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

This page Maintained by Dale H. Leschnitzer

Last Modified Monday, November 4, 1996

**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***Operated by the University of California for the U.S. Department of Energy*