# [−][src]Trait statrs::statistics::Mean

The `Mean`

trait specifies that an object has a closed form
solution for its mean(s)

## Required methods

`fn mean(&self) -> T`

Returns the mean. May panic depending on the implementor.

# Examples

use statrs::statistics::Mean; use statrs::distribution::Uniform; let n = Uniform::new(0.0, 1.0).unwrap(); assert_eq!(0.5, n.mean());

## Implementations on Foreign Types

`impl Mean<f64> for [f64]`

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`fn mean(&self) -> f64`

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Evaluates the sample mean, an estimate of the population mean.

# Remarks

Returns `f64::NAN`

if data is empty or an entry is `f64::NAN`

# Examples

#[macro_use] extern crate statrs; use std::f64; use statrs::statistics::Mean; let x = []; assert!(x.mean().is_nan()); let y = [0.0, f64::NAN, 3.0, -2.0]; assert!(y.mean().is_nan()); let z = [0.0, 3.0, -2.0]; assert_almost_eq!(z.mean(), 1.0 / 3.0, 1e-15);

## Implementors

`impl Mean<f64> for Bernoulli`

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`impl Mean<f64> for Beta`

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`fn mean(&self) -> f64`

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Returns the mean of the beta distribution

# Formula

α / (α + β)

where `α`

is shapeA and `β`

is shapeB

`impl Mean<f64> for Binomial`

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`impl Mean<f64> for Categorical`

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`fn mean(&self) -> f64`

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Returns the mean of the categorical distribution

# Formula

Σ(j * p_j)

where `p_j`

is the `j`

th probability mass,
`Σ`

is the sum from `0`

to `k - 1`

,
and `k`

is the number of categories

`impl Mean<f64> for Chi`

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`impl Mean<f64> for ChiSquared`

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`fn mean(&self) -> f64`

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Returns the mean of the chi-squared distribution

# Formula

`k`

where `k`

is the degrees of freedom

`impl Mean<f64> for DiscreteUniform`

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`fn mean(&self) -> f64`

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Returns the mean of the discrete uniform distribution

# Formula

(min + max) / 2

`impl Mean<f64> for Erlang`

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`impl Mean<f64> for Exponential`

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`fn mean(&self) -> f64`

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Returns the mean of the exponential distribution

# Formula

1 / λ

where `λ`

is the rate

`impl Mean<f64> for FisherSnedecor`

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`impl Mean<f64> for Gamma`

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`impl Mean<f64> for Geometric`

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`impl Mean<f64> for Hypergeometric`

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`impl Mean<f64> for InverseGamma`

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`impl Mean<f64> for LogNormal`

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`fn mean(&self) -> f64`

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Returns the mean of the log-normal distribution

# Formula

e^(μ + σ^2 / 2)

where `μ`

is the location and `σ`

is the scale

`impl Mean<f64> for Normal`

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`fn mean(&self) -> f64`

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Returns the mean of the normal distribution

# Remarks

This is the same mean used to construct the distribution

`impl Mean<f64> for Pareto`

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`fn mean(&self) -> f64`

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Returns the mean of the Pareto distribution

# Formula

if α <= 1 { INF } else { (α * x_m)/(α - 1) }

where `x_m`

is the scale and `α`

is the shape

`impl Mean<f64> for Poisson`

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`fn mean(&self) -> f64`

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Returns the mean of the poisson distribution

# Formula

`λ`

where `λ`

is the rate

`impl Mean<f64> for StudentsT`

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`impl Mean<f64> for Triangular`

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`fn mean(&self) -> f64`

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Returns the mean of the triangular distribution

# Formula

(min + max + mode) / 3

`impl Mean<f64> for Uniform`

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`fn mean(&self) -> f64`

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Returns the mean for the continuous uniform distribution

# Formula

(min + max) / 2

`impl Mean<f64> for Weibull`

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`fn mean(&self) -> f64`

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Returns the mean of the weibull distribution

# Formula

λΓ(1 + 1 / k)

where `k`

is the shape, `λ`

is the scale, and `Γ`

is
the gamma function

`impl Mean<Vec<f64>> for Dirichlet`

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`fn mean(&self) -> Vec<f64>`

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Returns the means of the dirichlet distribution

# Formula

α_i / α_0

for the `i`

th element where `α_i`

is the `i`

th concentration parameter
and `α_0`

is the sum of all concentration parameters