Chapter 1 Summary

 

Introduction:

 

Technology has made it possible for businesses to collect huge amounts of data.  Modern day businesses employ quantitative analysis to analyze data and make decisions.

 

The Methods:

 

The three themes in the book are:

1.      Data analysis (Description, Inference, Relationships)

2.      Decision making (Optimization, Decision analysis with Uncertainty, Sensitivity Analysis)

3.      Dealing with uncertainty (Measuring, Modeling)

 

The Software

 

MS Excel, along with some add-ins is the most commonly used spreadsheet software today.

Some special Excel functions like SUMPRODUCT, VLOOKUP, IF, NPV and COUNTIF will be used.

 

Some add-ins that will be used include:

  1. Slover Add-in
  2. StatPro Add-in
  3. RandFns Add-in
  4. SolverTable Add-in
  5. Decision Tools Suite
  6. @Risk
  7. Precision Tree
  8. TopRank
  9. BestFit
  10. RiskView

 

Examples:

 

Some typical examples are provided from various chapters to give the reader an insight into the subject matter.

 

 

Modeling and Models:

 

Models are an abstraction of a real problem. They are used to analyze complex problems with relative ease. Omitting unimportant details in the Modeling process does this.

 

Three types of models are:

  1. Graphical Models: Shows how the different elements of a problem are connected. Non-quantitative and most intuitive. They do not contain enough information to help solve a problem.
  2. Algebraic Models: These are mathematical models that can give highly accurate results. An ability to work with abstract mathematical symbols is required. Provide no immediate feedback on errors.
  3. Spreadsheet Models: An alternative to Algebraic models. Instant feedback on errors is possible. Different data are input in cells of the spreadsheet, and with the help of formulae that relate to the various quantities, the output is obtained. In order for them to be useful, they need to be designed and documented carefully.

 

The 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 decision
  6. Present results to the organization
  7. Implement the model and update it through time