statistics
Import it with @use:statistics, then call its constructs under that name.
@use:statisticsConstructs
statistics.normal
The normal density , drawn across four standard deviations either side of the mean.statistics.standard-normal
The standard normal — the density every other normal is a shift and a stretch of. It is normal with its parameters fixed, called the way you would call it yourself: inside the pack its own constructs are named without the pack prefix.colour
Default
bluestatistics.normal-area
A normal density with the probability between two points shaded, and the bounds marked on the axis. The curve it shades is named inside the template, so two uses in one graph never collide (D36) and the caller supplies no id.statistics.mean-marker
A vertical line at the mean, labelled .statistics.deviation-markers
Vertical lines one standard deviation either side of the mean.statistics.z-point
A point sitting on a normal curve, labelled with its z-score.
x and z are both supplied because a template cannot compute one from the other: substitution splices text and the value folds parse literals rather than evaluating arithmetic. Writing out is the honest form until the language grows expression abstraction.statistics.uniform
The continuous uniform density on .statistics.exponential
The exponential density with rate .statistics.laplace
The Laplace density with location and scale .statistics.logistic
The logistic density with location and scale .statistics.cauchy
The Cauchy density with location and scale — heavy enough in the tails that it has no mean, which is why it is drawn ten scales wide.statistics.t
Student's with degrees of freedom — the normal's heavier-tailed cousin, which is what a small sample's mean actually follows.statistics.chi-squared
The chi-squared density with degrees of freedom.
Drawn from just above zero: at the density is infinite for and the curve would have nowhere to start.statistics.f-distribution
The density with and degrees of freedom — the ratio of two scaled chi-squareds, and so what a variance ratio follows.statistics.normal-cdf
The normal's cumulative distribution — the probability of landing at or below , which is the shaded area of normal read off as a height.statistics.exponential-cdf
The exponential's cumulative distribution, .statistics.uniform-cdf
The uniform's cumulative distribution — the straight line every other CDF is measured against.statistics.logistic-cdf
The logistic's cumulative distribution — the sigmoid, which is this and nothing more.statistics.cauchy-cdf
The Cauchy's cumulative distribution, in terms of .statistics.normal-tail-above
The upper tail of a normal beyond a critical value — the rejection region of a one-sided test, shaded and marked.
A separate construct from normal-tail-below rather than one with a side to choose: a template compares a chosen side as WRITTEN text, so Upper would quietly take the other branch. Two names cannot be got wrong.statistics.normal-tail-below
The lower tail of a normal below a critical value.statistics.bernoulli
The Bernoulli mass function — one trial, drawn as the two outcomes it has.
Like every discrete construct here it is drawn as STEMS rather than bars: a bar chart stands on a categorical axis, and the support of a distribution is a run of numbers, so the mass sits on a numeric axis like everything else on the plane.statistics.binomial
The binomial mass function — one stem for each from to , generated from the parameters rather than written out.statistics.poisson
The Poisson mass function . The support is infinite, so to says where to stop drawing — far enough out that the tail is invisible, not so far that the stems vanish.statistics.geometric
The geometric mass function — the trial on which the first success arrives, counted from .statistics.box-at
A boxplot of one sample, placed in its own lane. sample takes a written series or a dataset column; at is where the lane sits on the other axis.