La fonction binom.test()<\/b> r\u00e9alise un test de conformit\u00e9 d\u2019une proportion mesur\u00e9e dans un \u00e9chantillon et permet de savoir si cette proportion est diff\u00e9rente d\u2019une valeur cible. Pour cela on compare Pobs \u00e0 Pth\u00e9o, dans un \u00e9chantillon de n individus.<\/p>\n
\u00a0<\/p>\n
Pour r\u00e9aliser ce test il est n\u00e9cessaire d\u2019avoir un \u00e9chantillonnage al\u00e9atoire et que la condition suivante soit respecter\u00a0:<\/p>\n
n Pobs\u00a0 \u2265 5\u00a0 et n (1-Pobs ) \u2265 5<\/p>\n
\u00a0<\/p>\n
On utilise la fonction binom.test(x, n, p)<\/b> o\u00f9\u00a0:<\/p>\n
\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 x : nombre de succ\u00e8s<\/p>\n
\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 n : nombre d\u2019essais<\/p>\n
\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 p : probabilit\u00e9 th\u00e9orique<\/p>\n
\u00a0<\/p>\n
Exemple :<\/p>\n
\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0\n\n# 92 \u00ab oui \u00bb sur 100 r\u00e9pondant est-ce diff\u00e9rent de 80% ?\n\n# L\u2019hypoth\u00e8se H0\u00a0: la proportion est \u00e9gale \u00e0 80%\n\nbinom.test(92,100,0.8)\n\n\u00a0\n\n# \u00a0 \u00a0 \u00a0 \u00a0Exact binomial test\n\n\u00a0\n\n#data:\u00a0 92 and 100\n\n#number of successes = 92, number of trials = 100, p-value = 0.001592\n\n#alternative hypothesis: true probability of success is not equal to 0.8\n\n#95 percent confidence interval:\n\n# 0.8484424 0.9648284\n\n#sample estimates:\n\n#probability of success\n\n# \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a00.92\n\n<\/code><\/pre>\n\u00a0<\/p>\n
La probabilit\u00e9 critique \u00e9tant tr\u00e8s inf\u00e9rieure \u00e0 0.05 nous rejetons l\u2019hypoth\u00e8se que la proportion est \u00e9gale \u00e0 80%.<\/p>\n","protected":false},"excerpt":{"rendered":"
La fonction binom.test() r\u00e9alise un test de conformit\u00e9 d\u2019une proportion mesur\u00e9e dans un \u00e9chantillon et permet de savoir si cette proportion est diff\u00e9rente d\u2019une valeur cible. Pour cela on compare Pobs \u00e0 Pth\u00e9o, dans un \u00e9chantillon de n individus. \u00a0 Pour r\u00e9aliser ce test il est n\u00e9cessaire d\u2019avoir un \u00e9chantillonnage al\u00e9atoire et que la condition suivante soit respecter\u00a0: n Pobs\u00a0 \u2265 5\u00a0 et n (1-Pobs ) \u2265 5 \u00a0 On utilise la fonction binom.test(x, n, p) o\u00f9\u00a0: \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 x : nombre de succ\u00e8s \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 n : nombre d\u2019essais \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 p : probabilit\u00e9 th\u00e9orique \u00a0 Exemple : \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0\u00a0 # 92Read 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":[21],"tags":[],"class_list":{"0":"entry","1":"post","2":"publish","3":"author-helene","4":"post-3179","6":"format-standard","7":"category-test"},"acf":[],"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/p9O7Sx-Ph","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/posts\/3179","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=3179"}],"version-history":[{"count":2,"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/posts\/3179\/revisions"}],"predecessor-version":[{"id":4294,"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/posts\/3179\/revisions\/4294"}],"wp:attachment":[{"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/media?parent=3179"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/categories?post=3179"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/tags?post=3179"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}