{"id":2362,"date":"2023-07-22T14:33:59","date_gmt":"2023-07-22T14:33:59","guid":{"rendered":"https:\/\/statorials.org\/ja\/t-python-%e3%83%86%e3%82%99%e3%82%a3%e3%82%b9%e3%83%88%e3%83%aa%e3%83%92%e3%82%99%e3%83%a5%e3%83%bc%e3%82%b7%e3%83%a7%e3%83%b3\/"},"modified":"2023-07-22T14:33:59","modified_gmt":"2023-07-22T14:33:59","slug":"t-python-%e3%83%86%e3%82%99%e3%82%a3%e3%82%b9%e3%83%88%e3%83%aa%e3%83%92%e3%82%99%e3%83%a5%e3%83%bc%e3%82%b7%e3%83%a7%e3%83%b3","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/t-python-%e3%83%86%e3%82%99%e3%82%a3%e3%82%b9%e3%83%88%e3%83%aa%e3%83%92%e3%82%99%e3%83%a5%e3%83%bc%e3%82%b7%e3%83%a7%e3%83%b3\/","title":{"rendered":"Python \u3067 t \u5206\u5e03\u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"

<\/p>\n

\n

t \u5206\u5e03\u306f\u3001\u6b63\u898f\u5206\u5e03\u3088\u308a\u3082\u91cd\u3044\u300c\u88fe\u300d\u304c\u3042\u308b\u3053\u3068\u3092<\/span>\u9664\u3044<\/strong>\u3066<\/a>\u3001\u6b63\u898f\u5206\u5e03\u306b\u4f3c\u305f\u78ba\u7387\u5206\u5e03\u3067\u3059<\/span>\u3002<\/p>\n

\u8a00\u3044\u63db\u3048\u308b\u3068\u3001\u6b63\u898f\u5206\u5e03\u3068\u6bd4\u8f03\u3059\u308b\u3068\u3001\u5206\u5e03\u5185\u306e\u5024\u304c\u4e2d\u5fc3\u3088\u308a\u3082\u7aef\u306b\u591a\u304f\u914d\u7f6e\u3055\u308c\u307e\u3059\u3002<\/span> <\/p>\n

\"\u6b63\u898f\u5206\u5e03\u3068<\/p>\n

\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067 t \u5206\u5e03\u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n

\u751f\u6210\u304b\u3089\u914d\u5e03\u307e\u3067\u306e\u65b9\u6cd5<\/strong><\/h3>\n

t.rvs(df, size)<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u3068\u3001\u7279\u5b9a\u306e\u81ea\u7531\u5ea6\u3068\u30b5\u30f3\u30d7\u30eb \u30b5\u30a4\u30ba\u306e\u5206\u5e03\u304b\u3089\u30e9\u30f3\u30c0\u30e0\u306a\u5024\u3092\u751f\u6210\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n

 from<\/span> scipy. stats<\/span> import<\/span> t\n\n#generate random values from t distribution with df=6 and sample size=10<\/span>\nt. rvs<\/span> (df= 6<\/span> , size= 10<\/span> )\n\narray([-3.95799716, -0.01099963, -0.55953846, -1.53420055, -1.41775611,\n       -0.45384974, -0.2767931, -0.40177789, -0.3602592, 0.38262431])\n<\/strong><\/pre>\n
\u7d50\u679c\u306f\u30016 \u81ea\u7531\u5ea6\u306e\u5206\u5e03\u306b\u5f93\u3063\u3066\u4e92\u3044\u306b\u7d9a\u304f 10 \u500b\u306e\u5024\u306e\u30c6\u30fc\u30d6\u30eb\u306b\u306a\u308a\u307e\u3059\u3002<\/span><\/p>\n
 t\u5206\u5e03\u3092\u4f7f\u7528\u3057\u305fP\u5024\u306e\u8a08\u7b97\u65b9\u6cd5<\/strong><\/h3>\n
\u95a2\u6570t.cdf(x, df, loc=0,scale=1) \u3092<\/strong>\u4f7f\u7528\u3057\u3066\u3001t \u691c\u5b9a\u7d71\u8a08\u91cf\u306b\u95a2\u9023\u4ed8\u3051\u3089\u308c\u305f p \u5024\u3092\u898b\u3064\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
\u4f8b 1: \u7247\u5074 P \u5024\u3092\u6c42\u3081\u308b<\/strong><\/span><\/p>\n
\u7247\u5074\u4eee\u8aac\u691c\u5b9a<\/a>\u3092\u5b9f\u884c\u3057\u3001\u691c\u5b9a\u7d71\u8a08\u91cf-1.5<\/strong>\u304a\u3088\u3073\u81ea\u7531\u5ea6 = 10<\/strong>\u304c\u5f97\u3089\u308c\u305f\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001\u3053\u306e\u691c\u5b9a\u7d71\u8a08\u91cf\u306b\u5bfe\u5fdc\u3059\u308b p \u5024\u3092\u8a08\u7b97\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 from<\/span> scipy. stats<\/span> import<\/span> t\n\n#calculate p-value<\/span>\nt. cdf<\/span> (x=-1.5, df=10)\n\n0.08225366322272008\n<\/strong><\/pre>\n
 10 \u81ea\u7531\u5ea6\u3067\u306e\u691c\u5b9a\u7d71\u8a08\u91cf -1.5 \u306b\u5bfe\u5fdc\u3059\u308b\u7247\u5074 p \u5024\u306f0.0822<\/strong>\u3067\u3059\u3002<\/span><\/p>\n
\u4f8b 2: \u4e8c\u5143\u914d\u7f6e P \u5024\u3092\u6c42\u3081\u308b<\/strong><\/span><\/p>\n
\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001\u3053\u306e\u691c\u5b9a\u7d71\u8a08\u91cf\u306b\u5bfe\u5fdc\u3059\u308b p \u5024\u3092\u8a08\u7b97\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 from<\/span> scipy. stats<\/span> import<\/span> t\n\n#calculate p-value<\/span>\n(1 - t. cdf<\/span> (x=2.14, df=20)) *<\/span> 2\n\n0.04486555082549959\n<\/strong><\/pre>\n
 20 \u81ea\u7531\u5ea6\u306e\u691c\u5b9a\u7d71\u8a08\u91cf 2.14 \u306b\u5bfe\u5fdc\u3059\u308b\u4e21\u5074 p \u5024\u306f0.0448<\/strong>\u3067\u3059\u3002<\/span><\/p>\n
\u6ce8<\/strong>: \u3053\u308c\u3089\u306e\u7b54\u3048\u306f\u3001\u9006 t \u5206\u5e03\u8a08\u7b97\u30c4\u30fc\u30eb\u3092\u4f7f\u7528\u3057\u3066\u78ba\u8a8d\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
\u914d\u5e03\u307e\u3067\u8ffd\u8de1\u3059\u308b\u65b9\u6cd5<\/strong><\/span><\/h3>\n
\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001\u7279\u5b9a\u306e\u81ea\u7531\u5ea6\u3067\u5206\u5e03\u3092\u30d7\u30ed\u30c3\u30c8\u3067\u304d\u307e\u3059\u3002<\/span> <\/p>\n
 from<\/span> scipy. stats<\/span> import<\/span> t\nimport<\/span> matplotlib. pyplot<\/span> as<\/span> plt\n\n#generate t distribution with sample size 10000<\/span>\nx = t. rvs<\/span> (df= 12<\/span> , size= 10000<\/span> )\n\n#create plot of t distribution\n<\/span>plt. hist<\/span> (x, density= True<\/span> , edgecolor=' black<\/span> ', bins= 20<\/span> )\n<\/strong><\/pre>\n
\"Python<\/p>\n
 import<\/span> seaborn as<\/span> sns\n\n#create density curve<\/span>\nsns. kdeplot<\/span> (x)\n<\/strong><\/pre>\n
\"Python\u3067t\u5206\u5e03\u66f2\u7dda\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\"<\/p>\n
\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n
\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u30c7\u30a3\u30b9\u30c8\u30ea\u30d3\u30e5\u30fc\u30b7\u30e7\u30f3\u306b\u95a2\u3059\u308b\u8ffd\u52a0\u60c5\u5831\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u6b63\u898f\u5206\u5e03\u3068 t \u5206\u5e03: \u9055\u3044\u306f\u4f55\u3067\u3059\u304b?<\/a>
\u9006t\u5206\u5e03\u8a08\u7b97\u6a5f<\/p>\n","protected":false},"excerpt":{"rendered":"
t \u5206\u5e03\u306f\u3001\u6b63\u898f\u5206\u5e03\u3088\u308a\u3082\u91cd\u3044\u300c\u88fe\u300d\u304c\u3042\u308b\u3053\u3068\u3092\u9664\u3044\u3066\u3001\u6b63\u898f\u5206\u5e03\u306b\u4f3c\u305f\u78ba\u7387\u5206\u5e03\u3067\u3059\u3002 \u8a00\u3044\u63db\u3048\u308b\u3068\u3001\u6b63\u898f\u5206\u5e03\u3068\u6bd4\u8f03\u3059\u308b\u3068\u3001\u5206\u5e03\u5185\u306e\u5024\u304c\u4e2d\u5fc3\u3088\u308a\u3082\u7aef\u306b\u591a\u304f\u914d\u7f6e\u3055\u308c\u307e\u3059\u3002 \u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067 t \u5206\u5e03\u3092 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"class_list":["post-2362","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"\nPython \u3067 t \u5206\u5e03\u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5 - Statorials<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067 t \u5206\u5e03\u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u3044\u304f\u3064\u304b\u306e\u4f8b\u3068\u3068\u3082\u306b\u8aac\u660e\u3057\u307e\u3059\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/ja\/t-python-\u30c6\u3099\u30a3\u30b9\u30c8\u30ea\u30d2\u3099\u30e5\u30fc\u30b7\u30e7\u30f3\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python \u3067 t \u5206\u5e03\u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5 - 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\u3042\u308b\u3044\u306f\u3001 seaborn<\/a>\u8996\u899a\u5316\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u4f7f\u7528\u3057\u3066\u5bc6\u5ea6\u66f2\u7dda<\/a>\u3092\u4f5c\u6210\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span> <\/p>\n
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