Enum StatisticsCode

pub enum StatisticsCode {
    N20Percent,
    N4Dev,
    N4Lower,
    N4Upper,
    N51,
    N52,
    N53,
    N54,
    N80Percent,
    Average,
    Count,
    Kurtosis,
    Maximum,
    Median,
    Minimum,
    Regression,
    Skew,
    StdDev,
    Sum,
    TotalCount,
    Variance,
}

StatisticsCode. The statistical operation parameter -“statistic” codes.

FHIR version: 5.0.0.

Variants

N20Percent

20-percent

20th Percentile. The 20th Percentile of N measurements over the stated period.

N4Dev

4-dev

Quartile Deviation. The difference between the upper and lower Quartiles is called the Interquartile range. (IQR = Q3-Q1) Quartile deviation or Semi-interquartile range is one-half the difference between the first and the third quartiles.

N4Lower

4-lower

Lower Quartile. The lower Quartile Boundary of N measurements over the stated period.

N4Upper

4-upper

Upper Quartile. The upper Quartile Boundary of N measurements over the stated period.

N51

5-1

1st Quintile. The lowest of four values that divide the N measurements into a frequency distribution of five classes with each containing one fifth of the total population.

N52

5-2

2nd Quintile. The second of four values that divide the N measurements into a frequency distribution of five classes with each containing one fifth of the total population.

N53

5-3

3rd Quintile. The third of four values that divide the N measurements into a frequency distribution of five classes with each containing one fifth of the total population.

N54

5-4

4th Quintile. The fourth of four values that divide the N measurements into a frequency distribution of five classes with each containing one fifth of the total population.

N80Percent

80-percent

80th Percentile. The 80th Percentile of N measurements over the stated period.

Average

average

Average. The mean of N measurements over the stated period.

Count

count

Count. The [number] of valid measurements over the stated period that contributed to the other statistical outputs.

Kurtosis

kurtosis

Kurtosis. Kurtosis is a measure of the “tailedness” of the probability distribution of a real-valued random variable. Source: Wikipedia.

Maximum

maximum

Maximum. The maximum value of N measurements over the stated period.

Median

median

Median. The median of N measurements over the stated period.

Minimum

minimum

Minimum. The minimum value of N measurements over the stated period.

Regression

regression

Regression. Linear regression is an approach for modeling two-dimensional sample points with one independent variable and one dependent variable (conventionally, the x and y coordinates in a Cartesian coordinate system) and finds a linear function (a non-vertical straight line) that, as accurately as possible, predicts the dependent variable values as a function of the independent variables. Source: Wikipedia This Statistic code will return both a gradient and an intercept value.

Skew

skew

Skew. Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. The skewness value can be positive or negative, or even undefined. Source: Wikipedia.

StdDev

std-dev

Standard Deviation. The standard deviation of N measurements over the stated period.

Sum

sum

Sum. The sum of N measurements over the stated period.

TotalCount

total-count

Total Count. The total [number] of valid measurements over the stated period, including observations that were ignored because they did not contain valid result values.

Variance

variance

Variance. The variance of N measurements over the stated period.

Trait Implementations

impl AsRef<str> for StatisticsCode

fn as_ref(&self) -> &str

Converts this type into a shared reference of the (usually inferred) input type.

impl Clone for StatisticsCode

fn clone(&self) -> StatisticsCode

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for StatisticsCode

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for StatisticsCode

fn deserialize<D>( deserializer: D, ) -> Result<StatisticsCode, <D as Deserializer<'de>>::Error>

where D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Display for StatisticsCode

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

Formats the value using the given formatter.

impl From<StatisticsCode> for CodeableConcept

fn from(code: StatisticsCode) -> CodeableConcept

Converts to this type from the input type.

impl From<StatisticsCode> for Coding

fn from(code: StatisticsCode) -> Coding

Converts to this type from the input type.

impl FromStr for StatisticsCode

type Err = String

The associated error which can be returned from parsing.

fn from_str(s: &str) -> Result<StatisticsCode, <StatisticsCode as FromStr>::Err>

Parses a string s to return a value of this type.

impl Hash for StatisticsCode

fn hash<__H>(&self, state: &mut __H)

where __H: Hasher,

Feeds this value into the given Hasher.

fn hash_slice<H>(data: &[Self], state: &mut H)

where H: Hasher, Self: Sized,

Feeds a slice of this type into the given Hasher.

impl PartialEq for StatisticsCode

fn eq(&self, other: &StatisticsCode) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Serialize for StatisticsCode

fn serialize<S>( &self, serializer: S, ) -> Result<<S as Serializer>::Ok, <S as Serializer>::Error>

where S: Serializer,

Serialize this value into the given Serde serializer.

impl Copy for StatisticsCode

impl Eq for StatisticsCode

impl StructuralPartialEq for StatisticsCode