{"id":5894,"date":"2023-09-08T15:29:09","date_gmt":"2023-09-08T13:29:09","guid":{"rendered":"https:\/\/francestat.com\/?page_id=5894"},"modified":"2026-06-01T17:29:13","modified_gmt":"2026-06-01T15:29:13","slug":"uniwin-adq","status":"publish","type":"page","link":"https:\/\/francestat.com\/index.php\/uniwin-adq\/","title":{"rendered":"Uniwin – ADQ"},"content":{"rendered":"

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<\/div>

UNIWIN - Analyse Discriminante Qualitative<\/h2><\/div><\/div>[\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text css=\u00a0\u00bb\u00a0\u00bb]L’Analyse Discriminante Qualitative (ADQ) est une g\u00e9n\u00e9ralisation de l\u2019Analyse Factorielle Discriminante (AFD) dans le cas o\u00f9 les variables explicatives sont qualitatives et non plus quantitatives.<\/p>\n

La premi\u00e8re \u00e9tape de l\u2019analyse consiste \u00e0 mettre en \u0153uvre une Analyse des Correspondances Multiples (ACM) des variables qualitatives.<\/p>\n

La deuxi\u00e8me \u00e9tape remplace les variables qualitatives d\u2019origine par les coordonn\u00e9es sur les axes factoriels issus de l\u2019ACM et effectue sur ces donn\u00e9es une Analyse Factorielle Discriminante (AFD).<\/p>\n

Les fonctions discriminantes sont ensuite exprim\u00e9es en fonction des indicatrices des modalit\u00e9s des variables qualitatives d\u2019origine.<\/p>\n

La m\u00e9thode r\u00e9alise d’abord l’analyse sur une population d’apprentissage, puis sur une population de validation et enfin sur une population de pr\u00e9vision.<\/p>\n

En fonction des donn\u00e9es et des param\u00e8tres d\u00e9finis par l\u2019utilisateur, l\u2019analyse ADB r\u00e9alise automatiquement les \u00e9tudes de la population d\u2019apprentissage et des \u00e9ventuelles populations de validation et de pr\u00e9vision.<\/p>\n

De fa\u00e7on plus pr\u00e9cise, la m\u00e9thode peut se d\u00e9composer en trois \u00e9tapes. Supposons une population de n individus. D\u00e9coupons cette population en trois sous-populations de tailles n1<\/sub>, n2<\/sub> et n3<\/sub> avec n1<\/sub> + n2 +<\/sub> n3<\/sub> = n. Les trois \u00e9tapes sont :<\/p>\n

    \n
  • une \u00e9tude initiale sur la population d\u2019apprentissage de taille n1<\/sub><\/li>\n
  • une \u00e9tude de validation sur la population de validation de taille n2<\/sub><\/li>\n
  • une \u00e9tude prospective sur une population de pr\u00e9vision de taille n3<\/sub><\/li>\n<\/ul>\n

    Des tableaux r\u00e9sum\u00e9s et d\u00e9taill\u00e9s des classements sont calcul\u00e9s. Un rapport g\u00e9n\u00e9ral de synth\u00e8se est propos\u00e9 ainsi que des graphiques des cercles et plans factoriels.[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_single_image image=\u00a0\u00bb6422″ img_size=\u00a0\u00bblarge\u00a0\u00bb alignment=\u00a0\u00bbcenter\u00a0\u00bb][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space height=\u00a0\u00bb5px\u00a0\u00bb][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text css=\u00a0\u00bb\u00a0\u00bb]<\/p>\n

    Tableaux<\/u><\/span><\/strong><\/p>\n\n\n<\/colgroup>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
    \n

    Tableau des inerties de l’ACM<\/p>\n<\/td>\n<\/tr>\n

    \n

    Centro\u00efdes des classes sur les composantes de l’ACM<\/p>\n<\/td>\n<\/tr>\n

    \n

    Distances de Mahalanobis, valeurs des Fishers et tests de signification<\/p>\n<\/td>\n<\/tr>\n

    \n

    Tableau des inerties de l’ADQ<\/p>\n<\/td>\n<\/tr>\n

    \n

    Test de Pillai<\/p>\n<\/td>\n<\/tr>\n

    \n

    Test de Box<\/p>\n<\/td>\n<\/tr>\n

    \n

    Fonctions discriminantes standardis\u00e9es (ACM)<\/p>\n<\/td>\n<\/tr>\n

    \n

    Fonctions discriminantes non standardis\u00e9es (ACM)<\/p>\n<\/td>\n<\/tr>\n

    \n

    Coordonn\u00e9es des variables (ACM)<\/p>\n<\/td>\n<\/tr>\n

    \n

    Fonctions discriminantes standardis\u00e9es exprim\u00e9es en fonction des variables qualitatives<\/p>\n<\/td>\n<\/tr>\n

    \n

    Fonctions discriminantes non standardis\u00e9es exprim\u00e9es en fonction des variables qualitatives<\/p>\n<\/td>\n<\/tr>\n

    \n

    Contributions des variables \u00e0 l’inertie<\/p>\n<\/td>\n<\/tr>\n

    \n

    R\u00e9sultats pour les individus<\/p>\n<\/td>\n<\/tr>\n

    \n

    Coordonn\u00e9es des centres des classes<\/p>\n<\/td>\n<\/tr>\n

    \n

    Coordonn\u00e9es des modalit\u00e9s des variables<\/p>\n<\/td>\n<\/tr>\n

    \n

    Matrice de confusion (apprentissage)<\/p>\n<\/td>\n<\/tr>\n

    \n

    D\u00e9tails du classement (apprentissage)<\/p>\n<\/td>\n<\/tr>\n

    \n

    Sensibilit\u00e9s, sp\u00e9cificit\u00e9s (apprentissage)<\/p>\n<\/td>\n<\/tr>\n

    \n

    Matrice de confusion (validation)<\/p>\n<\/td>\n<\/tr>\n

    \n

    D\u00e9tails du classement (validation)<\/p>\n<\/td>\n<\/tr>\n

    \n

    Sensibilit\u00e9s, sp\u00e9cificit\u00e9s (validation)<\/p>\n<\/td>\n<\/tr>\n

    \n

    Classement pr\u00e9vision<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    Graphiques<\/u><\/span><\/strong><\/p>\n\n\n<\/colgroup>\n\n\n\n\n\n\n\n\n\n\n\n
    \n

    Diagramme des inerties<\/p>\n<\/td>\n<\/tr>\n

    \n

    Plan factoriel des variables qualitatives<\/p>\n<\/td>\n<\/tr>\n

    \n

    Plan factoriel du jeu d’apprentissage<\/p>\n<\/td>\n<\/tr>\n

    \n

    Plan factoriel du jeu d’apprentissage + modalit\u00e9s des variables<\/p>\n<\/td>\n<\/tr>\n

    \n

    Plan factoriel du jeu de validation<\/p>\n<\/td>\n<\/tr>\n

    \n

    Plan factoriel du jeu de pr\u00e9vision<\/p>\n<\/td>\n<\/tr>\n

    \n

    Plans factoriels des jeux d’apprentissage et de validation<\/p>\n<\/td>\n<\/tr>\n

    \n

    Plans factoriels des jeux d’apprentissage et de pr\u00e9vision<\/p>\n<\/td>\n<\/tr>\n

    \n

    Graphiques des matrices de confusion pour les jeux d’apprentissage et de validation<\/p>\n<\/td>\n<\/tr>\n

    \n

    Courbes ROC pour les jeux d’apprentissage et de validation<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    [\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space height=\u00a0\u00bb5px\u00a0\u00bb][\/vc_column][\/vc_row][vc_row][vc_column][vc_btn title=\u00a0\u00bbConsulter la documentation compl\u00e8te\u00a0\u00bb align=\u00a0\u00bbcenter\u00a0\u00bb link=\u00a0\u00bburl:http%3A%2F%2Fwww.francestat.com%2Ftelecharg%2FUniwin%2Fpdf%2FAnalyse%20discriminante%20qualitative.pdf|title:UNIWIN%20-%20ADQ\u00a0\u00bb][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"

    [vc_row][vc_column][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text css=\u00a0\u00bb\u00a0\u00bb]L’Analyse Discriminante Qualitative (ADQ) est une g\u00e9n\u00e9ralisation de l\u2019Analyse Factorielle Discriminante (AFD) dans le cas o\u00f9 les variables explicatives sont qualitatives et non plus quantitatives. La premi\u00e8re \u00e9tape de l\u2019analyse consiste \u00e0 mettre en \u0153uvre une Analyse des Correspondances Multiples (ACM) des variables qualitatives. La deuxi\u00e8me \u00e9tape remplace les variables qualitatives d\u2019origine par les…<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-5894","page","type-page","status-publish","hentry","description-off"],"yoast_head":"\nUniwin - ADQ - FRANCESTAT<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/francestat.com\/index.php\/uniwin-adq\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Uniwin - ADQ - FRANCESTAT\" \/>\n<meta property=\"og:description\" content=\"[vc_row][vc_column][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text css=\u00a0\u00bb\u00a0\u00bb]L’Analyse Discriminante Qualitative (ADQ) est une g\u00e9n\u00e9ralisation de l\u2019Analyse Factorielle Discriminante (AFD) dans le cas o\u00f9 les variables explicatives sont qualitatives et non plus quantitatives. La premi\u00e8re \u00e9tape de l\u2019analyse consiste \u00e0 mettre en \u0153uvre une Analyse des Correspondances Multiples (ACM) des variables qualitatives. La deuxi\u00e8me \u00e9tape remplace les variables qualitatives d\u2019origine par les…\" \/>\n<meta property=\"og:url\" content=\"https:\/\/francestat.com\/index.php\/uniwin-adq\/\" \/>\n<meta property=\"og:site_name\" content=\"FRANCESTAT\" \/>\n<meta property=\"article:modified_time\" content=\"2026-06-01T15:29:13+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data1\" content=\"3 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-adq\\\/\",\"url\":\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-adq\\\/\",\"name\":\"Uniwin - ADQ - FRANCESTAT\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/francestat.com\\\/#website\"},\"datePublished\":\"2023-09-08T13:29:09+00:00\",\"dateModified\":\"2026-06-01T15:29:13+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-adq\\\/#breadcrumb\"},\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-adq\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-adq\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Accueil\",\"item\":\"https:\\\/\\\/francestat.com\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Uniwin – ADQ\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/francestat.com\\\/#website\",\"url\":\"https:\\\/\\\/francestat.com\\\/\",\"name\":\"FRANCESTAT\",\"description\":\"Logiciels, formations et services statistiques\",\"publisher\":{\"@id\":\"https:\\\/\\\/francestat.com\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/francestat.com\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"fr-FR\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/francestat.com\\\/#organization\",\"name\":\"FRANCESTAT\",\"url\":\"https:\\\/\\\/francestat.com\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\\\/\\\/francestat.com\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/francestat.com\\\/wp-content\\\/uploads\\\/2018\\\/05\\\/logo_horizontal_small_francestat.jpg\",\"contentUrl\":\"https:\\\/\\\/francestat.com\\\/wp-content\\\/uploads\\\/2018\\\/05\\\/logo_horizontal_small_francestat.jpg\",\"width\":155,\"height\":51,\"caption\":\"FRANCESTAT\"},\"image\":{\"@id\":\"https:\\\/\\\/francestat.com\\\/#\\\/schema\\\/logo\\\/image\\\/\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Uniwin - ADQ - FRANCESTAT","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/francestat.com\/index.php\/uniwin-adq\/","og_locale":"fr_FR","og_type":"article","og_title":"Uniwin - ADQ - FRANCESTAT","og_description":"[vc_row][vc_column][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text css=\u00a0\u00bb\u00a0\u00bb]L’Analyse Discriminante Qualitative (ADQ) est une g\u00e9n\u00e9ralisation de l\u2019Analyse Factorielle Discriminante (AFD) dans le cas o\u00f9 les variables explicatives sont qualitatives et non plus quantitatives. 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