Diagramme de Pareto dynamique<\/strong><\/p>\nLa Statlet – Diagramme de Pareto dynamique est con\u00e7ue pour afficher un diagramme de Pareto montrant comment les donn\u00e9es d’un ensemble de cat\u00e9gories \u00e9voluent dans le temps. A partir de donn\u00e9es repr\u00e9sentant\u00a0n<\/i>\u00a0cat\u00e9gories observ\u00e9es durant\u00a0p<\/i>\u00a0p\u00e9riodes de temps, le logiciel cr\u00e9e un affichage dynamique illustrant comment chaque cat\u00e9gorie se modifie dans le temps.[\/vc_column_text][vc_single_image image=\u00a0\u00bb4545″ img_size=\u00a0\u00bblarge\u00a0\u00bb][vc_column_text css=\u00a0\u00bb.vc_custom_1643185276034{background-color: #f4f4f4 !important;}\u00a0\u00bb]Graphique radar dynamique<\/strong><\/p>\nLa Statlet – Graphique radar dynamique est con\u00e7ue pour montrer comment les donn\u00e9es d’un ensemble de cat\u00e9gories ou de variables se modifient dans le temps. A partir de donn\u00e9es constitu\u00e9es de\u00a0k<\/i>\u00a0colonnes repr\u00e9sent\u00e9es sur\u00a0p<\/i>\u00a0p\u00e9riodes de temps, le logiciel affiche un graphique dynamique que illustre comment chaque colonne se modifie. Des donn\u00e9es classiques pour cette proc\u00e9dure sont, par exemple, des ventes mensuelles ou tout autre ensemble de donn\u00e9es ayant de forts effets saisonniers.[\/vc_column_text][vc_single_image image=\u00a0\u00bb4547″ img_size=\u00a0\u00bblarge\u00a0\u00bb][vc_column_text css=\u00a0\u00bb.vc_custom_1643185351930{background-color: #f4f4f4 !important;}\u00a0\u00bb]Graphique en spirale de s\u00e9ries temporelles<\/strong><\/p>\nLa Statlet \u2013 Graphique en spirale affiche des s\u00e9ries temporelles le long d\u2019une spirale d\u2019Archim\u00e8de qui d\u00e9bute au centre du graphique et se d\u00e9roule sous la forme d\u2019une spirale. Cela est particuli\u00e8rement utile pour afficher de grands volumes de donn\u00e9es ayant une structure saisonni\u00e8re. Les donn\u00e9es peuvent \u00eatre affich\u00e9es sous la forme de b\u00e2tons, de points ou de lignes.[\/vc_column_text][vc_single_image image=\u00a0\u00bb4548″ img_size=\u00a0\u00bblarge\u00a0\u00bb][vc_column_text css=\u00a0\u00bb.vc_custom_1643185449508{background-color: #f4f4f4 !important;}\u00a0\u00bb]S\u00e9parateurs \u00e0 vastes marges (SVM)<\/strong><\/p>\nLa proc\u00e9dure\u00a0<\/span><\/span>S\u00e9parateurs \u00e0\u00a0<\/span>V<\/span>astes\u00a0<\/span>M<\/span>arges impl\u00e9mente une proc\u00e9dure d’apprentissage machine pour pr\u00e9voir des observations \u00e0 partir de donn\u00e9es. Elle cr\u00e9e des mod\u00e8les de deux formes :<\/span>\u00a0<\/span>m<\/span>od\u00e8les de classement<\/span><\/span>\u00a0qui d\u00e9coupent des\u00a0observations en groupes en se basant sur les caract\u00e9ristiques observ\u00e9es<\/span>,\u00a0m<\/span>od\u00e8les de r\u00e9gression<\/span>\u00a0qui pr\u00e9voient la valeur d’une variable \u00e0 expliquer.<\/span>\u00a0<\/span>Dans le cas d’un mod\u00e8le de classement, l’algorithme d\u00e9coupent les observations en groupes en g\u00e9n\u00e9rant des marges autour des groupes aussi vastes que possible. Dans le cas d’un mod\u00e8le de r\u00e9gression, l’algorithme minimise les coefficients d’un mod\u00e8le dans lequel la distance des observations \u00e0 une r\u00e9gion autour du mod\u00e8le ajust\u00e9 d\u00e9finie par un montant d’erreur acceptable est aussi petite que possible.<\/span>\u00a0Les observations sont classiquement d\u00e9coup\u00e9es en trois jeux :<\/span>\u00a0un jeu d’apprentissage utilis\u00e9 pour construire le mod\u00e8le<\/span>, un jeu de validation pour lequel le groupe ou la valeur est connu, utilis\u00e9 pour valider le mod\u00e8le<\/span>, un jeu de pr\u00e9vision pour lequel le groupe ou la valeur n’est pas connu, utilis\u00e9 pour faire les pr\u00e9visions d\u00e9sir\u00e9es.<\/span>\u00a0La variable \u00e0 expliquer peut \u00eatre qualitative ou quantitative mais les facteurs pr\u00e9dictifs sont toutes quantitatifs.<\/span><\/span><\/span>\u00a0<\/span>[\/vc_column_text][vc_single_image image=\u00a0\u00bb4550″ img_size=\u00a0\u00bblarge\u00a0\u00bb][vc_column_text css=\u00a0\u00bb.vc_custom_1643185519465{background-color: #f4f4f4 !important;}\u00a0\u00bb]Ajustement de m\u00e9langes de lois non gaussiennes<\/strong><\/p>\nLa proc\u00e9dure Ajustement de lois (mod\u00e8les de m\u00e9langes non gaussiens)\u00a0ajuste une loi \u00e0 des donn\u00e9es num\u00e9riques continues constitu\u00e9es d’un m\u00e9lange de deux lois ou plus normales univari\u00e9es, de Weibull, gamma ou log-normales. Les composants du m\u00e9lange peuvent repr\u00e9senter diff\u00e9rents groupes dans l’\u00e9chantillon utilis\u00e9 pour ajuster la loi globale ou un mod\u00e8le de m\u00e9lange peut approximer une loi ayant une forme plus complexe.[\/vc_column_text][vc_single_image image=\u00a0\u00bb4551″ img_size=\u00a0\u00bblarge\u00a0\u00bb][\/vc_column][\/vc_row]<\/p>\n","protected":false},"excerpt":{"rendered":"
[vc_row][vc_column width=\u00a0\u00bb1\/4″][vc_column_text][\/vc_column_text][\/vc_column][vc_column width=\u00a0\u00bb3\/4″][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column width=\u00a0\u00bb1\/4″][vc_empty_space][vc_btn title=\u00a0\u00bbStatgraphics Centurion 19″ size=\u00a0\u00bbsm\u00a0\u00bb align=\u00a0\u00bbcenter\u00a0\u00bb link=\u00a0\u00bburl:https%3A%2F%2Ffrancestat.com%2Findex.php%2Fcenturion19%2F|title:Statgraphics%20Centurion%2019″][\/vc_column][vc_column width=\u00a0\u00bb3\/4″][vc_column_text css=\u00a0\u00bb.vc_custom_1643185109499{background-color: #f4f4f4 !important;}\u00a0\u00bb]Diagramme de Pareto dynamique La Statlet – Diagramme de Pareto dynamique est con\u00e7ue pour afficher un diagramme de Pareto montrant comment les donn\u00e9es d’un ensemble de cat\u00e9gories \u00e9voluent dans le temps. A partir de donn\u00e9es repr\u00e9sentant\u00a0n\u00a0cat\u00e9gories observ\u00e9es durant\u00a0p\u00a0p\u00e9riodes de temps, le logiciel…<\/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-4542","page","type-page","status-publish","hentry","description-off"],"yoast_head":"\n
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