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In models involving many input variables, sensitivity analysis is an essential ingredient of model building and quality assurance and can be useful to determine the impact of a uncertain variable for a range of purposes, [4] including: Testing the robustness of the results of a model or system in the presence of uncertainty.
In applied statistics, the Morris method for global sensitivity analysis is a so-called one-factor-at-a-time method, meaning that in each run only one input parameter is given a new value. It facilitates a global sensitivity analysis by making a number r {\displaystyle r} of local changes at different points x ( 1 → r ) {\displaystyle x(1 ...
[1] [2] In biomedical engineering, sensitivity analysis can be used to determine system dynamics in ODE-based kinetic models. Parameters corresponding to stages of differentiation can be varied to determine which parameter is most influential on cell fate.
PSPP is a free software application for analysis of sampled data, intended as a free alternative for IBM SPSS Statistics. It has a graphical user interface [2] and conventional command-line interface. It is written in C and uses GNU Scientific Library for its mathematical routines. The name has "no official acronymic expansion". [3]
Variance-based sensitivity analysis (often referred to as the Sobol’ method or Sobol’ indices, after Ilya M. Sobol’) is a form of global sensitivity analysis. [1] [2] Working within a probabilistic framework, it decomposes the variance of the output of the model or system into fractions which can be attributed to inputs or sets of inputs.
In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the outcome or response variable, or a label in machine learning parlance) and one or more error-free independent variables (often called regressors, predictors, covariates, explanatory ...
Additionally, the user must determine which of the many contexts this test is being used, such as a one-way ANOVA versus a multi-way ANOVA. In order to calculate power, the user must know four of five variables: either number of groups, number of observations, effect size, significance level (α), or power (1-β). G*Power has a built-in tool ...
One advantage of RATS, as opposed to automated forecasting software, is that it is an actual programming language, which enables the user to design custom models, and change specifications. Recent versions have added report-generation tools designed to facilitate accurate exporting of results for use in papers and other documents.