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Definition for Code System StatisticsCode
<?xml version="1.0" encoding="UTF-8"?> <CodeSystem xmlns="http://hl7.org/fhir"> <id value="observation-statistics"/> <meta> <lastUpdated value="2020-08-20T17:41:31.970+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="4.5.0"/> <name value="StatisticsCode"/> <title value="StatisticsCode"/> <status value="draft"/> <experimental value="false"/> <date value="2020-08-20T17:41:31+10:00"/> <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://hl7.org/fhir/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>
Usage note: every effort has been made to ensure that the examples are correct and useful, but they are not a normative part of the specification.