytics?
    1. 1. What Is Business Analytics?
      1. 1.1 Terminology
      2. 1.2 Business Analytics Process
      3. 1.3 Relationship of BA Process and Organization Decision-Making Process
      4. 1.4 Organization of This Book
      5. Summary
      6. Discussion Questions
      7. References
  10. Part II: Why Is Business Analytics Important?
    1. 2. Why Is Business Analytics Important?
      1. 2.1 Introduction
      2. 2.2 Why BA Is Important: Providing Answers to Questions
      3. 2.3 Why BA Is Important: Strategy for Competitive Advantage
      4. 2.4 Other Reasons Why BA Is Important
        1. 2.4.1 Applied Reasons Why BA Is Important
        2. 2.4.2 The Importance of BA with New Sources of Data
      5. Summary
      6. Discussion Questions
      7. References
    2. 3. What Resource Considerations Are Important to Support Business Analytics?
      1. 3.1 Introduction
      2. 3.2 Business Analytics Personnel
      3. 3.3 Business Analytics Data
        1. 3.3.1 Categorizing Data
        2. 3.3.2 Data Issues
      4. 3.4 Business Analytics Technology
      5. Summary
      6. Discussion Questions
      7. References
  11. Part III: How Can Business Analytics Be Applied?
    1. 4. How Do We Align Resources to Support Business Analytics within an Organization?
      1. 4.1 Organization Structures Aligning Business Analytics
        1. 4.1.1 Organization Structures
        2. 4.1.2 Teams
      2. 4.2 Management Issues
        1. 4.2.1 Establishing an Information Policy
        2. 4.2.2 Outsourcing Business Analytics
        3. 4.2.3 Ensuring Data Quality
        4. 4.2.4 Measuring Business Analytics Contribution
        5. 4.2.5 Managing Change
      3. Summary
      4. Discussion Questions
      5. References
    2. 5. What Is Descriptive Analytics?
      1. 5.1 Introduction
      2. 5.2 Visualizing and Exploring Data
      3. 5.3 Descriptive Statistics
      4. 5.4 Sampling and Estimation
        1. 5.4.1 Sampling Methods
        2. 5.4.2 Sampling Estimation
      5. 5.5 Introduction to Probability Distributions
      6. 5.6 Marketing/Planning Case Study Example: Descriptive Analytics Step in the BA Process
        1. 5.6.1 Case Study Background
        2. 5.6.2 Descriptive Analytics Analysis
      7. Summary
      8. Discussion Questions
      9. Problems
    3. 6. What Is Predictive Analytics?
      1. 6.1 Introduction
      2. 6.2 Predictive Modeling
        1. 6.2.1 Logic-Driven Models
        2. 6.2.2 Data-Driven Models
      3. 6.3 Data Mining
        1. 6.3.1 A Simple Illustration of Data Mining
        2. 6.3.2 Data Mining Methodologies
      4. 6.4 Continuation of Marketing/Planning Case Study Example: Prescriptive Analytics Step in the BA Process
        1. 6.4.1 Case Study Background Review
        2. 6.4.2 Predictive Analytics Analysis
      5. Summary
      6. Discussion Questions
      7. Problems
      8. References
    4. 7. What Is Prescriptive Analytics?
      1. 7.1 Introduction
      2. 7.2 Prescriptive Modeling
      3. 7.3 Nonlinear Optimization
      4. 7.4 Continuation of Marketing/Planning Case Study Example: Prescriptive Step in the BA Analysis
        1. 7.4.1 Case Background Review
        2. 7.4.2 Prescriptive Analysis
      5. Summary
      6. Addendum
      7. Discussion Questions
      8. Problems
      9. References
    5. 8. A Final Business Analytics Case Problem
      1. 8.1 Introduction
      2. 8.2 Case Study: Problem Background and Data
      3. 8.3 Descriptive Analytics Analysis
      4. 8.4 Predictive Analytics Analysis
        1. 8.4.1 Developing the Forecasting Models
        2. 8.4.2 Validating the Forecasting Models
        3. 8.4.3 Resulting Warehouse Customer Demand Forecasts
      5. 8.5 Prescriptive Analytics Analysis
        1. 8.5.1 Selecting and Developing an Optimization Shipping Model
        2. 8.5.2 Determining the Optimal Shipping Schedule
        3. 8.5.3 Summary of BA Procedure for the Manufacturer
        4. 8.5.4 Demonstrating Business Performance Improvement
      6. Summary
      7. Discussion Questions
      8. Problems
  12. Part IV: Appendixes
    1. A. Statistical Tools
      1. A.1 Introduction
      2. A.2 Counting
        1. A.2.1 Permutations
        2. A.2.2 Combinations
        3. A.2.3 Repetitions
      3. A.3 Probability Concepts
        1. A.3.1 Approaches to Probability Assessment
        2. A.3.2 Rules of Addition
        3. A.3.3 Rules of Multiplication
      4. A.4 Probability Distributions
        1. A.4.1 Discrete Probability Distribution
        2. A.4.2 Continuous Probability Distributions
      5. A.5 Statistical Testing
    2. B. Linear Programming
      1. B.1 Introduction
      2. B.2 Types of Linear Programming Problems/Models
      3. B.3 Linear Programming Problem/Model Elements
        1. B.3.1 Introduction
        2. B.3.2 The Objective Function
        3. B.3.3 Constraints
        4. B.3.4 The Nonnegativity and Given Requirements
      4. B.4 Linear Programming Problem/Model Formulation Procedure
        1. B.4.1 Stepwise Procedure
        2. B.4.2 LP Problem/Model Formulation Practice: Butcher Problem
        3. B.4.3 LP Problem/Model Formulation Practice: Diet Problem
        4. B.4.4 LP Problem/Model Formulation Practice: Farming Problem
        5. B.4.5 LP Problem/Model Formulation Practice: Customer Service Problem
        6. B.4.6 LP Problem/Model Formulation Practice: Clarke Special Parts Problem
        7. B.4.7 LP Problem/Model Formulation Practice: Federal Division Problem
      5. B.5 Computer-Based Solutions for Linear Programming Using the Simplex Method
        1. B.5.1 Introduction
        2. B.5.2 Simplex Variables
        3. B.5.3 Using the LINGO Software for Linear Programming Analysis
      6. B.6 Linear Programming Complications
        1. B.6.1 Unbounded Solutions
        2. B.6.2 Infeasible Solutions
        3. B.6.3 Blending Formulations
        4. B.6.4 Multidimensional Decision Variable Formulations
      7. B.7 Necessary Assumptions for Linear Programming Models
      8. B.8 Linear Programming Practice Problems
    3. C. Duality and Sensitivity Analysis in Linear Programming
      1. C.1 Introduction
      2. C.2 What Is Duality?
        1. C.2.1 The Informational Value of Duality
        2. C.2.2 Sensitivity Analysis
      3. C.3 Duality and Sensitivity Analysis Problems
        1. C.3.1 A Primal Maximization Problem
        2. C.3.2 A Second Primal Maximization Problem
        3. C.3.3 A Primal Minimization Problem
        4. C.3.4 A Second Primal Minimization Problem
      4. C.4 Determining the Economic Value of a Resource with Duality
      5. C.5 Duality Practice Problems
    4. D. Integer Programming
      1. D.1 Introduction
        1. D.1.1 What Is Integer Programming?
        2. D.1.2 Zero-One IP Problems/Models
      2. D.2 Solving IP Problems/Models
        1. D.2.1 Introduction
        2. D.2.2 A Maximization IP Problem
        3. D.2.3 A Minimization IP Problem
      3. D.3 Solving Zero-One Programming Problems/Models
      4. D.4 Integer Programming Practice Problems
    5. E. Forecasting
      1. E.1 Introduction
      2. E.2 Types of Variation in Time Series Data
        1. E.2.1 Trend Variation
        2. E.2.2 Seasonal Variation
        3. E.2.3 Cyclical Variation
        4. E.2.4 Random Variation
        5. E.2.5 Forecasting Methods
      3. E.3 Simple Regression Model
        1. E.3.1 Model for Trend
        2. E.3.2 Computer-Based Solution
        3. E.3.3 Interpreting the Computer-Based Solution and Forecasting Statistics
      4. E.4 Multiple Regression Models
        1. E.4.1 Introduction
        2. E.4.2 Application
        3. E.4.3 Limitations on the Use of Multiple Regression Models in Forecasting Time Series Data
      5. E.5 Simple Exponential Smoothing
        1. E.5.1 Introduction
        2. E.5.2 An Example of Exponential Smoothing
      6. E.6 Smoothing Averages
        1. E.6.1 Introduction
        2. E.6.2 An Application of Moving Average Smoothing
      7. E.7 Fitting Models to Data
      8. E.8 How to Select Models and Parameters for Models
      9. E.9 Forecasting Practice Problems
    6. F. Simulation
      1. F.1 Introduction
      2. F.2 Types of Simulation
        1. F.2.1 Deterministic Simulation
        2. F.2.2 Probabilistic Simulation
      3. F.3 Simulation Practice Problems
    7. G. Decision Theory
      1. G.1 Introduction
      2. G.2 Decision Theory Model Elements
      3. G.3 Types of Decision Environments
      4. G.4 Decision Theory Formulation
      5. G.5 Decision-Making Under Certainty
        1. G.5.1 Maximax Criterion
        2. G.5.2 Maximin Criterion
      6. G.6 Decision-Making Under Risk
        1. G.6.1 Origin of Probabilities
        2. G.6.2 Expected Value Criterion
        3. G.6.3 Expected Opportunity Loss Criterion
      7. G.7 Decision-Making under Uncertainty
        1. G.7.1 Laplace Criterion
        2. G.7.2 Maximin Criterion
        3. G.7.3 Maximax Criterion
        4. G.7.4 Hurwicz Criterion
        5. G.7.5 Minimax Criterion
      8. G.8 Expected Value of Perfect Information
      9. G.9 Sequential Decisions and Decision Trees
      10. G.10 The Value of Imperfect Information: Bayes’s Theorem
      11. G.11 Decision Theory Practice Problems
  13. Index

Product information

  • Title: Business Analytics Principles, Concepts, and Applications with SAS: What, Why, and How
  • Author(s): Marc J. Schniederjans, Dara G. Schniederjans, Christopher M. Starkey
  • Release date: October 2014
  • Publisher(s): Pearson
  • ISBN: 9780133989588