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It is a bug in the caching feature. Some distributions don't have p-value corrections since those based on the empirical CDF must be derived individually. The result is correct but the p-value is inflated due to lack of correction. When the test or any underlying one is ran again it returns the cached result with no message.


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As I understand the question, I think you are making things more difficult then they need be by calculating model fits in matrix. It seems you have already determined that the form of your model is $b \sin[\frac{x}{10}]$ and they you just need to fit $b$. This is exactly what NonlinearModelFit does. So you don't need to calculate out matrix and do your own ...


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This is not an answer, but rather one route to explore the feasibility of your model. Once you have the set of equations obtained from ParametricNDSolve you can plot them using Manipulate to see how the values of k affect the shape of the concentration vs. time plots: This graphic was obtained using the following (data contains the Imported google ...


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There are two approaches to this. One is to fit a linear trend for each of your two end members. You'll then end up with (two) functions describing the dependency of velocity on depth. This may be good enough depending on your application. The other is to invert your data for accoustic impedance contrasts. One code to do this is using CSIRO's Delivery. ...


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data = Table[{x, Exp[.2 + .3 x + .1 Sin[x] + RandomReal[{-.2, .2}]]}, {x, RandomReal[5, 100]}]; nlm = NonlinearModelFit[data, Exp[a + b Sin[x] + c Cos[x]], {a, b, c}, x]; pt = nlm["ParameterTable"]; You can also use Part as follows to access the row labels, column labels and the content of pt: rowlabels = Sequence[1, 1, 2 ;;, 1]; collabels ...


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Drop[(%["ParameterTable"] // First // First)[[All, 1]], 1]


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Assuming you have typed fit = NonlinearModelFit[{{0, 1}, {1, 2}, {3, 3}}, a + b*x, {a, b}, x] Using this great answer you can type StringProperties[NonlinearModelFit] ...


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From the documentation, StepMonitor is an option for iterative numerical computation functions that gives an expression to evaluate whenever a step is taken by the numerical method used. Meanwhile, EvaluationMonitor is an option for various numerical computation and plotting functions that gives an expression to evaluate whenever functions ...


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You're only providing y-values to FindFit, with so Mathematica has no knowledge of the x-values and has to assume them to be integers starting from 1. You can change this by doing xydata = Transpose[{Datax, Datay}] to generate a list of x-y pairs for the model to fit to. The fitting can then be carried out as before: xydata = Transpose[{Datax, Datay}]; ...



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