{"id":653,"date":"2025-10-07T12:58:40","date_gmt":"2025-10-07T11:58:40","guid":{"rendered":"https:\/\/unv-ebolowa.cm\/?p=653"},"modified":"2025-10-07T12:59:11","modified_gmt":"2025-10-07T11:59:11","slug":"statistiques-appliquees","status":"publish","type":"post","link":"https:\/\/unv-ebolowa.cm\/index.php\/en\/2025\/10\/07\/statistiques-appliquees\/","title":{"rendered":"STATISTIQUES APPLIQUEES"},"content":{"rendered":"\n

ESA 315-2 : Statistiques appliqu\u00e9es<\/strong><\/p>\n\n\n\n

    \n
  1. Informations concernant le cours<\/strong><\/li>\n<\/ol>\n\n\n\n

    Nom\/Intitul\u00e9 : Statistiques appliqu\u00e9es<\/strong> <\/strong><\/p>\n\n\n\n

    Code : <\/strong>ESA 315-2<\/strong> <\/strong><\/p>\n\n\n\n

    Fili\u00e8re et option : <\/strong>Economie, Sociologie rurale et Vulgarisation Agricole (ESVA)<\/p>\n\n\n\n

    Niveau : L3<\/strong><\/p>\n\n\n\n

    Responsable du cours : <\/strong>Pr TANESSONG Romeo Steve<\/strong><\/p>\n\n\n\n

    Disponibilit\u00e9 et contact : <\/strong>(+237) 675663700 \/ 694943889<\/strong><\/p>\n\n\n\n

    Date et p\u00e9riode du cours : D\u00e9coupage (CM : <\/strong>30 heures ; TD : <\/strong>5 heures ; TP : <\/strong>5 heures ; TPE : <\/strong>5 heures)<\/strong><\/p>\n\n\n\n

    Nombre de cr\u00e9dits : <\/strong>3  <\/strong><\/p>\n\n\n\n

    2. Description du cours<\/strong><\/p>\n\n\n\n

      \n
    • Vis\u00e9e g\u00e9n\u00e9rale du cours<\/strong><\/li>\n<\/ul>\n\n\n\n

      Le cours de statistiques appliqu\u00e9es vise \u00e0 donner des outils n\u00e9cessaires \u00e0 la r\u00e9solution des probl\u00e8mes relatifs \u00e0 l\u2019\u00e9conomie, sociologie et la vulgarisation agricole.<\/p>\n\n\n\n

        \n
      • Contenu du cours<\/strong><\/li>\n<\/ul>\n\n\n\n

        Rappel sur les \u00e9l\u00e9ments statistiques (variables, individus et population, tendances centrales) – Etude d\u2019un caract\u00e8re qualitatif – Etude d\u2019un caract\u00e8re quantitatif discret et continu – Technique d\u2019\u00e9chantillonnage – Propri\u00e9t\u00e9s statistiques – Covariance et corr\u00e9lation – Les lois statistiques (lois normale, Bernoulli, g\u00e9om\u00e9trique, de poisson, etc) – Rappel sur l\u2019analyse combinatoire (Arrangement, combinaison, tirage avec remise, tirage sans remise, variables al\u00e9atoires, th\u00e9or\u00e8me central, th\u00e9or\u00e8me central limite, th\u00e9or\u00e8me de continuit\u00e9) – Probit\/Logit multinomiaux et multivari\u00e9s.<\/p>\n\n\n\n

        3. Apprentissage vis\u00e9<\/strong><\/p>\n\n\n\n

          \n
        • Comp\u00e9tences escompt\u00e9es<\/strong><\/li>\n<\/ul>\n\n\n\n

          A la fin de ce cours, l\u2019\u00e9tudiant devrait \u00eatre capable de manier les outils statistiques de base et de les utiliser dans un domaine pr\u00e9cis.<\/p>\n\n\n\n

            \n
          • Domaines d\u2019application<\/strong><\/li>\n<\/ul>\n\n\n\n

             Economie, agronomie, . . .<\/p>\n\n\n\n

              \n
            • Pr\u00e9requis<\/strong><\/li>\n<\/ul>\n\n\n\n

              Math\u00e9matiques (Analyse et Alg\u00e8bre)<\/p>\n\n\n\n

              4. Programme et calendrier<\/strong><\/p>\n\n\n\n

              SEMAINES\/DATES (INDICATIF)<\/strong><\/td>CONTENUS\/OBJECTIFS<\/strong><\/td>ACTIVITES<\/strong><\/td>TRAVAIL PREALABLE<\/strong><\/td><\/tr>
              Semaine 1<\/td>Chapitre 1 : Variables, donn\u00e9es statistiques, tableaux, effectifs              <\/td>Cours Magistral<\/td>i.<\/strong> Lecture chapitre 1 (Support de cours remis avant le cours) ii.<\/strong> Compte rendu de lecture  <\/td><\/tr>
              Chapitre 2 : Statistique descriptive univari\u00e9e<\/td>Cours Magistral<\/td>i.<\/strong> Lecture chapitre 2 ii.<\/strong> Compte rendu de lecture iii.<\/strong> Exercices d\u2019application<\/td><\/tr>
              Chapitre 3 : Statistique descriptive bivari\u00e9e<\/td>Cours Magistral<\/td>i.<\/strong> Lecture chapitre 3 ii.<\/strong> Compte rendu de lecture iii.<\/strong> Exercices d\u2019application<\/td><\/tr>
              Semaine 2<\/td>Chapitre 4 : Calcul des probabilit\u00e9s et variables al\u00e9atoires<\/td>Cours Magistral<\/td>i.<\/strong> Lecture chapitre 4 ii.<\/strong> Compte rendu de lecture iii.<\/strong> Exercices d\u2019application<\/td><\/tr>
              Chapitre 5 : Tables statistiques<\/td>Cours Magistral<\/td>i.<\/strong> Lecture chapitre 5 ii.<\/strong> Compte rendu de lecture iii.<\/strong> Exercices d\u2019application<\/td><\/tr>
              Chapitre 6 : Th\u00e9orie d\u2019estimation<\/td>Cours Magistral<\/td>i.<\/strong> Lecture chapitre 6 ii.<\/strong> Compte rendu de lecture iii.<\/strong> Exercices d\u2019application<\/td><\/tr>
              Semaine 3<\/td>Chapitre 7 : Tests statistiques<\/td>Cours Magistral<\/td>i.<\/strong> Lecture chapitre 7 ii.<\/strong> Compte rendu de lecture iii.<\/strong> Exercices d\u2019application<\/td><\/tr>
              Chapitre 8 : Analyse de la Variance (ANOVA)<\/td>Cours Magistral<\/td>i.<\/strong> Lecture chapitre 8 ii.<\/strong> Compte rendu de lecture iii.<\/strong> Exercices d\u2019application<\/td><\/tr>
              Chapitre 9 : \u00c9chantillonnage<\/td>Cours Magistral<\/td>i.<\/strong> Lecture chapitre 9 ii.<\/strong> Compte rendu de lecture iii.<\/strong> Exercices d\u2019application<\/td><\/tr>
              Semaine 4<\/td>Tout le cours<\/td>TD et TPE<\/td>Lecture de tout le cours<\/td><\/tr>
              Evaluation \u00e9crite<\/td>CC<\/td>Evaluation sur le contenu du cours<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

              5. Mode d\u2019\u00e9valuation<\/strong><\/p>\n\n\n\n

              PERIODES<\/strong><\/td>TYPE D\u2019EXAMEN\/COMPETENCES VISEES<\/strong><\/td>POURCENTAGE<\/strong><\/td><\/tr>
              Les deux derni\u00e8res semaines de la programmation de l\u2019UE<\/td>Travaux Personnel de l\u2019Etudiant (TPE)<\/strong> V\u00e9rification de l\u2019assimilation des notions sur les statistiques descriptives et inferentielles<\/td>10%<\/td><\/tr>
              La derni\u00e8re semaine selon la programmation de l\u2019UE<\/td>Contr\u00f4le Continu (CC)<\/strong> La restitution des connaissances acquises durant tout le cours par les \u00e9tudiants<\/td>20%<\/td><\/tr>
              Selon la programmation de l\u2019ISABEE<\/td>Examen Semestriel (Session Normale)<\/strong> La restitution des connaissances acquises durant tout le cours par les \u00e9tudiants<\/td>70%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

              6. R\u00e8gles de fonctionnement du cours<\/strong><\/p>\n\n\n\n

              Tout au long du d\u00e9roulement du cours, la pr\u00e9sence des \u00e9tudiants est obligatoire<\/strong>, le contr\u00f4le effectif de la pr\u00e9sence se fait par le dressage d\u2019une liste de pr\u00e9sence et un appel syst\u00e9matique \u00e0 la fin de chaque s\u00e9ance.<\/p>\n\n\n\n

              Toute absence non justifi\u00e9e<\/strong> tout comme le non-respect des d\u00e9lais de remise des devoirs donn\u00e9s<\/strong> sont sujets \u00e0 une punition (d\u00e9cid\u00e9e en fonction de l\u2019activit\u00e9 manqu\u00e9e, pouvant aller d\u2019une t\u00e2che suppl\u00e9mentaire, d\u2019un retrait de point jusqu\u2019\u00e0 une non attribution de note).<\/p>\n\n\n\n

              La participation active<\/strong> au cours <\/strong>magistral (par des questions pos\u00e9es et par la r\u00e9ponse aux questions pos\u00e9es par l\u2019enseignant) par les \u00e9tudiants est tr\u00e8s recommand\u00e9e. Cette participation est encore plus sollicit\u00e9e pendant les s\u00e9ances de pr\u00e9sentations en salle, motiv\u00e9e par l\u2019octroi de points bonus aux \u00e9tudiants les plus actifs.<\/p>\n\n\n\n

              Toute fraude, tricherie et plagiat<\/strong> constat\u00e9s pendant les devoirs donn\u00e9s aux \u00e9tudiants sont sanctionn\u00e9s soit par l\u2019annulation pure et simple dudit devoir, donnant un nouveau travail \u00e0 faire, soit la non consid\u00e9ration du devoir ramenant ainsi la note de l\u2019\u00e9tudiant \u00e0 0.<\/p>\n\n\n\n

              7. Bibliographie du cours et ressources compl\u00e9mentaires  <\/strong><\/p>\n\n\n\n

              [1]  P. Dalgaard. Introductory Statistics with R<\/em>. Springer-Verlag, 2002.<\/p>\n\n\n\n

              [2]  J.-M. Bouroche, G. Saporta. L\u2019Analyse des Donn\u00e9es<\/em>. Presses Universitaires de France, 2002.<\/p>\n\n\n\n

              [3]  Y. Dodge. Analyse de r\u00e9gression appliqu\u00e9e, manuel et exercices<\/em>. Dunod, 2000.<\/p>\n\n\n\n

              [4]  H. Rouanet, D. Lepine. Comparison between treatments in a repeated-measurement design: Anova and multivariate methods<\/em>. British Journal of Mathematical and Statistical Psychology, 1970.<\/p>\n\n\n\n

              [5] P. Dalgaard. Introductory Statistics with R<\/em>. Springer-Verlag, 2002.<\/p>\n\n\n\n

              [6] J.-J. Droesbeke, \u00c9l\u00e9ments de statistiques<\/em>, Ellipses, 2001.<\/p>\n\n\n\n

                <\/ol>\n\n\n\n

                <\/p>\n","protected":false},"excerpt":{"rendered":"

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