# 7. Sensitivity Analysis

Overview

Sensitivity tables help you understand the impact that changing various input values can have on the final output - in our case additional profit per plane.

Summary

1. Creating a Sensitivity Analysis (00:04)

A sensitivity analysis reruns your model multiple times using different input values, and displays the corresponding output values in a table. It helps you better understand the relationship between the inputs and outputs of your model. You can create a sensitivity analysis for two inputs in your model using an Excel data table. If your model has more than two inputs, then you can conduct multiple sensitivity analyses.

To create a sensitivity analysis, we start by creating a table where the row labels are possible values for one of our model inputs, and the column labels are possible values for another input. These possible values are derived through business intuition and analysis of the problem. Each value in the cell will contain the output value of our model using the relevant values of the two inputs. We also link the top left cell in this table to the desired output value.

With the table set up, we create a data table using the shortcut Alt A, W, T. We specify the row input cell and column input cell using the cells from the model that correspond to the two inputs we are considering. Our table is then populated with the output values for each combination of the two inputs.

Conditional formatting can be used to make the table easier to understand. To do this, we select the cells in the data table and press Alt, H, L. Selecting an option, such as a color scale, makes the table easier to read and generate insights from.

When we conduct a sensitivity analysis, we analyze the impacts of a change in two model inputs. However, all the other inputs still have an impact on the model. If we adjust one of the other model inputs, it will affect every cell in the data table. This is important to bear in mind when conducting sensitivity analysis.

Transcript

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#### 18. Limitations of the Model

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