{"id":3265,"date":"2019-06-07T15:48:01","date_gmt":"2019-06-07T13:48:01","guid":{"rendered":"http:\/\/francestat.com\/?page_id=3265"},"modified":"2025-03-24T16:48:28","modified_gmt":"2025-03-24T15:48:28","slug":"formation_simca","status":"publish","type":"page","link":"https:\/\/francestat.com\/index.php\/formation_simca\/","title":{"rendered":"Formation SIMCA"},"content":{"rendered":"

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<\/div>

Formation au logiciel SIMCA<\/h2><\/div><\/div>[vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column width=\u00a0\u00bb1\/3″][vc_btn title=\u00a0\u00bbFormulaire d’inscription\u00a0\u00bb align=\u00a0\u00bbcenter\u00a0\u00bb link=\u00a0\u00bburl:http%3A%2F%2Ffrancestat.com%2Ftelecharg%2FFormation%2FInscription_Formation_Francestat.pdf|title:Formulaire%20d’inscription\u00a0\u00bb][vc_btn title=\u00a0\u00bbDemande d’informations\u00a0\u00bb align=\u00a0\u00bbcenter\u00a0\u00bb link=\u00a0\u00bburl:http%3A%2F%2Ffrancestat.com%2Findex.php%2Fcontact%2F|title:Demande%20d’informations\u00a0\u00bb][\/vc_column][vc_column width=\u00a0\u00bb2\/3″][vc_column_text css=\u00a0\u00bb.vc_custom_1683965772609{background-color: #f4f4f4 !important;}\u00a0\u00bb]Ma\u00eetriser les fonctionnalit\u00e9s du logiciel SIMCA pour la gestion, la manipulation et la pratique des m\u00e9thodes statistiques multivari\u00e9es dont l’analyse en composantes principales (ACP), la m\u00e9thode NIPALS, les r\u00e9gressions PLS1, PLS2, OPLS, O2PLS, l’analyse discriminante PLS et la m\u00e9thode SIMCA. L’objectif de cette formation est de montrer comment convertir de larges volumes de donn\u00e9es en mod\u00e8les robustes et fiables pouvant \u00eatre ais\u00e9ment interpr\u00e9t\u00e9s.[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space][vc_column_text css=\u00a0\u00bb.vc_custom_1742831304820{background-color: #f4f4f4 !important;}\u00a0\u00bb]<\/p>\n
    \n
  • R\u00e9f\u00e9rence<\/strong> : FOR-SIMCA<\/li>\n
  • Dur\u00e9e<\/strong> : 3 jours, soit 21 heures en pr\u00e9sentiel<\/li>\n
  • Tarif<\/strong> : 1950 \u20ac ht par inscrit (repas inclus)<\/li>\n
  • Lieu<\/strong> : sur notre site (inter-entreprises) ou sur votre site (intra-entreprise)<\/li>\n
  • Prochaines dates<\/strong> : nous contacter<\/li>\n
  • Public concern\u00e9<\/strong> : toute personne d\u00e9sirant comprendre, analyser et mod\u00e9liser de larges volumes de donn\u00e9es<\/li>\n
  • Population vis\u00e9e<\/strong> :\u00a0techniciens, ing\u00e9nieurs, chercheurs<\/li>\n
  • Pr\u00e9-requis<\/strong> : connaissances des statistiques de base<\/li>\n
  • M\u00e9thodes et moyens<\/strong> : explications th\u00e9oriques suivies d’applications pratiques<\/li>\n
  • M\u00e9thodes d’\u00e9valuation des acquis<\/strong> : exercices r\u00e9guliers en cours de formation<\/li>\n
  • Profil formateur<\/strong> : sp\u00e9cialiste des m\u00e9thodes d’analyse de donn\u00e9es multivari\u00e9es en charge du support technique du logiciel SIMCA<\/li>\n
  • Support stagiaire<\/strong> : support de cours papier et fichiers des exercices<\/li>\n<\/ul>\n

    [\/vc_column_text][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column width=\u00a0\u00bb1\/2″][vc_column_text css=\u00a0\u00bb.vc_custom_1683966061106{background-color: #f4f4f4 !important;}\u00a0\u00bb]Pr\u00e9sentation du logiciel SIMCA<\/strong><\/p>\n

    Description des rubans disponibles selon l\u2019avancement du projet :<\/p>\n

    File, Home, Data, Analyze, Predict, View, Tools<\/p>\n

    Les \u00e9tapes du d\u00e9roulement d\u2019une \u00e9tude avec SIMCA<\/strong><\/p>\n

    Choix du type de projet : Regular, Batch, Spectroscopy, Omics<\/p>\n

    D\u00e9marrage du projet<\/p>\n

    Importation des donn\u00e9es et cr\u00e9ation du ‘Dataset’<\/p>\n

    Mise en forme des donn\u00e9es<\/p>\n

    Gestion des valeurs manquantes<\/p>\n

    Visualisation, \u00e9tude pr\u00e9liminaire des donn\u00e9es<\/p>\n

    D\u00e9tection des valeurs aberrantes<\/p>\n

    Choix des donn\u00e9es \u00e0 utiliser dans l\u2019analyse<\/p>\n

    D\u00e9finition du \u2018Workset\u2019<\/p>\n

    Transformations des donn\u00e9es<\/p>\n

    Filtres disponibles dans SIMCA<\/p>\n

    Construction de graphiques dans SIMCA<\/p>\n

    Ajustement d\u2019un mod\u00e8le<\/p>\n

    Analyse des donn\u00e9es et interpr\u00e9tation des r\u00e9sultats : les indicateurs de la qualit\u00e9 du mod\u00e8le disponibles dans SIMCA<\/p>\n

    Visualisation des scores et loadings<\/p>\n

    Utilisation des mod\u00e8les \u00e9tablis pour de la pr\u00e9vision<\/p>\n

    NIPALS (Non Linear Iterative Partial Least Squares)<\/strong><\/p>\n

    Analyse en composantes principales (ACP) bas\u00e9e sur un mode de calcul qui permet d\u2019utiliser toutes les donn\u00e9es disponibles dans un tableau observations x variables pr\u00e9sentant des valeurs manquantes et non pas seulement les cas complets comme les m\u00e9thodes classiques. Le nombre de composantes significatives \u00e0 retenir est d\u00e9termin\u00e9 par validation crois\u00e9e. Les observations atypiques sont rep\u00e9r\u00e9es gr\u00e2ce \u00e0 des tests et une carte de contr\u00f4le.[\/vc_column_text][\/vc_column][vc_column width=\u00a0\u00bb1\/2″][vc_column_text css=\u00a0\u00bb.vc_custom_1683964709330{background-color: #f4f4f4 !important;}\u00a0\u00bb]R\u00e9gression PLS1<\/strong><\/p>\n

    M\u00e9thode robuste de mod\u00e9lisation d\u2019un tableau comportant une variable \u00e0 pr\u00e9dire Y en fonction d\u2019un tableau de variables pr\u00e9dictives X bas\u00e9e sur un algorithme d\u00e9riv\u00e9 de NIPALS qui consiste \u00e0 :<\/p>\n

    Rechercher des composantes orthogonales (structures latentes) de X, \u00e0 la fois les plus descriptives possible de X et les plus explicatives possible de Y<\/p>\n

    Effectuer la r\u00e9gression de Y sur ces composantes<\/p>\n

    Exprimer les coefficients de la r\u00e9gression en fonction des variables pr\u00e9dictives elles-m\u00eames<\/p>\n

    Choisir le nombre de composantes significatives par validation crois\u00e9e<\/p>\n

    G\u00e9rer les valeurs manquantes<\/p>\n

    M\u00e9thodes de filtrage des donn\u00e9es<\/strong><\/p>\n

    OSC (Orthogonal Signal Correction)<\/p>\n

    OPLS, O2PLS (Orthogonal PLS)<\/p>\n

    R\u00e9gression PLS2<\/strong><\/p>\n

    M\u00e9thode robuste de mod\u00e9lisation d\u2019un tableau comportant plusieurs variables \u00e0 pr\u00e9dire Y en fonction d\u2019un tableau de variables pr\u00e9dictives X bas\u00e9e sur un algorithme d\u00e9riv\u00e9 de
    \nNIPALS<\/p>\n

    Analyse discriminante PLS<\/strong><\/p>\n

    Extension de la r\u00e9gression PLS au cas o\u00f9 le tableau des variables \u00e0 pr\u00e9dire Y est constitu\u00e9 des indicatrices binaires des modalit\u00e9s d\u2019une variable qualitative<\/p>\n

    M\u00e9thode SIMCA (Soft Independent Modelling of Class Analogy)<\/strong><\/p>\n

    Cette alternative originale \u00e0 l\u2019analyse discriminante permet, apr\u00e8s avoir caract\u00e9ris\u00e9 des classes pr\u00e9d\u00e9finies d\u2019observations en fonction d\u2019un ensemble de variables descriptives, de pr\u00e9voir la probabilit\u00e9 d\u2019appartenance \u00e0 chaque classe de nouvelles observations d\u00e9crites par le m\u00eame ensemble de variables. La phase de caract\u00e9risation consiste \u00e0 r\u00e9aliser une ACP de chaque classe en utilisant l\u2019algorithme NIPALS. La phase\u00a0 pr\u00e9dictive consiste \u00e0 calculer la distance entre chaque observation nouvelle et le mod\u00e8le d\u2019ACP de chaque classe, et d\u2019estimer la probabilit\u00e9 correspondante. Cette m\u00e9thode permet de rep\u00e9rer des observations qui n\u2019appartiennent \u00e0 aucune des classes pr\u00e9d\u00e9finies.[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][\/vc_column][\/vc_row]<\/p>\n","protected":false},"excerpt":{"rendered":"

    [vc_row][vc_column width=\u00a0\u00bb1\/3″][vc_empty_space][\/vc_column][vc_column width=\u00a0\u00bb2\/3″][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column width=\u00a0\u00bb1\/3″][vc_btn title=\u00a0\u00bbFormulaire d’inscription\u00a0\u00bb align=\u00a0\u00bbcenter\u00a0\u00bb link=\u00a0\u00bburl:http%3A%2F%2Ffrancestat.com%2Ftelecharg%2FFormation%2FInscription_Formation_Francestat.pdf|title:Formulaire%20d’inscription\u00a0\u00bb][vc_btn title=\u00a0\u00bbDemande d’informations\u00a0\u00bb align=\u00a0\u00bbcenter\u00a0\u00bb link=\u00a0\u00bburl:http%3A%2F%2Ffrancestat.com%2Findex.php%2Fcontact%2F|title:Demande%20d’informations\u00a0\u00bb][\/vc_column][vc_column width=\u00a0\u00bb2\/3″][vc_column_text css=\u00a0\u00bb.vc_custom_1683965772609{background-color: #f4f4f4 !important;}\u00a0\u00bb]Ma\u00eetriser les fonctionnalit\u00e9s du logiciel SIMCA pour la gestion, la manipulation et la pratique des m\u00e9thodes statistiques multivari\u00e9es dont l’analyse en composantes principales (ACP), la m\u00e9thode NIPALS, les r\u00e9gressions PLS1, PLS2, OPLS, O2PLS, l’analyse discriminante PLS et la m\u00e9thode SIMCA.…<\/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-3265","page","type-page","status-publish","hentry","description-off"],"yoast_head":"\nFormation SIMCA - 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