Counting:                         fact(n)   [compute n factorial, n!];
choose(n,k)   [compute binomial coefficient nCk]; 
perm(n,k)   [compute permutation coefficient nPk];
Especially for larger n...   logfact(n)   [compute ln(n!)];   logfact(n,b)   [compute log(n!,base=b)].

Sample Statistics*:          ssize(x)   [sample size; i.e., number of values in x];  
sum(x)   [sum of x values];  
mean(x) or avg(x)    [sample mean or average of x values];  
variance(x)   [sample variance of x values];  
sd(x) or stdev(x)   [sample standard deviation of x values];  
mad(x)   [mean absolute deviation of x values];  
prop(x,q [, direction])   [proportion of values in x that are <= q (when 'direction' arg omitted)      or...
           for   = q,  >= q,   < q,  or   > q,   add the optional 'direction' argument   '=',  '>=', '<',  or   '>')],  
           e.g., prop(x,23.5,'=') gives proportion of x values equal to 23.5;
min(x)   [minimum of values in x];  
max(x)   [maximum of values in x];  
quantile(p,x) or quant(p,x)   [compute pth quantile of data x using Excel's percentile()=percentile.inc() formula];  
median(x)   [sample median of x values];  
iqr(x)   [interquartile range of x values];  
range(x)   [max(x)-min(x)].           

*In these functions, x is a string of comma-separated numbers,
          e.g., in the input box, enter 'mean("1,2,3")'   (but without the outside single quotes).
Alternatively, first create a string of comma-separated numbers, then apply a summary statistic formula,
           e.g., enter 'x=rnorm(0,1,25)' and then enter 'sd(x)' .

Generate Random #'s:    runi()   [generate an observation from Unif(0,1), the continuous Uniform(0,1) distribution];
runi(a,b [,N])   [generate N observations from Unif(a,b); if optional arg N is dropped, it is set to 1];
rnorm(mean,sd [,N])   [generate N from Normal(mean,sd)];
rexp(mean [,N])   [generate N from Expon(mean)];
rgam(shape,scale [,N])   [generate N from Gamma(shape,scale)];
rchisq(df [,N])   [generate N from ChiSq(df)];
rt(df [,N])   [generate N from Student's t(df)];    

rduni(a,b [,N])   [generate N from discrete U{a,..,b}];
rbin(n,p [,N])   [generate N from binom(n,p)];
rgeo(p [,N])   [generate N from Geom(p,#trials)];
rpoi(mean [,N])   [generate N from Poi(mean)];
rdice(n [,N])   [roll n six-sided dice N times, record total updots each time];  
rdiscrete(v,p [,N])   [generate N from Discrete(v,p), where v are the possible values and p are the probabilities;
            here, v and p are strings of comma-separated numbers; e.g., submit rdiscrete("0,5,10","0.2,0.3,0.5",500).

Generate Averages:        ravguni(a,b,N) [gen avg based on N from Unif(a,b)], ravgnorm(mean,sd,N) [gen avg based on N from Normal(mean,sd)], ravgexp(mean,N) [gen avg based on N from Expon(mean)], ravggam(shape,scale,N) [gen avg based on N from Gamma(shape,scale)];    ravgduni(a,b,N) [gen avg based on N from discrete U{a,..,b}], ravgbin(n,p,N) [gen avg based on N from binom(n,p)], ravggeo(p,N) [gen avg based on N from Geom(p,#trials)], ravgpoi(mean,N) [gen avg based on N from Poi(mean)].

Trigonometry:                  cos(theta) [cosine of radians argument theta], acos(y) [arccos of y in radians],
cosd(deg) [cosine of degrees argument deg], acosd(y)[arccos of y in degrees],..., plus sin() and tan() analogs.

Logarithms:                     log(x) [compute natural log of x, ln(x)],      log(x,b) [compute log of x base b], ...

Constants:                      pi=Math.PI = π = 3.141592653589793 (approx).

Transformation Trick:      To apply a function to each element in a comma-separated string x and store results in t,
enter, e.g.,    ' arr=x.split(","); arr=arr.map((u) => 10*u+5); t=arr.toString() '    in the input box.
If, e.g., x="1,2,5", then this code would give t="15,25,55".
Change function'10*u+5' and variable names, x and t, as desired.