You could try "gmax" from Robert Israel's Maple Advisor Database. It gave 2.236067977 as a max of your function between 0 and 2*Pi. Hope this helps, J. Tarr

You could try ?Worksheet, ?MathML and ?XML. Hope this helps, J. Tarr

AFAIK Scatterplot and display do not accept the legend option. However, if you want to name your scatterplot, try using the "title" option in the scatterplot or display command like this: restart; with(Statistics): N := 200: U := Sample(Normal(0, 1), N): X := : Y := : P := ScatterPlot(X, Y, lowess, degree = 3, thickness = 3,title="The data (blue) and\nFitted curve (red)"): Q := plot(sin(2*Pi*x/N), x = 1..N, thickness = 3, color = red): plots[display](P, Q); Hope this helps, J. Tarr

Please see ?interface and do something like this: restart; interface(displayprecision=6); a := 123.456789; J. Tarr

I am wondering why you should want to put a dot above a hat (circumflex). J. Tarr

This produces an algebraic solution to your ODE and then plots it: restart; with(plots): dsolve( {diff(y(t), t, t) + 6*diff(y(t), t) = 80*exp(2*t), y(0) = 4, D(y)(0) = 3 }, y(t)); assign(%); plot(y(t),t= -1..1,color= blue,title="2nd order ODE"); But if you really want to use the DEtools[DEplot] command, do something like this: restart;with(plots):with(DEtools): plot1 := DEplot( diff(y(t), t, t) + 6*diff(y(t), t) = 80*exp(2*t), [y(t)],t = -1 .. 1, [[ y(0) = 4, D(y)(0) = 3 ]],title= "2nd order ODE",linecolor=red,thickness=0): display(plot1); Hope all this helps, J. Tarr

Please see ?add. Try changing "sum" in your final command to "add". Hope this helps, J. Tarr Later edit. Sorry Joe. Once again there was nothing there when I posted, but somehow I have managed to post on top of yours.

Please see ?assign. You could do something like this: restart; y := proc (t) options operator, arrow; a*exp(2*t)+b*t*exp(2*t)+c*t^2*exp(2*t) end proc; sol := solve({3*c = -9, 3*b+12*c = 8, 3*a+6*b+2*c = 4}, {a, b, c}); assign(sol); y(3); Hope this helps, J. Tarr

Peter, All of it works fine for me. If you are using document mode, open a new file in worksheet mode (ctrl+N) and copy all of the following into it. Document mode can play tricks. restart; lignsub:=diff(y(x), x, x) = 0.7904982116e-3*sqrt(1+(diff(y(x), x))^2)/(1+0.1012020000e-2*sqrt(1+(diff(y(x), x))^2)); `løsning` := dsolve({lignsub, y(0) = 0, (D(y))(0) = 0}, type = numeric, output = listprocedure); u := subs(`løsning`, y(x)); u_dot := subs(`løsning`, diff(y(x),x)); plots[odeplot](`løsning`,[[x,y(x)],[x,diff(y(x),x)]],-10..10); seq(u(x),x=100..1000,200); seq(u_dot(x),x=100..1000,200); Hope this helps J. Tarr

You can add to the odeplot above thus: plots[odeplot](løsning,[[x,y(x)],[x,diff(y(x),x)]], -10..10); For more about these plots please see ?odeplot Hope this helps, J. Tarr

You could do something like this: u_dot := subs(løsning, diff(y(x),x)); Hope this helps, J. Tarr

Please see ?insertpattern. You could do something like this: restart; with(inttrans); addtable(invlaplace,exp(-p::algebraic*sqrt(s)/s),erfc(p/(2*sqrt(t))),s,t); invlaplace(exp(-p*sqrt(s)/s),s,t); invlaplace(exp(-alpha*sqrt(s)/s),s,t); Hope this helps J. Tarr

Share prices have an inconvenient habit of not conforming consistently to any statistical conventions - even as aggregates. Like jellyfish, they change shape all the time (and can give one a painful sting too). Good luck with your project, J. Tarr

Just to cheer the developers up a little bit, I tried ?copy. Scored a hole in one :-) J. Tarr

I hope that you were not offended, but your question looked just like something out of a textbook. Please accept my apologies. The Statistics[Distributions][LogNormal] in Maple 10 agrees with other sources, notably sections 1.3.3.6.9 and 8.1.6.4 in Engineering Statistics (in my opinion, the best book ever written on applied statistics). I believe that lognormal in the old statistics package had some bugs, so that might be the problem that you are seeing. If you are seeing differences between your results from @Risk and Maple' new Statistics package, you could check using the lognormal functions in MS Excel. You haven't mentioned the reason for believing that the distributions are lognormal, and perhaps you should question that. I would suggest that estimating a distribution's parameters from three quantiles is not going to produce very reliable results, but perhaps you have no other option. Good luck, J. Tarr