Abstract
Sensitivity analysis. Users of both simulation and mathematical models are given tools by sensitivity analysis to understand how closely a model's output is related to its input and to determine the relative importance of each input. All application fields are included, including engineering, sociology, and theoretical physics. In order to clarify the structure of the entire "Sensitivity Analysis" section of the Springer Handbook, this introduction paper presents the purposes and objectives of sensitivity analysis. A helpful software programmer, the mathematical notation used in the book papers, certain categorization grids to comprehend the application limits of each method, and the fundamental concepts of sensitivity analysis are also covered. Making decisions can benefit from sensitivity analysis in a number of ways. It first serves as a thorough analysis of all the factors. The predictions might be much more accurate because it's more thorough. Second, it enables decision-makers to pinpoint areas where future improvements can be made. The weighted sum technique is a cross decision-making process; as there are numerous possibilities, there are also many more factors that must be considered before choosing the best one. A weighted or weighted collection of sums is a machine learning strategy that combines predictions from various models, with each model's contribution being weighed according to its capacity or level of expertise. Weighted the with mean evening gown voting ensemble related to this method benefits of using it are ease of use, especially when working with convergent problems, such as when disadvantages an all in solution space make it impossible to find solutions and goals a simple way to ascribe weights there is no way. relative deviation ratio (RDR), partial rank correlation coefficient (PRCC), Standardized regression coefficients (SRC), rank regression coefficient (RRC). Beef cow ingestion rate, Atmospheric concentration, Beef transport time, Biological half-life, breathing rat, Feed-to-meat transfer factor, Feed-to-milk transfer factor, Meat consumption rate. from the result it is seen that Biological half-life, breathing rat and is got the first rank whereas is the Beef transport time got is having the lowest rank. Conclusion: The value of the dataset for Sensitivity analysis technique in GRA (Gray-related analysis) method shows that it results in Biological half-life, breathing rat and top ranking.