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some desired objective under constrained conditions. We will learn more about this
prescriptive analysis procedure later in this chapter.
Also of great interest will be the Scenarios and Goal Seek tools. The Scenario
tool is used to implement what we have referred to as what-if analysis. Simply put,
with Scenarios we have an efficient tool for automating the variation of inputs for a
problem formulation, and subsequently recording the resulting output. Its function
is to organize and structure. Without the formal structure of Scenarios it is very easy
to lose important information in your analysis.
Goal Seek is also a member of the What-if sub-group in the Data Tools group.
In situations where we know the outcome we desire from a problem, Goal Seek ,as
the name implies, is an approach that seeks to find the input that leads to our desired
outcome. For example, if we are calculating the constant and periodic payments
for a mortgage, we may ask the question—what interest rate will lead to a monthly
payment of $1,000 for a loan with a term of 240 monthly periods and principal value
of $100,000?
Before we become acquainted with these new tools let us take stock of where we
have been thus far on our analytical journey. We began with a careful classification
of data, from categorical to ratio, and we discussed the implications of the data type
on the forms of analysis that could be applied. Our classification focused on quan-
titative and qualitative ( non - quantitative ) techniques for presenting, summarizing,
and analyzing data. Through Chap. 6 we assumed that the data under consideration
were available from the collection of primary data 1 or available from a secondary
source. Additionally, our analysis was descriptive in nature, generally attempting to
describe some characteristic of a population, by analyzing a sample from that popu-
lation; for example, comparing the mean of a sample to the mean of the population,
and then determining if the sample mean could have come from the population, at
some level of statistical significance.
In Chaps. 7 and 8, Modeling and Simulation, we created models in which we gen-
erated data that we could then analyze. In these chapters, we began our modeling
effort by using the descriptive procedures discussed above to define our model, and
then we generated data from the model to use for prescriptive purposes. For exam-
ple, in Chap. 8 we used our understanding of the operating behavior of Autohaus
to create a Monte Carlo simulation. The data generated by the simulation allowed
us to prescribe to Inez the possible design selections for the Autohaus business
concept.
The models that we create with Solver have an even stronger prescriptive char-
acter than those encountered thus far. In using Solver, our goal is to determine the
values of decision variables that will minimize or maximize some objective, while
adhering to the technological constraints of the problem. Thus, the solution will
prescribe very specific action about decision variables. As you can see from this
1 Primary data is collected by someone in the role of collecting data for a specific purpose and
comes from sources that are generally not available as a result of other studies. For example, a
survey study performed by a company to determine their customers’ satisfaction with their service
is primary data, while data on similar industry-wide service that can be purchased from a consulting
firm is secondary data.
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