Introduction to Data Analysis and Decision Making
Quantitative Analysis helps take the
guesswork out of complex problems faced by businesses today.
Microsoft Excel contains a program
inside called Solver which can handle a variety of complex problems/algorithms.
Introduction
Today
Technology made it possible to collect huge amounts of data
Quantitative Analysis has become an
integral part in utilizing this huge amount of data in order to make decisions.
By using Quantitative Analysis,
companies can gain a competitive advantage from the information that is
discovered.
Methods
Statistics
is the study of data analysis
Management
science is the study of model building, optimization and decision making
Combining statistics and management
science, gives us the power and flexibility to solve a wide range of business
problems.
There
are three important themes mentioned in this text:
1.
Data analysis
includes data descriptions, data inference and search for relationships in data.
2.
Decision-making
includes optimization techniques with no uncertainty, decision analysis for
problems with uncertainty and structures.
3.
Dealing with
uncertainty includes measuring uncertainty and modeling uncertainty
explicitly into the analysis.
Software
MS
Excel is a very powerful, flexible and easy-to-use piece of software Tool .
MS Excel has available add-ins that can
handle complex problems or computing and we will use them for statistical
analysis.
Modeling and Models
A
model is an abstraction of a real problem Catering to key features of the
problem.
There
are three types of models:
1.
Graphical
Models: attempts to portray
graphically how different elements of a problem are related.
2.
Algebraic
Models: Using algebra they
specify a set of relationships among various factors.
3.
Spreadsheet
Models: They are
alternatives to algebraic models, where various quantities are related in a
spreadsheet with cell formulas.
The
Seven-Step Modeling Process
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