Free Ebook cover Complete Excel Course for Beginners

Complete Excel Course for Beginners

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sensitivity analysis

Capítulo 33

Estimated reading time: 1 minutes

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Sensitivity analysis is a very important technique in Excel that allows you to evaluate the impact of different variables on a model. It is useful for understanding how changes to certain parameters affect the final result of a worksheet.

There are different types of sensitivity analysis, but one of the most common is what-if analysis. In this case, the user sets a series of values ​​for one or more variables and evaluates how these changes affect the final result of the worksheet.

To perform what-if analysis in Excel, you need to follow a few simple steps:

  1. Define the variables to be analyzed;
  2. Define the values ​​for each of the variables;
  3. Configure worksheet formulas so that they use the defined variables;
  4. Evaluate the results for each of the defined values.

One of the most useful tools for performing sensitivity analysis in Excel is the data table. This table allows the user to define different values ​​for one or more variables and visualize the corresponding results.

To create a data table in Excel, you need to follow these steps:

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  1. Define the variables to be analyzed;
  2. Define the values ​​for each of the variables;
  3. Create a table with the values ​​of the variables and the corresponding results;
  4. Use the Data Table function to calculate the results for each of the defined values.

Sensitivity analysis is a very useful technique to evaluate different scenarios and make more informed decisions. It can be applied in different areas, such as finance, marketing, engineering, among others.

Now answer the exercise about the content:

_What is one of the most useful tools for performing sensitivity analysis in Excel?

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Data tables are a specific tool in Excel designed for performing what-if analysis, allowing users to evaluate the impact of varying values on one or more variables. They help visualize results efficiently and are fundamental for sensitivity analysis, as highlighted in the text.

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Monte Carlo Simulation

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