Excel formula for latin hypercube3/10/2024 Chi Square – Uses the chi-square statistic and distribution to rank the distributions.The default Goodness-of-Fit test is Anderson-Darling. The Goodness of Fit test is used to select the best Metalog form for each data variable among the candidate distributions containing a different number of terms, from 2 to the value entered for Max Terms. ![]() If no lower or upper bound is entered, Analytic Solver will fit a semi- (with one bound present) or unbounded (with no bounds present) Metalog function.ĭistribution Fitting section of the Generate Data dialogĬlick the down arrow to select the desired Goodness-of-Fit test used by Analytic Solver. By default the lower and upper bounds are set to the variable’s minimum and maximum values, respectively. If Auto is selected, Analytic Solver will attempt to fit all possible Metalog distributions, up to the entered value for Max Terms, and select and utilize the best Metalog distribution according to the goodness-of-fit test selected in the Metalog Selection Test menu.Ĭlick the down arrow on the right of Fitting Options to enter either the maximum number of terms (if Auto is selected) or the exact number of terms (if Fixed is selected) for each variable as well as a lower and/or upper bound.(Only 1 distribution will be fit.) If Fixed is selected, Metalog Selection Test is disabled. If Fixed is selected, Analytic Solver will attempt to fit and use the Metalog distribution with the specified number of terms entered into the # Terms column.These options pertain to the Distribution Terms, Correlation Fitting and available output. The following options appear on the Parameters tab of the Generate Synthetic Data dialog. Generate Synthetic Data dialog, Parameters tab Synthetic data will be generated for the variables appearing in this field. Select a variable(s) in the Variables field, then click > to move the variable(s) to the Selected Variables field. If the first row in the dataset contains headings, select First Row Contains Headers. VariablesĪll variables in the data source data range are listed in this field. These options pertain to the data source and the variables included. The following options appear on the Data tab of the Generate Synthetic Data dialog. Decomposition of variance įrom a black box perspective, any model may be viewed as a function Y= f( X), where X is a vector of d uncertain model inputs įor the estimation of the S i and the S Ti respectively.įor the estimation of the S i and the S Ti for all input variables, N( d+2) model runs are required.The following options appear on the Generate Data tabs, Data and Parameters. it is a global method), they can deal with nonlinear responses, and they can measure the effect of interactions in non- additive systems. Variance-based measures of sensitivity are attractive because they measure sensitivity across the whole input space (i.e. These percentages are directly interpreted as measures of sensitivity. ![]() For example, given a model with two inputs and one output, one might find that 70% of the output variance is caused by the variance in the first input, 20% by the variance in the second, and 10% due to interactions between the two. 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. Sobol’) is a form of global sensitivity analysis. Variance-based sensitivity analysis (often referred to as the Sobol’ method or Sobol’ indices, after Ilya M.
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