# Statistics, mean, stanard deviation (population/sample), sum and range browsing

• Here is a quick example of how to calculate a few common statistical functions on a list of numbers. You can generate the list any way you like. I have generated the list by drawing a sample of 10 numbers from a uniform distribution. The list is then stored in the variable $m1. # Generates a sample of 10 numbers from a uniform distribution on the range 0 to 5$m1 = maple("
randomize(): #initialise random seed
A:=Statistics[RandomVariable](Uniform(0,5)):  # uniform on a range 0 to 5
nums:=Statistics[Sample](A,10);  #pull 10 numbers from the sample
convert(nums,list)  # converts the vector of numbers to a list
");


If you want to define your own list of numbers you can define $m1 as something like $m1="[1.2, 4.4, 5.0, 2.5, 3.4, 4.6, 8.6, 7.8, 1.7, 0.4]";


You can then calculate the various quantities on the list stored in $m1. Note: As a check, in the following I have calculated the sample and population standard deviations using built in functions and the explicit formulas. $m2=maple("
#mean
mu:=Statistics[Mean]($m1); #sample s.d. s0:=Statistics[StandardDeviation]($m1);
s1:=sqrt(add(i, i = map( x-> (x-mu)^2, $m1))/(nops($m1)-1)); #explicit

#population s.d.
sigma0:=evalf(Statistics[StandardDeviation]($m1)*sqrt((nops($m1)-1)/nops($m1))); sigma1:=sqrt(add(i, i = map( x-> (x-mu)^2,$m1))/(nops($m1))); #explicit #sum sum0:=add(i,i=$m1);

#range
range0:=abs(max($m1)-min($m1));

# return values
mu,s0,s1,sigma0,sigma1,sum0,range0
");

#use switch to get the values from the maple call
$mean=switch(0,$m2);
$s0=switch(1,$m2);
$sigma0=switch(3,$m2);
$sum0=switch(5,$m2);
$range0=switch(6,$m2);


The resultant variable in the algorithm section should now be set to something like the following