Microsoft Office Tutorials and References
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is introduced. Excel also resides in Microsoft Office, a suite of similarly popu-
lar tools that permit interoperability. Finally, there are tremendous advantages to
“one-stop shopping” in the selection of a modeling tool, that is, a tool with many
capabilities. There is so much power and capability built into Excel, that unless you
have received very recent training in its latest capabilities, you might be unaware
of the variety of modeling that is possible with Excel. Herein lies the first layer
of important questions for decision makers who are considering a decision tool
1. What forms of analysis are possible with Excel?
2. If my modeling effort requires multiple forms of analysis, can Excel handle the
various techniques required?
3. If I commit to using Excel, will it be capable of handling new forms of analysis
and a potential increase in the scale and complexity of my models?
The general answer to these questions is—just about any analytical technique
that you can conceive that fits in the row-column structure of spreadsheets can be
modeled with Excel. Note that this is a very broad and bold statement. Obviously,
if you are modeling phenomena related to high energy physics or theoretical math-
ematics, you are very likely to choose other modeling tools. Yet, for the individual
looking to model business problems, Excel is a must, and that is why this topic will
be of value to you. More specifically, Table 1.1 provides a partial list of the types of
analysis this topic will address.
When we first conceptualize and plan to solve a decision problem, one of the
first considerations we face is which modeling approach to use. There are business
problems that are sufficiently unique and complex that they will require a much
more targeted and specialized modeling approach than Excel. Yet, most of us are
involved with business problems that span a variety of problem areas—e.g. market-
ing issues that require qualitative database analysis, finance problems that require
simulation of financial statements, and risk analysis that requires the determination
of risk profiles. Spreadsheets permit us to unify these analyses on a single modeling
platform. This makes our modeling effort: (1) durable —a robust structure that can
anticipate varied use, (2) flexible —capable of adaptation as the problem changes
and evolves, and (3) shareable —models that can be shared by a variety of individu-
als at many levels of the organization, all of whom are collaborating in the solution
Table 1.1 Types of analysis this topic will undertake
Quantitative Data Presentation—Graphs and Charts
Quantitative Data Analysis—Summary Statistics and Data Exploration and Manipulation
Qualitative Data Presentation—Pivot Tables and Pivot Charts
Qualitative Data Analysis—Data Tables, Data Queries, and Data Filters
Advanced Statistical Analysis—Hypothesis testing, Correlation Analysis, and Regression Model
Sensitivity Analysis—One-way, Two-way, Data Tables, Graphical Presentation
Optimization Models and Goal Seeking—Solver for Constrained Optimization, Scenarios
Models with Uncertainty—Monte Carlo Simulation
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