Struct average::Variance

pub struct Variance { /* fields omitted */ }

Estimate the arithmetic mean and the variance of a sequence of numbers ("population").

This can be used to estimate the standard error of the mean.

Example

use average::Variance;

let a: Variance = (1..6).map(f64::from).collect();
println!("The mean is {} ± {}.", a.mean(), a.error());

Implementations

impl Variance

pub fn new() -> Variance

Create a new variance estimator.

pub fn is_empty(&self) -> bool

Determine whether the sample is empty.

pub fn mean(&self) -> f64

Estimate the mean of the population.

Returns 0 for an empty sample.

pub fn len(&self) -> u64

Return the sample size.

pub fn sample_variance(&self) -> f64

Calculate the sample variance.

This is an unbiased estimator of the variance of the population.

pub fn population_variance(&self) -> f64

Calculate the population variance of the sample.

This is a biased estimator of the variance of the population.

pub fn error(&self) -> f64

Estimate the standard error of the mean of the population.

Trait Implementations

impl Clone for Variance

fn clone(&self) -> Variance

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for Variance

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for Variance

fn default() -> Variance

Returns the "default value" for a type.

impl Estimate for Variance

fn add(&mut self, sample: f64)

Add an observation sampled from the population.

fn estimate(&self) -> f64

Estimate the statistic of the population.

impl<'a> FromIterator<&'a f64> for Variance

fn from_iter<T>(iter: T) -> Variance where
    T: IntoIterator<Item = &'a f64>, 

Creates a value from an iterator.

impl FromIterator<f64> for Variance

fn from_iter<T>(iter: T) -> Variance where
    T: IntoIterator<Item = f64>, 

Creates a value from an iterator.

impl Merge for Variance

fn merge(&mut self, other: &Variance)

Merge another sample into this one.

Example

use average::{Variance, Merge};

let sequence: &[f64] = &[1., 2., 3., 4., 5., 6., 7., 8., 9.];
let (left, right) = sequence.split_at(3);
let avg_total: Variance = sequence.iter().collect();
let mut avg_left: Variance = left.iter().collect();
let avg_right: Variance = right.iter().collect();
avg_left.merge(&avg_right);
assert_eq!(avg_total.mean(), avg_left.mean());
assert_eq!(avg_total.sample_variance(), avg_left.sample_variance());