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3

Your set up: c = 116*10^(-6); U = 16000; data = {{0, 0}, {0.25*10^(-6), 132000}, {0.5*10^(-6), 330000}, {1*10^(-6), 462000}, {2*10^(-6), 600000}, {3*10^(-6), 462000}, {4*10^(-6), 330000}, {5*10^(-6), 66000}, {6*10^(-6), -198000}, {7*10^(-6), -264000}, {8*10^(-6), \ -198000}, {9*10^(-6), -132000}}; Use analytic not numerical form DSolve: ...


4

I have changed the name of some of the parameters (B->d,C->v,[Theta]->u). Using mydata (and changing the model to $\rho(T)=\rho(0)+(T/\theta)^n\int_0^{\theta/T}x^5/(e^x-1)(1-e^{-x})dx$ where $n=5$. f[a_, b_, c_] := c b^5/a^5 Integrate[x^5/(Exp[x] - 1) (1 - Exp[-x]), {x, 0, a/b}] nlm = NonlinearModelFit[mydata, d + f[u, t, v], {{u, 100}, {v, 0.2}, {d, ...


5

As of version 10.0 there is a built in implementation of Random Forests which is accessible through the Classify function. trainingset = {1 -> "A", 2 -> "A", 3.5 -> "B", 4 -> "B"}; classifier = Classify[trainingset, Method->"RandomForest"];


4

I post this for illustrative purposes. You can access values. I suggest looking at the properties of your model, e.g. if your model is nlm then nlm["Properties"]. Some data and model: wd = WeatherData["Brisbane", "Temperature", {{2004, 1, 1}, {2013, 12, 31}, "Day"}]; vl = QuantityMagnitude /@ wd["Values"]; bnl = ...


6

Fit seems to work fine, you just need to include your 1.1 step size. data = Import["data.dat", "List"]; d = Thread[{1.1 Range@Length@d, d}]; Fit[d, {1, x}, x] 8.24575*10^8 - 0.0402358 x So now we have the -0.040 that you wanted. It looks ok by eye: Show[ListPlot[d, Joined -> True], Plot[8.245747660409383`*^8 - 0.04023578596262912` x, {x, ...


1

Citing this Demonstration by Darren Glosemeyer (Wolfram Research): Single prediction bands incorporate both the variation in parameter estimates and the overall variation in response values, while the mean confidence bands incorporate only the variation in parameter estimates. As a result, single prediction bands are wider than mean prediction ...


1

You can check the documentation about the option NominalVariables http://reference.wolfram.com/language/ref/NominalVariables.html NominalVariables is an option for machine learning functions such as LinearModelFit or Classify that specifies which variables should be treated as having discrete values specified by names. So LogitModelFit[{{300, 0, ...



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