[{"data":1,"prerenderedAt":1677},["Reactive",2],{"options:asyncdata:$ogpPUTwkW6:/p/percentiles:0":3},{"page":4,"book":25,"news":1671,"questionSent":19,"questions":1672,"formData":1673,"attachments":22,"chartData":22,"pending":19,"chartOptions":1674,"afspec":19,"aflink":1676},{"id":5,"book_id":6,"chapter_id":7,"name":8,"slug":9,"html":10,"priority":11,"created_at":12,"updated_at":13,"created_by":14,"updated_by":18,"draft":19,"markdown":20,"revision_count":15,"template":19,"owned_by":21,"editor":20,"trends":22,"raw_html":23,"tags":24},4003,2,0,"Percentiles","percentiles","\u003Cp id=\"bkmrk-un-percentil-es-el-v\">Un \u003Cstrong>percentil\u003C/strong> es el valor de una variable estadística que deja por debajo suyo un porcentaje dado de datos de una muestra. Por ejemplo, el percentil 10, expresado P10, de las calificaciones de un grupo de alumnos, es la calificación en puntos por debajo de la cual se sitúa el 10% de los alumnos del grupo. Una definición alternativa de percentil y al porcentaje que representa incluye al propio valor del percentil, es decir, define el percentil como valor q\u003Cem>ue coincide o es inferior\u003C/em> a un porcentaje de datos datos.\u003Cbr>\u003C/p>\r\n\u003Cp id=\"bkmrk-los-percentiles-son-\">Los percentiles son un tipo de estadístico de orden perteneciente a la familia de los cuantiles; más concretamente los percentiles no son más que 100-cuantiles, por el hecho de que los percentiles P1, P2, ..., P99 dividen en 100 partes que comprenden un 1% cada una de ellas una distribución de datos. Por otra parte, hay una serie de cuantiles que coinciden conceptualmente con ciertos percentiles; por ejemplo, los \u003Ca href=\"https://ikusmira.org/p/cuartiles-primer-cuartil-segundo-cuartil-tercer-cuartil\">cuartiles\u003C/a> o 4-cuantiles coinciden con los percentiles 25, 50 y 75 respectivamente y los \u003Ca href=\"https://ikusmira.org/p/deciles\">deciles\u003C/a> o 10-cuantiles con los percentiles 10, 20, ..., 90. \u003C/p>\r\n\u003Cp id=\"bkmrk-hay-que-recalcar-que\">Hay que recalcar que los percentiles son un valor de variable que se examina y que por tanto vienen expresados en su unidad. Así, si en una muestra de 40 alumnos debe calcularse el percentil 10 de sus notas, este percentil corresponde con una nota, por ejemplo 2.7, de modo que el 10% de alumnos, es decir 4, tiene una nota inferior (o igual). Teniendo en cuenta lo anterior, deben distinguirse el percentil, el rango ordinal y el rango percentil, en el ejemplo dado, el rango ordinal del percentil sería 4, porque hay 4 datos inferiores o iguales a 2.7 y el rango percentil de 2.7 sería 10%. \u003C/p>",325,"2026-02-18T08:23:51.000000Z","2026-02-18T09:36:57.000000Z",{"id":15,"name":16,"slug":17},1,"Admin","admin",{"id":15,"name":16,"slug":17},false,"",{"id":15,"name":16,"slug":17},null,"\u003Cp id=\"bkmrk-un-percentil-es-el-v\">Un \u003Cstrong>percentil\u003C/strong> es el valor de una variable estadística que deja por debajo suyo un porcentaje dado de datos de una muestra. Por ejemplo, el percentil 10, expresado P10, de las calificaciones de un grupo de alumnos, es la calificación en puntos por debajo de la cual se sitúa el 10% de los alumnos del grupo. Una definición alternativa de percentil y al porcentaje que representa incluye al propio valor del percentil, es decir, define el percentil como valor q\u003Cem>ue coincide o es inferior\u003C/em> a un porcentaje de datos datos.\u003Cbr>\u003C/p>\r\n\u003Cp id=\"bkmrk-los-percentiles-son-\">Los percentiles son un tipo de estadístico de orden perteneciente a la familia de los cuantiles; más concretamente los percentiles no son más que 100-cuantiles, por el hecho de que los percentiles P1, P2, ..., P99 dividen en 100 partes que comprenden un 1% cada una de ellas una distribución de datos. Por otra parte, hay una serie de cuantiles que coinciden conceptualmente con ciertos percentiles; por ejemplo, los \u003Ca href=\"https://ikusmira.org/p/cuartiles-primer-cuartil-segundo-cuartil-tercer-cuartil\">cuartiles\u003C/a> o 4-cuantiles coinciden con los percentiles 25, 50 y 75 respectivamente y los \u003Ca href=\"https://ikusmira.org/p/deciles\">deciles\u003C/a> o 10-cuantiles con los percentiles 10, 20, ..., 90. \u003C/p>\r\n\u003Cp id=\"bkmrk-hay-que-recalcar-que\">Hay que recalcar que los percentiles son un valor de variable que se examina y que por tanto vienen expresados en su unidad. Así, si en una muestra de 40 alumnos debe calcularse el percentil 10 de sus notas, este percentil corresponde con una nota, por ejemplo 2.7, de modo que el 10% de alumnos, es decir 4, tiene una nota inferior (o igual). Teniendo en cuenta lo anterior, deben distinguirse el percentil, el rango ordinal y el rango percentil, en el ejemplo dado, el rango ordinal del percentil sería 4, porque hay 4 datos inferiores o iguales a 2.7 y el rango percentil de 2.7 sería 10%. \u003C/p>",[],{"id":6,"name":26,"slug":27,"description":20,"created_at":28,"updated_at":29,"created_by":15,"updated_by":15,"owned_by":15,"default_template_id":22,"pages":30,"index":61,"shelves":1664},"Estadística general","estadistica-general","2023-05-06T08:26:42.000000Z","2023-05-16T06:24:05.000000Z",[31,36,41,46,51,56],{"id":32,"name":33,"slug":34,"html":35},2897,"Distribución monomodular o equiespaciada","distribucion-monomodular-o-equiespaciada","\u003Cp id=\"bkmrk-una-distribuci%C3%B3n-de-\">Una \u003Cstrong>distribución de frecuencias \u003C/strong>se dice que es \u003Cstrong>monomodular o equiespaciada\u003C/strong> cuando todos sus \u003Cstrong>\u003Ca href=\"https://ikusmira.org/p/intervalo-de-clase\">intervalos de clase\u003C/a>\u003C/strong> tienen la misma amplitud o módulo. \u003C/p>\r\n\u003Cp id=\"bkmrk-puede-interesarte-ta\">\u003Cstrong>Puede interesarte también\u003C/strong>\u003C/p>\r\n\u003Cul id=\"bkmrk-intervalos-de-clase-\">\r\n\u003Cli class=\"null\">\u003Ca href=\"https://ikusmira.org/p/intervalos-de-clase-monomodulares-y-no-monomodulares\">\u003Cstrong>Intervalos de clase monomodulares y no monomodulares\u003C/strong>\u003C/a>\u003C/li>\r\n\u003C/ul>",{"id":37,"name":38,"slug":39,"html":40},1139,"Probabilidad empírica (probabilidad experimental, probabilidad frecuencial)","probabilidad-empirica-probabilidad-experimental","\u003Cp id=\"bkmrk-la-probabilidad-emp%C3%AD\">La \u003Cstrong>probabilidad frecuencial, probabilidad empírica o probabilidad experimental\u003C/strong> es la probabilidad de ocurrencia de un suceso entendida y calculada como la \u003Cstrong>\u003Ca href=\"https://ikusmira.org/p/frecuencia-relativa\">frecuencia relativa\u003C/a>\u003C/strong> o porcentaje de aparición de dicho suceso en una secuencia de datos tomados de la realidad. Por ejemplo, si se lanza una moneda 200 veces y ha salido cara 120 veces, la probabilidad empírica es 120/200=0.6=60%. La probabilidad empírica se considera una aproximación de la probabilidad teórica de un suceso, determinada a partir de la asunción de un modelo; por ejemplo, en el ejemplo de la moneda, la probabilidad teórica es 0.5, suponiendo que la moneda es equilibrada y que por tanto los dos lados tienen la misma probabilidad de ocurrencia. En estadística, existen varias técnicas basadas en la discrepancia entre probabilidad empírica y teórica para examinar la validez del modelo establecido en relación a un fenómeno aleatorio y sus sucesos asociados, siendo entres estas la más utilizada la prueba de chi-cuadrado. \u003Cbr>\u003C/p>\r\n\u003Cp id=\"bkmrk-puede-interesarte-ad\">\u003Cstrong>Puede interesarte además\u003C/strong>\u003C/p>\r\n\u003Cul id=\"bkmrk-frecuencia-observada\">\r\n\u003Cli class=\"null\">\u003Ca href=\"https://ikusmira.org/p/probabilidad-clasica-probabilidad-teorica\">Probabilidad clásica (probabilidad teórica)\u003C/a>\u003C/li>\r\n\u003Cli>\u003Ca href=\"https://ikusmira.org/p/frecuencia-observada-frecuencia-empirica\">Frecuencia observada (frecuencia empírica)\u003C/a>\u003C/li>\r\n\u003C/ul>",{"id":42,"name":43,"slug":44,"html":45},3063,"Característica cualitativa","caracteristica-cualitativa","\u003Cp id=\"bkmrk-una-caracter%C3%ADstica-c\">Una \u003Cstrong>característica cualitativa\u003C/strong> es una característica o rasgo de los elementos de una población que puede medirse a través de un número, mediante una \u003Ca href=\"https://ikusmira.org/p/escala-de-intervalo\">escala de intervalo\u003C/a> o una \u003Ca href=\"https://ikusmira.org/p/escala-de-razon-escala-de-proporcion-escala-de-cociente-escala-de-ratio/\">escala de razón\u003C/a>, dando lugar a una \u003Cstrong>\u003Ca href=\"https://ikusmira.org/p/variable-cuantitativa/\">variable estadística cuantitativa\u003C/a>\u003C/strong>. \u003C/p>\r\n\u003Cp id=\"bkmrk-%C2%A0\"> \u003C/p>",{"id":47,"name":48,"slug":49,"html":50},2172,"Individuo (unidad estadística)","individuo-unidad-estadistica","\u003Cp id=\"bkmrk-un-individuo-o-unida\">Un \u003Cstrong>individuo o unidad estadística\u003C/strong> es cada uno de los elementos que forma una población estadística. A pesar que el término individuo se refiere en su acepción habitual a personas individuales, en estadística un individuo puede referirse a cualquier ser vivo, vegetal o animal. Los individuos o unidades estadísticas de una población pueden ser estudiados uno a uno sin excepción, en el caso de los censos, o mñas generalmente seleccionadas al azar de la población como muestra estadística representativa. De una forma u otra, los individuos son observados y por tanto generan datos, siendo por tanto a la vez, en el contexto de la investigación científica,  \u003Ca href=\"https://ikusmira.org/p/unidad-de-observacion\">unidades de observación\u003C/a>.\u003C/p>",{"id":52,"name":53,"slug":54,"html":55},3732,"Mapa de calor de correlaciones (correlograma)","mapa-de-calor-de-correlaciones-correlograma","\u003Cp id=\"bkmrk-\">\u003Ca href=\"https://es.gizapedia.org/uploads/images/gallery/2024-02/06RQrppGSVQIO9Vj-carmilagedata2.png\" target=\"_blank\" rel=\"noopener\">\u003Cimg src=\"https://es.gizapedia.org/uploads/images/gallery/2024-02/scaled-1680-/06RQrppGSVQIO9Vj-carmilagedata2.png\" alt=\"CarMilageData(2).png\" width=\"335\" height=\"335\">\u003C/a>\u003C/p>\r\n\u003Cp id=\"bkmrk-imagen%3A-correlograma\">\u003Cem>Imagen: Correlograma que muestra las correlaciones mutuas entre 11 variables. Créditos: Jackverr-Commons.\u003C/em>\u003C/p>\r\n\u003Cp id=\"bkmrk-un%C2%A0correlograma-es-u\">En análisis multivariante, un \u003Cstrong>mapa de calor de correlaciones, también llamado a veces correlograma,\u003C/strong>  es una representación gráfica de los coeficientes de correlación entre los pares de variables de un grupo de variables.\u003C/p>\r\n\u003Cp id=\"bkmrk-puede-interesarte-ta\">\u003Cstrong>Puede interesarte también\u003C/strong>\u003C/p>\r\n\u003Cul id=\"bkmrk-correlograma%2C-en-la-\">\r\n\u003Cli class=\"null\">\u003Cstrong>\u003Ca href=\"https://ikusmira.org/p/correlograma\">Correlograma\u003C/a>, en la acepción de autocorrelograma\u003C/strong>\u003C/li>\r\n\u003C/ul>",{"id":57,"name":58,"slug":59,"html":60},2046,"Frecuencia acumulada relativa","frecuencia-acumulada-relativa","\u003Cp id=\"bkmrk-para-una-variable-es\">Para una \u003Ca href=\"https://ikusmira.org/p/variables-estadisticas\">\u003Cstrong>variable estadística cuantitativa\u003C/strong>\u003C/a>, la \u003Cstrong>frecuencia acumulada relativa, frecuencia relativa acumulada, frecuencia porcentual acumulada\u003C/strong>  \u003Cstrong>o porcentaje acumulado \u003C/strong>es el porcentaje de elementos sobre el tamaño de  la muestra inferior o igual a un valor dado de una variable estadística cuantitativa. Por ejemplo, para datos de calificaciones de alumnos entre 0 y 10, la frecuencia relativa acumulada del 40% para el valor 7 indica que el porcentaje de alumnos con calificación de 7 o inferior es del 40%.\u003C/p>\r\n\u003Cp id=\"bkmrk-las-frecuencias-rela\">Las frecuencias relativas acumuladas pueden determinarse simplemente acumulando las \u003Cstrong>\u003Ca href=\"https://ikusmira.org/p/frecuencia-relativa\">frecuencias relativas simples\u003C/a> \u003C/strong>para los valores ordenados de la variable en cuestión.\u003C/p>\r\n\u003Cp id=\"bkmrk-%C2%A0la-notaci%C3%B3n-habitua\"> La notación habitual para la frecuencia absoluta acumulada es F (efe mayúscula). \u003Cbr>\u003C/p>\r\n\u003Cp id=\"bkmrk-puede-interesarte-ta\">\u003Cstrong>Puede interesarte también\u003C/strong>\u003C/p>\r\n\u003Cul id=\"bkmrk-frecuencia-absoluta-\">\r\n\u003Cli class=\"null\">\u003Ca href=\"https://ikusmira.org/p/frecuencia-acumulada\">\u003Cstrong>Frecuencias acumuladas\u003C/strong>\u003C/a>\u003C/li>\r\n\u003Cli class=\"null\">\u003Ca href=\"https://ikusmira.org/p/frecuencia-acumulada-absoluta\">\u003Cstrong>Frecuencia acumulada absoluta\u003C/strong>\u003C/a>\u003C/li>\r\n\u003C/ul>",{"":62},[63,67,71,75,80,85,90,95,100,105,107,112,117,122,127,132,137,142,147,152,157,162,167,172,177,182,187,192,197,202,207,212,217,222,227,232,236,241,245,250,255,260,265,270,274,278,283,288,293,298,302,307,312,317,322,327,332,337,342,347,352,357,362,367,372,377,382,387,392,397,402,407,409,414,418,423,428,433,438,443,448,453,458,462,467,472,476,481,486,491,496,501,506,511,516,521,525,530,535,540,545,550,555,560,565,570,575,580,585,587,592,597,602,607,612,617,622,627,632,637,642,647,652,657,662,667,672,677,682,687,692,697,702,707,709,714,719,724,729,734,739,744,749,753,757,762,767,771,775,779,784,789,794,799,803,808,813,818,823,828,833,838,842,847,852,857,862,867,872,877,882,887,892,896,901,906,911,916,921,926,931,936,941,945,950,954,959,964,969,974,979,983,988,993,998,1003,1007,1012,1017,1021,1026,1031,1036,1040,1045,1050,1055,1060,1064,1069,1074,1079,1081,1086,1091,1096,1101,1106,1110,1115,1120,1125,1129,1134,1139,1144,1148,1152,1157,1162,1167,1172,1177,1181,1185,1190,1195,1200,1205,1209,1214,1219,1222,1226,1231,1236,1240,1245,1250,1255,1260,1265,1270,1275,1280,1285,1290,1295,1300,1304,1309,1314,1319,1324,1329,1334,1339,1344,1349,1354,1359,1364,1369,1374,1379,1382,1386,1391,1396,1401,1405,1409,1413,1418,1422,1426,1431,1436,1441,1446,1451,1456,1461,1466,1471,1476,1481,1486,1491,1495,1500,1505,1509,1514,1519,1524,1526,1530,1534,1538,1543,1548,1553,1558,1563,1568,1573,1578,1583,1588,1593,1598,1603,1608,1613,1614,1619,1624,1629,1634,1639,1644,1649,1654,1659],{"id":64,"name":65,"slug":66,"priority":7,"chapter_name":22},3173,"Aleatorización (diseño de experimentos)","aleatorizacion-diseno-de-experimentos",{"id":68,"name":69,"slug":70,"priority":15,"chapter_name":22},631,"Amplitud de clase","amplitud-de-clase",{"id":72,"name":73,"slug":74,"priority":6,"chapter_name":22},387,"Análisis de trayectorias","analisis-de-trayectorias",{"id":76,"name":77,"slug":78,"priority":79,"chapter_name":22},624,"Arranque aleatorio","arranque-aleatorio",3,{"id":81,"name":82,"slug":83,"priority":84,"chapter_name":22},43,"Asociación estadística","asociacion-estadistica",4,{"id":86,"name":87,"slug":88,"priority":89,"chapter_name":22},2019,"Asociación no estadística","asociacion-no-estadistica",5,{"id":91,"name":92,"slug":93,"priority":94,"chapter_name":22},2948,"Banco de datos","banco-de-datos",6,{"id":96,"name":97,"slug":98,"priority":99,"chapter_name":22},1621,"Base del índice (periodo base)","base-del-indice-periodo-base",7,{"id":101,"name":102,"slug":103,"priority":104,"chapter_name":22},35,"Cálculo de la moda estadística para datos agrupados en intervalos","calculo-de-la-moda-estadistica-para-datos-agrupados-en-intervalos",8,{"id":42,"name":43,"slug":44,"priority":106,"chapter_name":22},9,{"id":108,"name":109,"slug":110,"priority":111,"chapter_name":22},2708,"Casos particulares","casos-particulares",10,{"id":113,"name":114,"slug":115,"priority":116,"chapter_name":22},2730,"Censo estadístico","censo-estadistico",11,{"id":118,"name":119,"slug":120,"priority":121,"chapter_name":22},2551,"Clase mediana","clase-mediana",12,{"id":123,"name":124,"slug":125,"priority":126,"chapter_name":22},1011,"Clase modal","clase-modal",13,{"id":128,"name":129,"slug":130,"priority":131,"chapter_name":22},2133,"Coeficiente de asimetría de Bowley","coeficiente-de-asimetria-de-bowley",14,{"id":133,"name":134,"slug":135,"priority":136,"chapter_name":22},1714,"Coeficiente de asimetría de Fisher","coeficiente-de-asimetria-de-fisher",15,{"id":138,"name":139,"slug":140,"priority":141,"chapter_name":22},1844,"Coeficiente de asimetría de Pearson","coeficiente-de-asimetria-de-pearson",16,{"id":143,"name":144,"slug":145,"priority":146,"chapter_name":22},2057,"Coeficiente de contingencia de Pearson","coeficiente-de-contingencia-de-pearson",17,{"id":148,"name":149,"slug":150,"priority":151,"chapter_name":22},2648,"Coeficiente de correlación biserial puntual","coeficiente-de-correlacion-biserial-puntual",18,{"id":153,"name":154,"slug":155,"priority":156,"chapter_name":22},1938,"Coeficiente de curtosis de Pearson","coeficiente-de-curtosis-de-pearson",19,{"id":158,"name":159,"slug":160,"priority":161,"chapter_name":22},2032,"Coeficiente de determinación ajustado (coeficiente de determinación corregido)","coeficiente-de-determinacion-ajustado-coeficiente-de-determinacion-corregido",20,{"id":163,"name":164,"slug":165,"priority":166,"chapter_name":22},2649,"Coeficiente de Tschuprow","coeficiente-de-tschuprow",21,{"id":168,"name":169,"slug":170,"priority":171,"chapter_name":22},37,"Coeficiente de variación","coeficiente-de-variacion",22,{"id":173,"name":174,"slug":175,"priority":176,"chapter_name":22},2646,"Coeficiente Q de Yule","coeficiente-q-de-yule",23,{"id":178,"name":179,"slug":180,"priority":181,"chapter_name":22},2218,"Comprobación de Charlier","comprobacion-de-charlier",24,{"id":183,"name":184,"slug":185,"priority":186,"chapter_name":22},2095,"Concepto de estadística","concepto-de-estadistica",25,{"id":188,"name":189,"slug":190,"priority":191,"chapter_name":22},2607,"Constante estadística","constante-estadistica",26,{"id":193,"name":194,"slug":195,"priority":196,"chapter_name":22},2087,"Corrección de Bessel","correccion-de-bessel",27,{"id":198,"name":199,"slug":200,"priority":201,"chapter_name":22},308,"Corrección de Sheppard","correccion-de-sheppard",28,{"id":203,"name":204,"slug":205,"priority":206,"chapter_name":22},1080,"Corrección de Yates","correccion-de-yates",29,{"id":208,"name":209,"slug":210,"priority":211,"chapter_name":22},49,"Corrección por continuidad","correccion-por-continuidad",30,{"id":213,"name":214,"slug":215,"priority":216,"chapter_name":22},2685,"Correlación","correlacion",31,{"id":218,"name":219,"slug":220,"priority":221,"chapter_name":22},1624,"Correlación espuria (correlación espúrea)","correlacion-espuria-correlacion-espurea",32,{"id":223,"name":224,"slug":225,"priority":226,"chapter_name":22},2684,"Correlación por rangos","correlacion-por-rangos",33,{"id":228,"name":229,"slug":230,"priority":231,"chapter_name":22},1665,"Correlograma","correlograma",34,{"id":233,"name":234,"slug":235,"priority":101,"chapter_name":22},148,"Covariación","covariacion",{"id":237,"name":238,"slug":239,"priority":240,"chapter_name":22},44,"Covarianza","covarianza",36,{"id":242,"name":243,"slug":244,"priority":168,"chapter_name":22},1652,"Criterio (variable)","criterio-variable",{"id":246,"name":247,"slug":248,"priority":249,"chapter_name":22},2135,"Cuartiles (primer cuartil, segundo cuartil, tercer cuartil)","cuartiles-primer-cuartil-segundo-cuartil-tercer-cuartil",38,{"id":251,"name":252,"slug":253,"priority":254,"chapter_name":22},2159,"Cuasivarianza (varianza corregida)","cuasivarianza-varianza-corregida",39,{"id":256,"name":257,"slug":258,"priority":259,"chapter_name":22},1669,"Curtosis (estadística)","curtosis-estadistica",40,{"id":261,"name":262,"slug":263,"priority":264,"chapter_name":22},2072,"Datos agregados","datos-agregados",41,{"id":266,"name":267,"slug":268,"priority":269,"chapter_name":22},1732,"Datos agrupados","datos-agrupados",42,{"id":271,"name":272,"slug":273,"priority":81,"chapter_name":22},2692,"Datos aislados (datos no agrupados)","datos-aislados-datos-no-agrupados",{"id":275,"name":276,"slug":277,"priority":237,"chapter_name":22},2745,"Datos bivariados","datos-bivariados",{"id":279,"name":280,"slug":281,"priority":282,"chapter_name":22},73,"Datos blandos","datos-blandos",45,{"id":284,"name":285,"slug":286,"priority":287,"chapter_name":22},2315,"Datos 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