Sensitivity Analysis: Definition, Uses & Importance

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  • 0:02 What Is Sensitivity Analysis?
  • 1:10 Sensitivity Analysis in Action
  • 2:12 The Demand Equation
  • 4:02 Changing the Values
  • 5:04 Lesson Summary
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Lesson Transcript
Instructor: Dr. Douglas Hawks

Douglas has two master's degrees (MPA & MBA) and is currently working on his PhD in Higher Education Administration.

Decision makers need tools to help them use historical data to predict, as best they can, the future impact of their decisions. In this lesson, we'll learn about one of these tools - the very important sensitivity analysis.

What Is Sensitivity Analysis?

There are many different ways decision makers can try to predict the future outcomes of their decisions. They can, and many do, just guess, or in kinder terms, go with their gut. Some may consider the past and look for similar decisions and what outcomes those decisions had. If they're really clever, they'll look for differences in the current conditions and the historical example they are using and consider how that may impact the outcomes. And, if they have the right information and understanding, they can use sensitivity analysis.

Sensitivity analysis is a data-driven investigation of how certain variables impact a single, dependent variable and how much changes in those variables will change the dependent variable. That's a complex idea, so let's use an example.

Imagine you own your own business that makes cases for smartphones. Each month, if you make too many cases, they sit around and may even go to waste if a new phone is introduced. But, if you don't make enough cases, you aren't selling as many as you could, so you aren't maximizing your profit. Thus, at the beginning of each month you are faced with the decision of how many cases to make.

Sensitivity Analysis in Action

The first step of a sensitivity analysis is to identify: (a) the dependent variable you want to predict and (b) as many of the independent variables that might impact the dependent variable. In your business case, the number of cases to produce is the dependent variable. It's dependent because it depends on a number of factors. Those factors are the independent variables.

So, what independent variables could impact how many cases you sell and, thus, how many cases you need to make? Your advertising budget is one; the more you advertise, to some extent, the more you sell. Let's measure that in terms of your advertising budget. What else? The price of your case has an impact. You've noticed when you drop the price you sell more than when you raise the price. Anything else? How about days, or weeks, since the latest version of a specific, popular smartphone was announced? If your customers think a new phone is coming out, they aren't going to buy that phone, and if they don't buy that phone, they aren't buying a case for it.

The Demand Equation

After considering those independent variables, you would use a data analysis package (probably Microsoft Excel, but others exist) to use historical data in a regression analysis to create your demand equation. This is an equation where you can put in each of your independent variables and then see what the outcome is. For this lesson, let's say you use Excel and you get the following demand equation:

Demand in Units = 90,000 + 2.8(Advertising) - 375(Price) - 480(Days since last version)

What that equation means is that if you enter in your advertising budget, price, and days since the last version of the major phone was announced and do the math, the answer will be your estimated demand in units. Now, this isn't perfect. Whatever data analysis software you are using is finding the best answer, not the answer that is exactly right.

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