1. To solve complex problems in the business
world, there is a shift from using business experience, intuition, common
sense, and guesswork to using data analysis and algorithms. The use of software to “crunch” data
provides optimization the complete analysis.
1.1 In this technology age, there exists the
ability to collect large amounts of data and people are given the power and
responsibility to analyze the data and make decisions based on the
analysis. Given that most everyone has
access to software packages that “crunch” data, quantitative analysis is now
for everybody. The key to holding large
amounts of data is to find the information in the data.
1.2 This is a hands-on book that provides many
examples and descriptions of tools to use for data analysis.
1.2.1 The methods in this book draw from two
fields: statistics – the study of data
analysis; and managerial science – the study of model building. Optimization,
and decision making.
The use
of Excel and other add-ons integrate various topics in the book.
Three
themes throughout the books are:
1) data
analysis, including data description, data inference, and data relationships;
2)
decision making, including optimization, decision analysis, and sensitivity
analysis;
3)
dealing with uncertainty, including measuring and modeling.
1.2.2 The primary software used in this book is
Excel. Additional add-ons provide more
ability to look at data. Most people
have a general awareness of Excel, but the intent for students of this book is
that they become “power users” of Excel and the add-on software.
1.3 A sampling of examples used throughout the
book shows several ways in which data is analyzed: Scatter-plot, Decision Tree, Analysis of Auditing, Charts,
Multiple Regression, Time Series Plot, Cash Balance Model, Spreadsheet
Simulation, and Histogram.
1.4 Models and the modeling process are key
elements throughout the book. Three
types of models are graphical, algebraic, and spreadsheet.
1.4.1 Graphical models graphically portray
important elements of a problem and how they are related.
1.4.2 Algebraic models specify a set of
relationships in a very precise way using mathematics. The drawback to this type of modeling is that
it requires the ability to work with abstract mathematical symbols.
1.4.3 Spreadsheet models are similar to algebraic
models but use a spreadsheet and cell formulas to show data relationships. This may be more intuitive to more people
than algebraic modeling. This type of
modeling must be well designed and documented in order to be effective.
1.4.4 This book uses a seven step modeling
process:
1) Define the problem
2) Collect and summarize the 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
1.5 In conclusion, you do not have to be a
“quant jock” to use the above methods of data analysis and decision
making. The common Personal Computer is
a powerful tool to use for solving complex business problems.