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

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. Title Page
  2. Copyright Page
  3. Dedication Page
  4. Contents at a Glance
  5. About the Authors
  6. Preface
    1. Conceptual Content
    2. Software
    3. Analytic Tools
  7. Part I: What Are Business Analytics
    1. 1. What Are 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
  8. Part II: Why Are Business Analytics Important
    1. 2. Why Are 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
      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
      4. 3.4. Business Analytics Technology
      5. Summary
      6. Discussion Questions
      7. References
  9. 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
      2. 4.2. Management Issues
      3. Summary
      4. Discussion Questions
      5. References
    2. 5. What Are Descriptive Analytics?
      1. 5.1. Introduction
      2. 5.2. Visualizing and Exploring Data
      3. 5.3. Descriptive Statistics
      4. 5.4. Sampling and Estimation
      5. 5.5. Introduction to Probability Distributions
      6. 5.6. Marketing/Planning Case Study Example: Descriptive Analytics Step in the BA Process
      7. Summary
      8. Discussion Questions
      9. Problems
    3. 6. What Are Predictive Analytics?
      1. 6.1. Introduction
      2. 6.2. Predictive Modeling
      3. 6.3. Data Mining
      4. 6.4. Continuation of Marketing/Planning Case Study Example: Prescriptive Analytics Step in the BA Process
      5. Summary
      6. Discussion Questions
      7. Problems
      8. References
    4. 7. What Are 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
      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
      5. 8.5. Prescriptive Analytics Analysis
      6. Summary
      7. Discussion Questions
      8. Problems
  10. Part IV: Appendixes
    1. A. Statistical Tools
      1. A.1. Introduction
      2. A.2. Counting
      3. A.3. Probability Concepts
      4. A.4. 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
      4. B.4. Linear Programming Problem/Model Formulation Procedure
      5. B.5. Computer-Based Solutions for Linear Programming Using the Simplex Method
      6. B.6. Linear Programming Complications
      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?
      3. C.3. Duality and Sensitivity Analysis Problems
      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
      2. D.2. Solving IP Problems/Models
      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
      3. E.3. Simple Regression Model
      4. E.4. Multiple Regression Models
      5. E.5. Simple Exponential Smoothing
      6. E.6. Smoothing Averages
      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
      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
      6. G.6. Decision-Making Under Risk
      7. G.7. Decision-Making under Uncertainty
      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