Maple has a statistics package built in. To
activate the package use the
with(stats) command:
> with(stats);
[describe, fit, importdata, random,statevalf, statplots, transform]
Let us go ahead and describe a sample data set. Note that the data is enclosed in square brackets, [].
> sample := [52.54, 89.45, 36.98, 101.32,74.03, 58.65, 18.00, 25.45];
sample := [52.54, 89.45, 36.98, 101.32,74.03, 58.65, 18.00, 25.45]
Now we can quickly calculate the mean, median,
and standard deviations with the
describe command:
> describe[mean](sample);
57.05250000
> describe[median](sample);
55.59500000
> describe[standarddeviation](sample);
27.94432131
Maple can calculate probability distributions including normal, c-squared, student T, F, and exponential. For example, suppose you had a mean value of 76.43 with a 2.3 standard deviation:
> ex_mean := 76.43;
ex_mean :=76.43
> ex_sdev := 2.3;
ex_sdev :=2.3
Now, you want the (normald)
probability that a value is <= 73.40:
> prob := statevalf[cdf,normald[ex_mean,ex_sdev]](73.40);
prob :=.09385374720
Maple can fit models to data via Least Squares methods. One needs to define the data:
> Xdata := [-1.9,-1.1,0.2,2.1,3.0];
Xdata := [-1.9, -1.1,.2, 2.1, 3.0]
> Ydata := [-4.1,-3.0,-2.2,-0.1,0.8];
Ydata := [-4.1, -3.0,-2.2, -.1, .8]
Now, go ahead and fit this to a standard y=mx+b equation:
>eq_fit:=fit[leastsquare[[x,y],y=m*x+b,{m,b}]]([Xdata,Ydata]);
eq_fit := y = .9758308159 x - 2.168882175
L O S A L A M O S N A T I O N A L L A B O R A T O R Y
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