{"id":1197,"date":"2024-09-05T23:05:36","date_gmt":"2024-09-05T21:05:36","guid":{"rendered":"https:\/\/site.ellistat.com\/?p=1197"},"modified":"2024-10-23T15:16:29","modified_gmt":"2024-10-23T13:16:29","slug":"pruebas-de-valores-atipicos","status":"publish","type":"post","link":"https:\/\/clooma.ai\/es\/pruebas-de-valores-atipicos\/","title":{"rendered":"Pruebas de valores at\u00edpicos"},"content":{"rendered":"<p>Al analizar una serie de datos, a veces nos encontramos con valores que no parecen formar parte de la distribuci\u00f3n normal de los datos. Estos puntos se conocen como valores at\u00edpicos y, como es habitual, no siempre debes confiar en tu intuici\u00f3n para poder detectar si un valor es un valor at\u00edpico o no. Existen pruebas que pueden ponerlos de manifiesto y software estad\u00edstico como&nbsp;<a href=\"https:\/\/clooma.ai\/es\/soluciones-de-analisis-de-datos\/\">Ellistat<\/a>&nbsp;para ayudarte con los c\u00e1lculos.<\/p>\n\n\n\n<p>Desde un punto de vista estad\u00edstico, un valor at\u00edpico es un valor que no pertenece a la distribuci\u00f3n normal de los datos. Puede proceder de :<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Un error de medici\u00f3n o de copia (olvido del punto decimal)<\/li>\n\n\n\n<li>Una causa especial, como que una pieza no se haya lavado antes de medirla.<\/li>\n<\/ul>\n\n\n\n<p>Todos los c\u00e1lculos estad\u00edsticos que utilizan las propiedades de la distribuci\u00f3n normal (pruebas estad\u00edsticas, c\u00e1lculos de capacidad, c\u00e1lculos % fuera de tolerancia) son muy sensibles a la presencia de valores at\u00edpicos, por lo que es importante comprender su origen y eliminarlos antes de utilizar estos c\u00e1lculos. Tambi\u00e9n pueden utilizarse pruebas estad\u00edsticas no param\u00e9tricas, mucho menos sensibles a los valores at\u00edpicos.<\/p>\n\n\n\n<p>Se utilizan dos pruebas principales:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/fr.wikipedia.org\/wiki\/Test_Q\">Prueba de Dixon<\/a>\u00a0muy interesante cuando el n\u00famero de datos es bajo (&lt;30)<\/li>\n\n\n\n<li><a href=\"https:\/\/fr.wikipedia.org\/wiki\/Test_de_Grubbs\">Prueba Grubbs<\/a>\u00a0puede utilizarse en todos los casos.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Prueba de Dixon<\/h2>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img decoding=\"async\" src=\"https:\/\/clooma.ai\/wp-content\/uploads\/2024\/09\/Test-de-Dixon.png\" alt=\"\" class=\"wp-image-1199\"\/><\/figure><\/div>\n\n\n<p>Para utilizar la prueba de Dixon, calcule el cociente :<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>b = Alcance global de las mediciones (aqu\u00ed 14.1)<\/li>\n\n\n\n<li>a = Distancia entre la pieza sospechosa de ser un valor at\u00edpico y su vecino m\u00e1s pr\u00f3ximo (aqu\u00ed 8,6).<\/li>\n<\/ul>\n\n\n\n<p>La proporci\u00f3n se calcula en %.<\/p>\n\n\n\n<p>A continuaci\u00f3n, se compara este informe con la tabla de Dixon:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>&nbsp;<\/th><th>&nbsp;<\/th><th>&nbsp;<\/th><th>&nbsp;<\/th><th>&nbsp;<\/th><th>&nbsp;<\/th><th>&nbsp;<\/th><\/tr><\/thead><tbody><tr><td>N\u00famero de piezas<\/td><td>3<\/td><td>5<\/td><td>10<\/td><td>16<\/td><td>20<\/td><td>30<\/td><\/tr><tr><td>Coeficiente m\u00e1ximo<\/td><td>0.94<\/td><td>0.72<\/td><td>0.46<\/td><td>0.38<\/td><td>0.34<\/td><td>0.30<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>Si el valor es inferior a la relaci\u00f3n m\u00e1xima sugerida por la tabla, entonces el valor no es un valor at\u00edpico. En este caso, la proporci\u00f3n de 62% para 5 piezas es inferior a 72%. Por lo tanto, no se trata de un valor at\u00edpico.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Prueba Grubb<\/h3>\n\n\n\n<p>Para utilizar la prueba de Grubb, primero calculamos :<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>X: La media de todas las mediciones<\/li>\n\n\n\n<li>S: Desviaci\u00f3n t\u00edpica de todas las mediciones<\/li>\n\n\n\n<li>G: Distancia entre el valor sospechoso de ser un valor at\u00edpico y la media G.<\/li>\n<\/ul>\n\n\n\n<p class=\"has-text-align-center\"><span class=\"wp-katex-eq\" data-display=\"false\">G=\\frac{(Valor - X)}{S}<\/span><\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img decoding=\"async\" src=\"https:\/\/clooma.ai\/wp-content\/uploads\/2024\/09\/Test-de-Grubb.png\" alt=\"\" class=\"wp-image-1198\"\/><\/figure><\/div>\n\n\n<p>A continuaci\u00f3n, el valor de G obtenido se compara con un l\u00edmite G :<\/p>\n\n\n\n<p class=\"has-text-align-center\"><span class=\"wp-katex-eq\" data-display=\"false\">G_{limite}=\\frac{N-1}{\\sqrt{N}}.\\sqrt{\\frac{t^2_{\\frac{a}{N},N-2}{}}{N-2 +t^2{}_{\\frac{a}{n}}{,} N-2}}<\/span><\/p>\n\n\n\n<p>Si el l\u00edmite es G&gt;G, el valor se considera at\u00edpico y viceversa.<\/p>","protected":false},"excerpt":{"rendered":"<p>Al analizar una serie de datos, a veces nos encontramos con valores que no parecen formar parte de la distribuci\u00f3n normal de los datos. Estos puntos se conocen como valores at\u00edpicos y, como es habitual, no siempre debes confiar en tu intuici\u00f3n para poder detectar si un valor [...]<\/p>","protected":false},"author":2,"featured_media":1202,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[7],"tags":[],"class_list":["post-1197","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-analysis"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.9 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Tests de valeurs aberrantes - Clooma<\/title>\n<meta name=\"description\" content=\"Lorsque l&#039;on trouve des valeurs aberrantes, il ne faut pas se fier \u00e0 son intuition. 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