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Spreadsheet Modeling Decision Analysis A Practical Introduction to Business Analytics

A practical introduction to solving complex business problems and making better decisions using descriptive, predictive, and prescriptive analytics techniques within Microsoft Excel.

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What it’s about

This textbook provides students and professionals with a comprehensive introduction to the field of business analytics, focusing on practical application through Microsoft Excel and the Analytic Solver Platform add-in. It covers the three pillars of analytics: descriptive, predictive, and prescriptive modeling. Readers will learn to formulate and solve a wide array of real-world business problems, including resource allocation, scheduling, financial planning, risk analysis, and forecasting. Through step-by-step instructions, numerous annotated examples, and case studies, the book demystifies quantitative methods and empowers readers to build powerful spreadsheet models for optimization, simulation, data mining, and decision analysis, ultimately leading to better, data-driven decision-making.

The through-line

Who it’s for
A student or business professional who needs to analyze complex problems and make better, more justifiable decisions. They want to master practical tools to solve real-world business challenges, enhancing their analytical skills and career prospects.
The problem
Facing complex business problems involving limited resources, uncertainty, and competing objectives, and needing a practical way to analyze alternatives and find optimal solutions. Feeling overwhelmed by the complexity of business decisions, uncertain about how to justify choices with data, and potentially intimidated by quantitative methods.
The plan
  1. Learn the fundamentals of modeling and the problem-solving process.
  2. Master prescriptive analytics by building and solving optimization models (linear, integer, nonlinear) in a spreadsheet.
  3. Understand and apply predictive analytics through regression, data mining, and time-series forecasting.
  4. Use descriptive analytics, primarily simulation, to model and analyze risk and uncertainty.
  5. Apply decision analysis frameworks to make choices under uncertainty and with multiple criteria.
The payoff
Confidently transform complex business problems into structured spreadsheet models. · Generate data-driven insights to make better, more profitable, and more justifiable decisions. · Become a valued analytical thinker capable of managing risk and optimizing resources within an organization.

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Additional reading

  • Linear Programming and Extensions · G. Dantzig

    This is the foundational text on linear programming by its creator, providing the deep theoretical underpinnings for the optimization techniques central to the book.

  • Making Hard Decisions: An Introduction to Decision Analysis · R. Clemen

    Provides a comprehensive and practical treatment of the topics covered in Chapter 14, including decision trees, utility theory, and sensitivity analysis in decision-making.

  • Data Mining for Business Intelligence · G. Shmueli, N. Patel, and P. Bruce

    Offers a broader and deeper exploration of the data mining concepts introduced in Chapter 10, covering a wider range of techniques and business applications.

  • Simulation Modeling and Analysis · A. Law and W. Kelton

    A classic text that provides a thorough grounding in the theory and practice of simulation, expanding significantly on the introduction to spreadsheet simulation in Chapter 12.

  • Multiple Criteria Optimization: Theory, Computation, and Application · R. Steuer

    A detailed resource for readers interested in the advanced theory and methods behind the multiple objective optimization topics introduced in Chapter 7.

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