An Overview of Chapter One

The book combines two different fields into one subject:  statistics and management science.  These two topics are not distinguished between one another in the text.  Instead, they are viewed as an entire collection of useful methods to analyze data and make business decisions.  Furthermore, there are three important themes to the book:

1.      Data Analysis – Involves description, inference, and relationships

2.      Decision Making – Includes optimization and sensitivity analysis

3.      Dealing with Uncertainty – Entails measuring and modeling

The main tool used for analysis will be Excel.  In addition to built-in features, the accompanying CD offers add-in capabilities such as Solver Add-in, StatPro, SolverTable, and others to perform many different forms of analyses.  A software guide shows what add-ins are relevant to what chapters.  A sample group of examples is also provided to show the types of problems the will be encountered.

Different types of models are explained:

Graphical Models – These are the most intuitive and least quantitative of all types.  They show how different parts of a model are related to one another.

Algebraic Models – These outline relationships through the use of algebraic equations and inequalities.  In contrast to graphical models, they are very specific and precise in nature.

Spreadsheet Models – These are recent alternatives to algebraic models.  Cell formulas are used instead of algebraic expressions to show relationships.  They are more intuitive by nature and provide instant feedback.

Seven-Step Modeling Process:

1. Define the problem
2. Collect and summarize data
3. Formulate a model
4. Verify the model
5. Select one or more suitable decisions
6. Present the results to the organization
7. Implement the model and update it through time