I have a list with values at some points:
list1 = {{-5.4, 0.000513081122}, {-4.8, 0.000840821088}, {-4.0,
0.001059617102}, {-3.2, 0.001114222604}, {-2.4,
0.001086309651}, {-1.6, 0.001120999228}, {-0.80,
0.001128926919}, {0.0, 0.001119484321}, {0.80,
0.001128926919}, {1.6, 0.001120999228}, {2.4,
0.001086309651}, {3.2, 0.001114222604}, {4.0,
0.001059617102}, {4.8, 0.000840821088}, {5.4, 0.000513081122}};
Can I predict the value outside the data set, for eg., at -15, 10, 10, 15? I tried the following:
fit = Fit[list1, Table[x^i, {i, 0, 500}], x];
p1 = ListPlot[list1, Joined -> True,
PlotRange -> {{-10, 10}, {0.0015, 0.0001}}];
p2 = Plot[fit, {x, -10, 10},
PlotRange -> {{-10, 10}, {0.0015, 0.0001}}];
Show[p1, p2]
However, the curve blows up outside the data point. I tried using this solution, but still no success. How should I proceed. Any help would be appreciated.
NonlinearModelFitis necessary as that function will give you confidence bands (asFitjust gives you the fit and nothing else). There must be some theoretical model you could use rather than an arbitrary polynomial. Also (unless this data is not real), you really only have 8 points because of the exact symmetry in the data. – JimB Nov 27 '19 at 14:38