Sensitivity analysis

Sensitivity analysis is a quantitative method used to determine how the different values of an independent variable (or input) will impact a particular dependent variable (or output) under a given set of assumptions. It assesses how sensitive the output of a model is to changes in input values, allowing researchers and decision-makers to understand the robustness and reliability of their results or predictions.

Sensitivity analysis is a powerful tool used in business to evaluate how changes in certain input variables can affect the outcome of a model or decision. Here are several applications of sensitivity analysis in a business context: 1. **Financial Modeling**: Businesses use sensitivity analysis to understand how changes in key financial assumptions (e.g., sales volume, pricing, cost of goods sold) impact profitability, cash flow, and overall financial performance.

Experimental uncertainty analysis is a process used in scientific experimentation to quantify and evaluate the uncertainties associated with measurement results. It involves identifying and estimating the various sources of uncertainty that can affect the precision and accuracy of experimental data. Here are some key components and steps involved in experimental uncertainty analysis: 1. **Identification of Uncertainties**: Researchers identify potential sources of uncertainty in their experiments. This can include instrumental errors, environmental conditions, systematic errors, and human factors.

Extreme Bounds Analysis (EBA) is a statistical technique used in econometrics and social sciences to assess the robustness of the estimated relationships between variables in a regression model. Developed by economist Edward Leamer in the 1980s, EBA helps researchers evaluate how sensitive their regression results are to the inclusion or exclusion of certain variables.

Fourier Amplitude Sensitivity Testing (FAST) is a global sensitivity analysis method used to assess how variations in model input parameters affect the output of a mathematical model. This approach is particularly useful in complex models with many inputs, as it allows researchers to identify which parameters have the most significant impact on the output. ### Key Concepts: 1. **Fourier Series**: FAST employs Fourier series to represent the behavior of the model output as a function of the input parameters.

A hyperparameter is a configuration or parameter that is set before the training of a machine learning model begins and is not learned from the data during training. Essentially, these parameters influence the training process itself and can affect the model's performance. Hyperparameters differ from model parameters, which are the values adjusted by the learning algorithm during the training process, such as weights in a neural network.

Sensitivity analysis in the context of an EnergyPlus model refers to the process of evaluating how the output of the model responds to changes in its input parameters. EnergyPlus is a widely used building energy simulation software designed to model heating, cooling, lighting, ventilating, and other energy flows within buildings. ### Key Components of Sensitivity Analysis: 1. **Purpose**: - To identify which input variables have the most significant impact on the simulation results.

Sensitivity auditing refers to the process of assessing and evaluating the sensitivity of data within an organization, particularly focusing on how personal, confidential, or sensitive information is handled, stored, and shared. This practice is crucial for organizations that collect, process, or store data that could be classified as sensitive, such as personally identifiable information (PII), financial records, health information, or other proprietary data.

A Tornado diagram is a type of bar chart that is used in sensitivity analysis to visually display the impact of different variables on a specific outcome or metric. It is particularly useful in decision-making processes, project management, risk assessment, and financial forecasting. The name "Tornado diagram" comes from its shape, which resembles a tornado or a funnel. ### Key Features of a Tornado Diagram: 1. **Horizontal Bars**: The diagram displays horizontal bars that represent different variables or factors.