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Introduction to Data Analysis and Decision Making
(i)Quantitative Analysis helps take the guesswork out of complex problems
faced by businesses today.
(ii)Microsoft Excel contains a program inside called Solver which can handle a variety of complex
problems/algorithms.
Section 1.1 Introduction
(i)Technology of today has allowed us to collect huge amounts
of data.
(ii)Quantitative Analysis has become an integral part in utilizing this huge amount of data in order to
make decisions.
(iii)By using Quantitative Analysis, companies can gain a competitive advantage from the information
that is discovered.
Section 1.2.1 The Methods
(i)Statistics is the study of data analysis.
(ii)Management
Science is the study of model building, optimization, and decision-making.
(iii)Combining statistics and management science,
gives us the power and flexibility to solve a wide range of business problems.
(iv)There are 3 important themes noted
in this text:
Data Analysis includes data descriptions, data inference and the search for relationships in data.
Decision-making includes optimization techniques with no uncertainty, decision analysis for problems with uncertainty,
and structures
sensitivity analysis.
Dealing with uncertainty includes measuring uncertainty and
modeling uncertainty explicitly into the analysis.
Section 1.2.2 The Software
(i)Microsoft Excel
is a powerful, flexible, and easy to use software package.
(ii)Microsoft Excel contains or has available add-ins
that can handle complex problems or computing.
Section 1.3 A Sampling of Examples
This section presented
a sampling of examples from later chapters. The purpose was to illustrate the types of problems we will learn to solve.
Section 1.4 Modeling and Models
A model is an abstraction of a real problem.
There are 3 types
of models:
a.Graphical models attempt to portray graphically, how different elements of a problem are related.
b.Algebraic models specify a set relationship in a very precise way.
c.Spreadsheet models are alternatives to
algebraic models, where various quantities are related in a spreadsheet with cell formulas.
Seven Step Modeling Process
The overall modeling process can be depicted in seven key steps:
1.Define the problem It is important to
precisely identify the underlying problem
2.Collect and Summarize Data
3.Formulate a mathematical model
that captures the essence of the problem
4.Verify the Model
5.Select one or more suitable decisions to arrive
at the optimal solution
6.Present results to the organization
7.Implement the model and update it periodically
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