Introduction
Sensitivity analysis (SA) is a statistical method or technique to investigate how the sensitivity output of a model or system is relevant to change input variables. Decision-makers need to be able to understand them in order to make informed choices, and the modelers should assess their models based on this understanding.
Importance of Sensitivity Analysis
Assessing Risk: Identifying Sentisitive Variables allows for prioritizing risk mitigation efforts.
Decision Making: Sensitive analysis enables the evaluation of different decisions consequences.
Model Validation: It can be utilized to evaluate the strength of models and pinpoint potential improvements.
Resource Allocation: It Interprets how changes in resource affect the output? This helps organizations to optimize resources.
Types of Sensitivity Analysis
One-Way Sensitivity Analysis:
Analyzes the effect of altering an individual variable, holding all others constant.
Good for seeing the individual effect of each variable.
Two-Way Sensitivity Analysis:
Assesses the combined effect of changes in two variables.
Aids in comprehending variable relationships
Tornado Diagram:
A graphical depiction of the influence of various features on the output.
Each bar is measure of how big the impact is.
Monte Carlo Simulation:
It is a probabilistic approach that simulates hundreds or thousands of scenarios with random variation in the input variables.
Gives you a distribution of possible results
How To Conduct Sensitivity Analysis — Steps
Learn your Key Variables: Understanding which input variables are most likely to change the output
Define the Limits of Variation: Placeholder for the full text.
Step 2: Select the Analysis Technique —Select a technique based on how complex the model is and how detailed you want it to be.
Perform the Analysis: Use the selected method to determine how variation in inputs affects output.
RESULT INTERPRETATION: Analyze the results to know which variables affect more, how much, and what is their contribution in causing variance.
