Le test de Fisher-Snedecor permet de savoir si les variances de deux populations (\u03b41\u00b2 et \u03b42\u00b2) sont \u00e9gales ou non. L\u2019hypoth\u00e8se H0 test\u00e9e correspond \u00e0 \u03b41\u00b2= \u03b42\u00b2.<\/p>\n
Pour r\u00e9aliser ce test il est n\u00e9cessaire d\u2019avoir un \u00e9chantillonnage al\u00e9atoire de chaque individu et que les ces deux \u00e9chantillons suivent une loi Normale. On utilise pour tester cette hypoth\u00e8se la fonction var.test().<\/b><\/p>\n \u00a0<\/p>\n On commence par tester la normalit\u00e9 de cette variable gr\u00e2ce au test de Shapiro\u00a0:<\/p>\n \u00a0<\/p>\n La p-value est sup\u00e9rieur \u00e0 0.05 on ne rejette donc pas l’hypoth\u00e8se de normalit\u00e9.<\/p>\n \u00a0<\/p>\n On remarque que la probabilit\u00e9 critique (p-value) vaut 0.008, ce qui est bien inf\u00e9rieur \u00e0 0.05. Nous rejetons donc l\u2019hypoth\u00e8se H0. Cela signifie que la variance de la longueur des s\u00e9pales et significativement diff\u00e9rente entre les deux esp\u00e8ces d\u2019Iris \u00e9tudi\u00e9es. L\u2019intervalle de confiance avec un seuil de 95% est [0.26\u00a0; 0.82] et le quotient des variances de 0.46.<\/p>\n","protected":false},"excerpt":{"rendered":" Le test de Fisher-Snedecor permet de savoir si les variances de deux populations (\u03b41\u00b2 et \u03b42\u00b2) sont \u00e9gales ou non. L\u2019hypoth\u00e8se H0 test\u00e9e correspond \u00e0 \u03b41\u00b2= \u03b42\u00b2. Pour r\u00e9aliser ce test il est n\u00e9cessaire d\u2019avoir un \u00e9chantillonnage al\u00e9atoire de chaque individu et que les ces deux \u00e9chantillons suivent une loi Normale. On utilise pour tester cette hypoth\u00e8se la fonction var.test(). data(iris) A<-subset(iris,Species==\u00a0\u00bbsetosa\u00a0\u00bb)[,2] #On isole la 2\u00e8me colonne : la largeur des s\u00e9pales \u00a0 On commence par tester la normalit\u00e9 de cette variable gr\u00e2ce au test de Shapiro\u00a0: shapiro.test(A) \u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Shapiro-Wilk normality test \u00a0 data:\u00a0 A W = 0.9717, p-value = 0.2715 \u00a0 La p-valueRead More →<\/a><\/p>\n","protected":false},"author":13,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","rop_custom_images_group":[],"rop_custom_messages_group":[],"rop_publish_now":"initial","rop_publish_now_accounts":{"twitter_399453572_399453572":""},"rop_publish_now_history":[],"rop_publish_now_status":"pending","jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[8,12],"tags":[],"class_list":{"0":"entry","1":"post","2":"publish","3":"author-helene","4":"post-3165","6":"format-standard","7":"category-fonctions-utiles","8":"category-manipulation-de-donnees"},"acf":[],"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/p9O7Sx-P3","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/posts\/3165","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/users\/13"}],"replies":[{"embeddable":true,"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/comments?post=3165"}],"version-history":[{"count":2,"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/posts\/3165\/revisions"}],"predecessor-version":[{"id":4290,"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/posts\/3165\/revisions\/4290"}],"wp:attachment":[{"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/media?parent=3165"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/categories?post=3165"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/tags?post=3165"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
data(iris)\n\nA<-subset(iris,Species==\"setosa\")[,2]\n\n#On isole la 2\u00e8me colonne : la largeur des s\u00e9pales\n\n<\/code><\/pre>\n
shapiro.test(A)\n\n\u00a0\n\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Shapiro-Wilk normality test\n\n\u00a0\n\ndata:\u00a0 A\n\nW = 0.9717, p-value = 0.2715\n\n<\/code><\/pre>\n
#On isole les 100 premi\u00e8res lignes qui correspondent aux donn\u00e9es des esp\u00e8ces Setosa et Versicolor.\n\nvar.test(Sepal.Length~Species, data=iris[1:100,])\n\n\u00a0\n\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 F test to compare two variances\n\n\u00a0\n\ndata:\u00a0 Sepal.Length by Species\n\nF = 0.4663, num df = 49, denom df = 49, p-value = 0.008657\n\nalternative hypothesis: true ratio of variances is not equal to 1\n\n95 percent confidence interval:\n\n\u00a00.2646385 0.8217841\n\nsample estimates:\n\nratio of variances\n\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 0.4663429\n\n<\/code><\/pre>\n