{"id":164358,"date":"2022-06-01T09:42:00","date_gmt":"2022-06-01T08:42:00","guid":{"rendered":"https:\/\/datascientest.com\/?p=164358"},"modified":"2023-11-09T14:04:10","modified_gmt":"2023-11-09T14:04:10","slug":"coefficient-de-determination","status":"publish","type":"post","link":"https:\/\/datascientest.com\/coefficient-de-determination","title":{"rendered":"Coefficient de d\u00e9termination : qu\u2019est ce que c\u2019est et comment s\u2019en servir ?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"164358\" class=\"elementor elementor-164358\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-5db95c4 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5db95c4\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-b11b6b2\" data-id=\"b11b6b2\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d2327cc elementor-widget elementor-widget-heading\" data-id=\"d2327cc\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">Calcul\u00e9 par les logiciels de statistiques, le coefficient de d\u00e9termination permet de comprendre combien de variables d\u2019un facteur peuvent \u00eatre expliqu\u00e9es par sa relation avec un autre facteur.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-f8267e0 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f8267e0\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-cbc293f\" data-id=\"cbc293f\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-018c867 elementor-widget elementor-widget-heading\" data-id=\"018c867\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">D\u00e9finition du coefficient de d\u00e9termination<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d046720 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d046720\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d244000\" data-id=\"d244000\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7344d14 elementor-widget elementor-widget-text-editor\" data-id=\"7344d14\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p>Dans une <a href=\"https:\/\/datascientest.com\/\/regression-logistique-quest-ce-que-cest\">r\u00e9gression lin\u00e9aire<\/a>, l\u2019objectif est de d\u00e9terminer une relation lin\u00e9aire entre deux jeux de donn\u00e9es. Lorsque l\u2019on parle de coefficient de d\u00e9termination, \u00e9galement appel\u00e9 \u00ab R carr\u00e9 \u00bb, celui-ci intervient lorsque l\u2019on cherche, dans cette r\u00e9gression lin\u00e9aire, la force d\u2019ad\u00e9quation entre le mod\u00e8le de cette r\u00e9gression et les donn\u00e9es collect\u00e9es. On parle alors de \u00ab qualit\u00e9 d\u2019ajustement \u00bb.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-4051f4f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4051f4f\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-269a1da\" data-id=\"269a1da\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f76ab92 elementor-widget elementor-widget-heading\" data-id=\"f76ab92\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Pourquoi se servir du coefficient de d\u00e9termination ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-f97d1b1 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f97d1b1\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-a6060fa\" data-id=\"a6060fa\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5d9afa9 elementor-widget elementor-widget-text-editor\" data-id=\"5d9afa9\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Concr\u00e8tement, le coefficient de d\u00e9termination est un indice de la qualit\u00e9 de la pr\u00e9diction de la r\u00e9gression lin\u00e9aire. Le coefficient de d\u00e9termination se situe entre 0 et 1. Plus il est proche de 1, plus la r\u00e9gression lin\u00e9aire est en ad\u00e9quation avec les donn\u00e9es collect\u00e9es. 1 est \u00e9gal \u00e0 100% donc dans ce cas, la corr\u00e9lation entre les variables est totale. \u00c0 l\u2019inverse, si l\u2019indice est proche de z\u00e9ro, il signale la quasi-absence de donn\u00e9es corr\u00e9l\u00e9es. Le coefficient de d\u00e9termination est utile pour pr\u00e9voir des \u00e9v\u00e9nements futurs en fonction de la probabilit\u00e9 que fournit le r\u00e9sultat de son calcul. Il est donc n\u00e9cessaire d\u2019avoir le plus de donn\u00e9es possibles afin que le r\u00e9sultat soit le plus pr\u00e9cis possible.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-dd1bcc7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"dd1bcc7\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-671cb28\" data-id=\"671cb28\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4600487 elementor-widget elementor-widget-heading\" data-id=\"4600487\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Comment calculer le coefficient de d\u00e9termination ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-66faab1 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"66faab1\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-66955ab\" data-id=\"66955ab\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c0a2f0c elementor-widget elementor-widget-text-editor\" data-id=\"c0a2f0c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Le coefficient de d\u00e9termination est \u00e9gal au coefficient de corr\u00e9lation (R) au carr\u00e9. Le <a href=\"https:\/\/datascientest.com\/correlation-entre-variables-comment-mesurer-la-dependance\">coefficient de corr\u00e9lation<\/a> mesure la force de la relation entre deux variables : la variable d\u00e9pendante (x) et la variable pr\u00e9dictive (y). Cette force est comprise entre -1 et 1. Ainsi, en ayant R, il est possible de calculer R\u00b2. En revanche, ce calcul ne permet pas de d\u00e9terminer l\u2019effet qui a men\u00e9 \u00e0 l\u2019ad\u00e9quation ou non des donn\u00e9es avec la r\u00e9gression lin\u00e9aire. Une autre m\u00e9thode se fonde sur la qualit\u00e9 des donn\u00e9es ; parmi la totalit\u00e9 des donn\u00e9es enregistr\u00e9es (TSS), on compte celles qui ne constituent que des variantes r\u00e9siduelles (RSS). Le calcul suivant permet donc de trouver un coefficient de d\u00e9termination plus adapt\u00e9 et pr\u00e9cis :\u00a0<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-0aeca16 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0aeca16\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-9c787cf\" data-id=\"9c787cf\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-af871a5 elementor-widget elementor-widget-text-editor\" data-id=\"af871a5\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><strong>R\u00b2 = 1 \u2013 RSS \/ TSS<\/strong><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-884bd8c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"884bd8c\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-e9de998\" data-id=\"e9de998\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2f97cf9 elementor-widget elementor-widget-heading\" data-id=\"2f97cf9\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">R\u00b2 ou R\u00b2 ajust\u00e9 ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d602472 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d602472\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d951056\" data-id=\"d951056\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-8a21c3b elementor-widget elementor-widget-text-editor\" data-id=\"8a21c3b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>La limite du coefficient de d\u00e9termination r\u00e9side dans l\u2019ajout de variables \u00e0 une r\u00e9gression lin\u00e9aire. Lorsque l\u2019on en ajoute un trop grand nombre, cela tend \u00e0 augmenter la valeur de R\u00b2, de mani\u00e8re injustifi\u00e9e. Dans ce cas, il est utile de se r\u00e9f\u00e9rer au \u00ab R\u00b2 ajust\u00e9 \u00bb qui va d\u00e9terminer la fiabilit\u00e9 de la corr\u00e9lation et si elle est d\u00e9termin\u00e9e par l\u2019ajout de variables.\u00a0<\/p><p>De mani\u00e8re g\u00e9n\u00e9rale, le coefficient de d\u00e9termination est un bon outil pour estimer le lien entre la r\u00e9gression lin\u00e9aire et les variables. Mais son utilisation reste limit\u00e9e puisqu\u2019il ne mesure que partiellement l\u2019utilit\u00e9 d\u2019une r\u00e9gression lin\u00e9aire et l\u2019ajustement des points avec le mod\u00e8le de la r\u00e9gression.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-30c126a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"30c126a\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-01dd558\" data-id=\"01dd558\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1a84308 elementor-widget elementor-widget-text-editor\" data-id=\"1a84308\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Vous savez maintenant tout sur le coefficient de d\u00e9termination. DataScientest vous offre la possibilit\u00e9 d\u2019aller plus loin en apprenant \u00e0 ma\u00eetriser la gestion d\u2019un projet data de A \u00e0 Z. D\u00e9couvrez <a href=\"https:\/\/datascientest.com\/#formations\">nos formations<\/a>, n\u2019h\u00e9sitez plus !<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-7d71fdf elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7d71fdf\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-c3b45bf\" data-id=\"c3b45bf\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7149fb7 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"7149fb7\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/formations-data\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">D\u00e9couvrez les formations de DataScientest<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t<div class=\"elementor-element elementor-element-4aa64db e-flex e-con-boxed e-con\" data-id=\"4aa64db\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-aa2a71b elementor-widget elementor-widget-html\" data-id=\"aa2a71b\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [{\n    \"@type\": \"Question\",\n    \"name\": \"D\u00e9finition du coefficient de d\u00e9termination\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Dans une r\u00e9gression lin\u00e9aire, l\u2019objectif est de d\u00e9terminer une relation lin\u00e9aire entre deux jeux de donn\u00e9es. Lorsque l\u2019on parle de coefficient de d\u00e9termination, \u00e9galement appel\u00e9 \u00ab R carr\u00e9 \u00bb, celui-ci intervient lorsque l\u2019on cherche, dans cette r\u00e9gression lin\u00e9aire, la force d\u2019ad\u00e9quation entre le mod\u00e8le de cette r\u00e9gression et les donn\u00e9es collect\u00e9es. On parle alors de \u00ab qualit\u00e9 d\u2019ajustement \u00bb.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"Pourquoi se servir du coefficient de d\u00e9termination ?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Concr\u00e8tement, le coefficient de d\u00e9termination est un indice de la qualit\u00e9 de la pr\u00e9diction de la r\u00e9gression lin\u00e9aire. Le coefficient de d\u00e9termination se situe entre 0 et 1. Plus il est proche de 1, plus la r\u00e9gression lin\u00e9aire est en ad\u00e9quation avec les donn\u00e9es collect\u00e9es. 1 est \u00e9gal \u00e0 100% donc dans ce cas, la corr\u00e9lation entre les variables est totale. \u00c0 l\u2019inverse, si l\u2019indice est proche de z\u00e9ro, il signale la quasi-absence de donn\u00e9es corr\u00e9l\u00e9es. Le coefficient de d\u00e9termination est utile pour pr\u00e9voir des \u00e9v\u00e9nements futurs en fonction de la probabilit\u00e9 que fournit le r\u00e9sultat de son calcul. Il est donc n\u00e9cessaire d\u2019avoir le plus de donn\u00e9es possibles afin que le r\u00e9sultat soit le plus pr\u00e9cis possible.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"Comment calculer le coefficient de d\u00e9termination ?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Le coefficient de d\u00e9termination est \u00e9gal au coefficient de corr\u00e9lation (R) au carr\u00e9. Le coefficient de corr\u00e9lation mesure la force de la relation entre deux variables : la variable d\u00e9pendante (x) et la variable pr\u00e9dictive (y). Cette force est comprise entre -1 et 1. Ainsi, en ayant R, il est possible de calculer R\u00b2. En revanche, ce calcul ne permet pas de d\u00e9terminer l\u2019effet qui a men\u00e9 \u00e0 l\u2019ad\u00e9quation ou non des donn\u00e9es avec la r\u00e9gression lin\u00e9aire. Une autre m\u00e9thode se fonde sur la qualit\u00e9 des donn\u00e9es ; parmi la totalit\u00e9 des donn\u00e9es enregistr\u00e9es (TSS), on compte celles qui ne constituent que des variantes r\u00e9siduelles (RSS). Le calcul suivant permet donc de trouver un coefficient de d\u00e9termination plus adapt\u00e9 et pr\u00e9cis :\"\n    }\n  }]\n}\n<\/script>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Calcul\u00e9 par les logiciels de statistiques, le coefficient de d\u00e9termination permet de comprendre combien de variables d\u2019un facteur peuvent \u00eatre expliqu\u00e9es par sa relation avec un autre facteur. D\u00e9finition du coefficient de d\u00e9termination Dans une r\u00e9gression lin\u00e9aire, l\u2019objectif est de d\u00e9terminer une relation lin\u00e9aire entre deux jeux de donn\u00e9es. Lorsque l\u2019on parle de coefficient de [&hellip;]<\/p>\n","protected":false},"author":77,"featured_media":165055,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2497],"tags":[164],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Coefficient de d\u00e9termination : qu\u2019est ce que c\u2019est et comment s\u2019en servir ?<\/title>\n<meta name=\"description\" content=\"Calcul\u00e9 par les logiciels de statistiques, le coefficient de d\u00e9termination permet de comprendre combien de variables d\u2019un facteur peuvent \u00eatre expliqu\u00e9es par sa relation avec un autre facteur.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/coefficient-de-determination\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Coefficient de d\u00e9termination : qu\u2019est ce que c\u2019est et comment s\u2019en servir ?\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/coefficient-de-determination\" \/>\n<meta property=\"og:site_name\" content=\"Formation Data Science | DataScientest.com\" \/>\n<meta property=\"article:published_time\" content=\"2022-06-01T08:42:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-11-09T14:04:10+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/06\/Coefficient-de-determination.jpeg\" \/>\n\t<meta property=\"og:image:width\" content=\"1520\" \/>\n\t<meta property=\"og:image:height\" content=\"600\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Raphael Kassel\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u00c9crit par\" \/>\n\t<meta name=\"twitter:data1\" content=\"Raphael Kassel\" \/>\n\t<meta name=\"twitter:label2\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data2\" content=\"3 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/datascientest.com\/coefficient-de-determination\",\"url\":\"https:\/\/datascientest.com\/coefficient-de-determination\",\"name\":\"Coefficient de d\u00e9termination : qu\u2019est ce que c\u2019est et comment s\u2019en servir ?\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/#website\"},\"datePublished\":\"2022-06-01T08:42:00+00:00\",\"dateModified\":\"2023-11-09T14:04:10+00:00\",\"author\":{\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/6eca16f63daaafcbc96e15549b9d4ee1\"},\"description\":\"Calcul\u00e9 par les logiciels de statistiques, le coefficient de d\u00e9termination permet de comprendre combien de variables d\u2019un facteur peuvent \u00eatre expliqu\u00e9es par sa relation avec un autre facteur.\",\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/datascientest.com\/coefficient-de-determination\"]}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/datascientest.com\/#website\",\"url\":\"https:\/\/datascientest.com\/\",\"name\":\"Formation Data Science | DataScientest.com\",\"description\":\"Leader de la formation en Data Science\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/datascientest.com\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"fr-FR\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/6eca16f63daaafcbc96e15549b9d4ee1\",\"name\":\"Raphael Kassel\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/11\/raph.jpeg\",\"contentUrl\":\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/11\/raph.jpeg\",\"caption\":\"Raphael Kassel\"},\"description\":\"Raphael Kassel est le Head of Data Programs chez DataScientest. 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