HL7 Terminology
2.0.0 - Publication
HL7 Terminology
2.0.0 - Publication
This page is part of the HL7 Terminology (v2.0.0: Release) based on FHIR R4. The current version which supercedes this version is 5.2.0. For a full list of available versions, see the Directory of published versions 
<CodeSystem xmlns="http://hl7.org/fhir">
<id value="observation-statistics"/>
<meta>
<lastUpdated value="2020-04-10T07:10:28.568+10:00"/>
</meta>
<text>
<status value="generated"/>
<div xmlns="http://www.w3.org/1999/xhtml"><p>This code system http://terminology.hl7.org/CodeSystem/observation-statistics defines the following codes:</p><table class="codes"><tr><td style="white-space:nowrap"><b>Code</b></td><td><b>Display</b></td><td><b>Definition</b></td></tr><tr><td style="white-space:nowrap">average<a name="observation-statistics-average"> </a></td><td>Average</td><td>The [mean](https://en.wikipedia.org/wiki/Arithmetic_mean) of N measurements over the stated period.</td></tr><tr><td style="white-space:nowrap">maximum<a name="observation-statistics-maximum"> </a></td><td>Maximum</td><td>The [maximum](https://en.wikipedia.org/wiki/Maximal_element) value of N measurements over the stated period.</td></tr><tr><td style="white-space:nowrap">minimum<a name="observation-statistics-minimum"> </a></td><td>Minimum</td><td>The [minimum](https://en.wikipedia.org/wiki/Minimal_element) value of N measurements over the stated period.</td></tr><tr><td style="white-space:nowrap">count<a name="observation-statistics-count"> </a></td><td>Count</td><td>The [number] of valid measurements over the stated period that contributed to the other statistical outputs.</td></tr><tr><td style="white-space:nowrap">total-count<a name="observation-statistics-total-count"> </a></td><td>Total Count</td><td>The total [number] of valid measurements over the stated period, including observations that were ignored because they did not contain valid result values.</td></tr><tr><td style="white-space:nowrap">median<a name="observation-statistics-median"> </a></td><td>Median</td><td>The [median](https://en.wikipedia.org/wiki/Median) of N measurements over the stated period.</td></tr><tr><td style="white-space:nowrap">std-dev<a name="observation-statistics-std-dev"> </a></td><td>Standard Deviation</td><td>The [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation) of N measurements over the stated period.</td></tr><tr><td style="white-space:nowrap">sum<a name="observation-statistics-sum"> </a></td><td>Sum</td><td>The [sum](https://en.wikipedia.org/wiki/Summation) of N measurements over the stated period.</td></tr><tr><td style="white-space:nowrap">variance<a name="observation-statistics-variance"> </a></td><td>Variance</td><td>The [variance](https://en.wikipedia.org/wiki/Variance) of N measurements over the stated period.</td></tr><tr><td style="white-space:nowrap">20-percent<a name="observation-statistics-20-percent"> </a></td><td>20th Percentile</td><td>The 20th [Percentile](https://en.wikipedia.org/wiki/Percentile) of N measurements over the stated period.</td></tr><tr><td style="white-space:nowrap">80-percent<a name="observation-statistics-80-percent"> </a></td><td>80th Percentile</td><td>The 80th [Percentile](https://en.wikipedia.org/wiki/Percentile) of N measurements over the stated period.</td></tr><tr><td style="white-space:nowrap">4-lower<a name="observation-statistics-4-lower"> </a></td><td>Lower Quartile</td><td>The lower [Quartile](https://en.wikipedia.org/wiki/Quartile) Boundary of N measurements over the stated period.</td></tr><tr><td style="white-space:nowrap">4-upper<a name="observation-statistics-4-upper"> </a></td><td>Upper Quartile</td><td>The upper [Quartile](https://en.wikipedia.org/wiki/Quartile) Boundary of N measurements over the stated period.</td></tr><tr><td style="white-space:nowrap">4-dev<a name="observation-statistics-4-dev"> </a></td><td>Quartile Deviation</td><td>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.</td></tr><tr><td style="white-space:nowrap">5-1<a name="observation-statistics-5-1"> </a></td><td>1st Quintile</td><td>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.</td></tr><tr><td style="white-space:nowrap">5-2<a name="observation-statistics-5-2"> </a></td><td>2nd Quintile</td><td>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.</td></tr><tr><td style="white-space:nowrap">5-3<a name="observation-statistics-5-3"> </a></td><td>3rd Quintile</td><td>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.</td></tr><tr><td style="white-space:nowrap">5-4<a name="observation-statistics-5-4"> </a></td><td>4th Quintile</td><td>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.</td></tr><tr><td style="white-space:nowrap">skew<a name="observation-statistics-skew"> </a></td><td>Skew</td><td>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).</td></tr><tr><td style="white-space:nowrap">kurtosis<a name="observation-statistics-kurtosis"> </a></td><td>Kurtosis</td><td>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).</td></tr><tr><td style="white-space:nowrap">regression<a name="observation-statistics-regression"> </a></td><td>Regression</td><td>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.</td></tr></table></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"/>
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<telecom>
<system value="email"/>
<value value="fhir@lists.hl7.org"/>
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<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>
IG © 2020+ HL7 International - Vocabulary Work Group. Package hl7.terminology#2.0.0 based on FHIR 4.0.1. Generated 2020-11-07
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