This page is part of the HL7 Terminology (v5.3.0: Release) based on FHIR R4. This is the current published version in its permanent home (it will always be available at this URL). For a full list of available versions, see the Directory of published versions
: StatisticsCode - XML Representation
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<CodeSystem xmlns="http://hl7.org/fhir">
<id value="observation-statistics"/>
<meta>
<lastUpdated value="2020-04-09T21:10:28.568+00:00"/>
</meta>
<text>
<status value="generated"/>
<div xmlns="http://www.w3.org/1999/xhtml">Placeholder</div>
</text>
<url value="http://terminology.hl7.org/CodeSystem/observation-statistics"/>
<identifier>
<system value="urn:ietf:rfc:3986"/>
<value value="urn:oid:2.16.840.1.113883.4.642.1.1126"/>
</identifier>
<version value="0.1.0"/>
<name value="StatisticsCode"/>
<title value="StatisticsCode"/>
<status value="draft"/>
<experimental value="false"/>
<date value="2020-04-09T21:10:28+00:00"/>
<publisher value="HL7 (FHIR Project)"/>
<contact>
<telecom>
<system value="url"/>
<value value="http://hl7.org/fhir"/>
</telecom>
<telecom>
<system value="email"/>
<value value="fhir@lists.hl7.org"/>
</telecom>
</contact>
<description
value="The statistical operation parameter -"statistic" codes."/>
<caseSensitive value="true"/>
<valueSet
value="http://terminology.hl7.org/ValueSet/observation-statistics"/>
<content value="complete"/>
<concept>
<code value="average"/>
<display value="Average"/>
<definition
value="The [mean](https://en.wikipedia.org/wiki/Arithmetic_mean) of N measurements over the stated period."/>
</concept>
<concept>
<code value="maximum"/>
<display value="Maximum"/>
<definition
value="The [maximum](https://en.wikipedia.org/wiki/Maximal_element) value of N measurements over the stated period."/>
</concept>
<concept>
<code value="minimum"/>
<display value="Minimum"/>
<definition
value="The [minimum](https://en.wikipedia.org/wiki/Minimal_element) value of N measurements over the stated period."/>
</concept>
<concept>
<code value="count"/>
<display value="Count"/>
<definition
value="The [number] of valid measurements over the stated period that contributed to the other statistical outputs."/>
</concept>
<concept>
<code value="total-count"/>
<display value="Total Count"/>
<definition
value="The total [number] of valid measurements over the stated period, including observations that were ignored because they did not contain valid result values."/>
</concept>
<concept>
<code value="median"/>
<display value="Median"/>
<definition
value="The [median](https://en.wikipedia.org/wiki/Median) of N measurements over the stated period."/>
</concept>
<concept>
<code value="std-dev"/>
<display value="Standard Deviation"/>
<definition
value="The [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation) of N measurements over the stated period."/>
</concept>
<concept>
<code value="sum"/>
<display value="Sum"/>
<definition
value="The [sum](https://en.wikipedia.org/wiki/Summation) of N measurements over the stated period."/>
</concept>
<concept>
<code value="variance"/>
<display value="Variance"/>
<definition
value="The [variance](https://en.wikipedia.org/wiki/Variance) of N measurements over the stated period."/>
</concept>
<concept>
<code value="20-percent"/>
<display value="20th Percentile"/>
<definition
value="The 20th [Percentile](https://en.wikipedia.org/wiki/Percentile) of N measurements over the stated period."/>
</concept>
<concept>
<code value="80-percent"/>
<display value="80th Percentile"/>
<definition
value="The 80th [Percentile](https://en.wikipedia.org/wiki/Percentile) of N measurements over the stated period."/>
</concept>
<concept>
<code value="4-lower"/>
<display value="Lower Quartile"/>
<definition
value="The lower [Quartile](https://en.wikipedia.org/wiki/Quartile) Boundary of N measurements over the stated period."/>
</concept>
<concept>
<code value="4-upper"/>
<display value="Upper Quartile"/>
<definition
value="The upper [Quartile](https://en.wikipedia.org/wiki/Quartile) Boundary of N measurements over the stated period."/>
</concept>
<concept>
<code value="4-dev"/>
<display value="Quartile Deviation"/>
<definition
value="The difference between the upper and lower [Quartiles](https://en.wikipedia.org/wiki/Quartile) 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."/>
</concept>
<concept>
<code value="5-1"/>
<display value="1st Quintile"/>
<definition
value="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."/>
</concept>
<concept>
<code value="5-2"/>
<display value="2nd Quintile"/>
<definition
value="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."/>
</concept>
<concept>
<code value="5-3"/>
<display value="3rd Quintile"/>
<definition
value="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."/>
</concept>
<concept>
<code value="5-4"/>
<display value="4th Quintile"/>
<definition
value="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."/>
</concept>
<concept>
<code value="skew"/>
<display value="Skew"/>
<definition
value="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](https://en.wikipedia.org/wiki/Skewness)."/>
</concept>
<concept>
<code value="kurtosis"/>
<display value="Kurtosis"/>
<definition
value="Kurtosis is a measure of the "tailedness" of the probability distribution of a real-valued random variable. Source: [Wikipedia](https://en.wikipedia.org/wiki/Kurtosis)."/>
</concept>
<concept>
<code value="regression"/>
<display value="Regression"/>
<definition
value="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](https://en.wikipedia.org/wiki/Simple_linear_regression) This Statistic code will return both a gradient and an intercept value."/>
</concept>
</CodeSystem>
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