Your model function is not differentiable at t=0, yet it appears that t=0 is one of the data points. Try removing this one data point. Of course, I can't test this without having your actual data.

If all of the parameters being estimated are coefficients, then you can and should use linear regression. The model function doesn't need to be a linear function of the data variables in order to use linear regression; it merely needs to be a linear function of the parameters being estimated.

If A is the matrix, then you can use convert(A^+, listlist). The first argument, A^+, means the transpose of A. Using this doesn't require foreknowledge of the number of columns of A.

k1:= seq(add(X[h,t], t= 1..23) >= 9, h= 1..6);

foldl(gcd, 0, P[]);

The title says it all. The command that you want is assign~(A =~ B):.

But I can't imagine any situation where I would want to or where it would be beneficial to make 1 million assignment statements. I suspect that Maple will crash or will become extremely slow.

What you're asking for---to reverse the effect of KroeneckerDelta---is mathematically impossible. It would be like multiplying ME by 0 and then trying to reverse the effect of that.

Use "Export as"  (rather than "Save" or "Save as") from the File menu to save the inner file as type Maple Input. Simply changing the file extension to .mpl is not enough.

The answer Dirac(X - 0.5) is what you get if X hasn't been previously assigned. So it seems that the X in you PDF command is not the same as the X in your RandomVariable command. One of the Xs may contain a nonprinting character.

This may be a bug that has been corrected in Maple 2016; I can't duplicate it. Try using restart. If that doesn't work, here's a workaround that should work in any Maple with the Statistics package:

eval(PDF(X, _t), _t= 0.5);

Let me know how it goes.

I can't tell for sure because you didn't show your whole code, but I suspect that you spelled the Greek letter pi rather than Pi. Only Pi represents the familiar mathematical constant, even though they both appear the same when prettyprinted.

f:= x-> -x^2+3*x+2:
l:= x-> 4*x:
m:= x-> 2*x-4:
plots:-display(
   plots:-inequal(
      {y <= f(x), y <= l(x), y >= m(x), y >= 0},
      x= -1..4, y= -5..10, nolines
   ),
   plot([f,l,m](x), x= -1..4, y= -5..10)
);

The t is a bound variable (see this Wikipedia article) bound by the maximize, and the m is bound by the plot.

The system of equations that you passed to fsolve clearly has no solution. This is obvious by inspection of the first two equations (as ordered in the error message), which give different values for y[1].

X:= <21.2, 24.7, 20.8, 20.8, 20.3>:
Y:= <16.6, 19.7, 16.4, 16.8, 16.9>:
L:= Statistics:-LinearFit([1,x], <X|Y>, x);
plot([<X|Y>, L], x= min(X)..max(X), style= [point, line]);