Sensitivity Analysis is used to understand how changes in input variables affect the outcome of a project or decision. By analyzing the sensitivity of the outcome to changes in input variables, decision-makers can identify the most influential factors and make informed decisions.

Linear Programming is an optimization technique used to find the optimal solution to a linear objective function subject to linear constraints. It is not typically used in Sensitivity Analysis, which focuses on assessing the impact of changes in input variables on the outcome of a project or decision.

In Scenario Analysis, multiple scenarios are created to explore different possible outcomes of a project or decision. By varying the values of input variables, decision-makers can identify the range of possible outcomes and make informed decisions based on this information.

Monte Carlo Simulation is a technique that uses random sampling to generate multiple possible outcomes of a project or decision. It generates random values for input variables based on their probability distributions, and then calculates the outcome for each set of generated values. This process is repeated multiple times to obtain a distribution of possible outcomes.

A Tornado Diagram is a graphical representation that shows the impact of changes in input variables on the outcome of a project or decision. It ranks the input variables based on their sensitivity, with the most influential variables at the top. This helps decision-makers identify the variables that have the greatest impact on the outcome and focus their attention on those variables.

Sensitivity Analysis does not eliminate the need for making assumptions about the values of input variables. It is a technique that helps decision-makers understand the impact of changes in input variables on the outcome of a project or decision, but it still requires decision-makers to make assumptions about the values of these variables.

Sensitivity Analysis can be time-consuming and computationally intensive, especially when dealing with complex projects or decisions with a large number of input variables. This is because it requires multiple iterations of calculations to assess the impact of changes in input variables on the outcome.

In Sensitivity Analysis, the base case refers to the most likely scenario or set of input values. It is the starting point for the analysis, and changes are made to input variables around this base case to assess the impact on the outcome.

Three-way sensitivity analysis is not a common type of sensitivity analysis. One-way sensitivity analysis, two-way sensitivity analysis, and global sensitivity analysis are more commonly used.

The primary objective of conducting a sensitivity analysis is to identify the most influential input variables, determine the range of possible outcomes, and assess the impact of changes in input variables on the outcome.

Regression analysis is not a common method for conducting sensitivity analysis. Scenario analysis, Monte Carlo simulation, and tornado diagram are more commonly used.

The primary limitation of using a tornado diagram for sensitivity analysis is that it does not provide a quantitative measure of sensitivity. It only shows the relative importance of input variables, but it does not provide a numerical value for the sensitivity.

Expected value scenario is not a common type of scenario analysis. Best-case scenario, worst-case scenario, and most likely scenario are more commonly used.

The primary advantage of using Monte Carlo simulation for sensitivity analysis is that it can be used for a large number of input variables, it provides a quantitative measure of sensitivity, and it is easy to interpret.

A scatter plot is not a common output of a sensitivity analysis. A list of the most influential input variables, a range of possible outcomes, and a tornado diagram are more commonly used.