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erratum
erratum

... examination of plots of the estimated autocorrelation and partial autocorrelation functions as recommended in Box-Jenkins procedure and provides a step-by-step modeling strategy which can be easily executed on a digital computer to obtain the statistically adequate model irrespective of its order. T ...
Recommended Course Topics for an Acceptable Course to Meet the
Recommended Course Topics for an Acceptable Course to Meet the

... Measures of central tendency Measures of variability or dispersion Kurtosis and skewness Univariate analysis of data Bivariate analysis of data Correlation and covariance ...
New results of the MEG experiment: search for e with sensitivity to BR
New results of the MEG experiment: search for e with sensitivity to BR

... • Paper on the arXive containing the results shown here: http://xxx.lanl.gov/abs/1107.5547 • 2011 data taking started (noise problem in DC cured) • MEG will continue to run this and next year to reach a sensitivity of few times x ...
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Sensitivity analysis

Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be apportioned to different sources of uncertainty in its inputs. A related practice is uncertainty analysis, which has a greater focus on uncertainty quantification and propagation of uncertainty. Ideally, uncertainty and sensitivity analysis should be run in tandem.The process of recalculating outcomes under alternative assumptions to determine the impact of variable under analysis Sensitivity analysis can be useful for a range of purposes, including Testing the robustness of the results of a model or system in the presence of uncertainty. Increased understanding of the relationships between input and output variables in a system or model. Uncertainty reduction: identifying model inputs that cause significant uncertainty in the output and should therefore be the focus of attention if the robustness is to be increased (perhaps by further research). Searching for errors in the model (by encountering unexpected relationships between inputs and outputs). Model simplification – fixing model inputs that have no effect on the output, or identifying and removing redundant parts of the model structure. Enhancing communication from modelers to decision makers (e.g. by making recommendations more credible, understandable, compelling or persuasive). Finding regions in the space of input factors for which the model output is either maximum or minimum or meets some optimum criterion (see optimization and Monte Carlo filtering). In case of calibrating models with large number of parameters, a primary sensitivity test can ease the calibration stage by focusing on the sensitive parameters. Not knowing the sensitivity of parameters can result in time being uselessly spent on non-sensitive ones.Taking an example from economics, in any budgeting process there are always variables that are uncertain. Future tax rates, interest rates, inflation rates, headcount, operating expenses and other variables may not be known with great precision. Sensitivity analysis answers the question, ""if these variables deviate from expectations, what will the effect be (on the business, model, system, or whatever is being analyzed), and which variables are causing the largest deviations?""
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