{"id":2997,"date":"2023-07-19T17:14:42","date_gmt":"2023-07-19T17:14:42","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab%e1%84%8b%e1%85%b4-%e1%84%8b%e1%85%b5%e1%86%af%e1%84%87%e1%85%a1%e1%86%ab-cdf\/"},"modified":"2023-07-19T17:14:42","modified_gmt":"2023-07-19T17:14:42","slug":"%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab%e1%84%8b%e1%85%b4-%e1%84%8b%e1%85%b5%e1%86%af%e1%84%87%e1%85%a1%e1%86%ab-cdf","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab%e1%84%8b%e1%85%b4-%e1%84%8b%e1%85%b5%e1%86%af%e1%84%87%e1%85%a1%e1%86%ab-cdf\/","title":{"rendered":"Python\uc5d0\uc11c \uc77c\ubc18 cdf\ub97c \uacc4\uc0b0\ud558\uace0 \ud50c\ub86f\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

\ub204\uc801 \ubd84\ud3ec \ud568\uc218( CDF<\/strong> ) \ub294 \uc784\uc758 \ubcc0\uc218\uac00 \ud2b9\uc815 \uac12\ubcf4\ub2e4 \uc791\uac70\ub098 \uac19\uc740 \uac12\uc744 \uac00\uc9c8 \ud655\ub960\uc744 \uc54c\ub824\uc90d\ub2c8\ub2e4.<\/span><\/p>\n

\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\uc5d0\uc11c \uc77c\ubc18 CDF \uac12\uc744 \uacc4\uc0b0\ud558\uace0 \ud50c\ub86f\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n

\uc608\uc81c 1: Python\uc5d0\uc11c \uc77c\ubc18 CDF \ud655\ub960 \uacc4\uc0b0<\/strong><\/span><\/h3>\n

Python\uc5d0\uc11c \uc77c\ubc18 CDF \ud655\ub960\uc744 \uacc4\uc0b0\ud558\ub294 \uac00\uc7a5 \uc26c\uc6b4 \ubc29\ubc95\uc740 SciPy<\/a> \ub77c\uc774\ube0c\ub7ec\ub9ac\uc758 norm.cdf()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.<\/span><\/p>\n

\ub2e4\uc74c \ucf54\ub4dc\ub294 \ud45c\uc900 \uc815\uaddc \ubd84\ud3ec\uc5d0\uc11c \ud655\ub960 \ubcc0\uc218\uac00 1.96\ubcf4\ub2e4 \uc791\uc740 \uac12\uc744 \uac00\uc9c8 \ud655\ub960\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n

 from<\/span> scipy. stats<\/span> import<\/span> norm\n<\/span>\n#calculate probability that random value is less than 1.96 in normal CDF<\/span>\nnorm. cdf<\/span> ( 1.96<\/span> )\n\n<\/span>0.9750021048517795\n<\/strong><\/pre>\n
 \ud45c\uc900\uc815\uaddc\ubd84\ud3ec\uc5d0\uc11c \ud655\ub960\ubcc0\uc218\uac00 1.96\ubcf4\ub2e4 \uc791\uc740 \uac12\uc744 \uac00\uc9c8 \ud655\ub960\uc740 \ub300\ub7b5 0.975<\/strong> \uc774\ub2e4.<\/span><\/p>\n
 \ub610\ud55c 1\uc5d0\uc11c \uc774 \uac12\uc744 \ube7c\uba74 \uc784\uc758 \ubcc0\uc218\uac00 1.96\ubcf4\ub2e4 \ud070 \uac12\uc744 \uac00\uc9c8 \ud655\ub960\uc744 \ucc3e\uc744 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 from<\/span> scipy. stats<\/span> import<\/span> norm\n<\/span>\n#calculate probability that random value is greater than 1.96 in normal CDF<\/span>\n1 - norm. cdf<\/span> ( 1.96<\/span> )\n\n<\/span>0.024997895148220484\n<\/strong><\/pre>\n
 \ud45c\uc900\uc815\uaddc\ubd84\ud3ec\uc5d0\uc11c \ud655\ub960\ubcc0\uc218\uac00 1.96\ubcf4\ub2e4 \ud070 \uac12\uc744 \uac00\uc9c8 \ud655\ub960\uc740 \uc57d 0.025<\/strong> \uc774\ub2e4.<\/span><\/p>\n
 \uc608\uc81c 2: \uc815\uaddc CDF \ub3c4\ud45c\ud654<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 Python\uc5d0\uc11c \uc77c\ubc18 CDF\ub97c \uadf8\ub9ac\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span> <\/p>\n
 import<\/span> matplotlib. pyplot<\/span> as<\/span> plt\nimport<\/span> numpy as<\/span> np\nimport<\/span> scipy. stats<\/span> as<\/span> ss\n\n#define x and y values to use for CDF\n<\/span>x = np. linspace<\/span> (-4, 4, 1000)\ny = ss. norm<\/span> . cdf<\/span> (x)\n\n#normal plot CDF\n<\/span>plt. plot<\/span> (x, y)<\/strong> <\/pre>\n
\"Python\uc758<\/p>\n
 x\ucd95\uc740 \ud45c\uc900\uc815\uaddc\ubd84\ud3ec\ub97c \ub530\ub974\ub294 \ud655\ub960\ubcc0\uc218\uc758 \uac12\uc744 \ub098\ud0c0\ub0b4\uace0, y\ucd95\uc740 \ud655\ub960\ubcc0\uc218\uac00 x\ucd95\uc5d0 \ud45c\uc2dc\ub41c \uac12\ubcf4\ub2e4 \uc791\uc740 \uac12\uc744 \uac00\uc9c8 \ud655\ub960\uc744 \ub098\ud0c0\ub0b8\ub2e4.<\/span><\/p>\n
 \uc608\ub97c \ub4e4\uc5b4 x = 1.96\uc744 \ubcf4\uba74 x\uac00 1.96\ubcf4\ub2e4 \uc791\uc744 \ub204\uc801 \ud655\ub960\uc740 \uc57d 0.975<\/strong> \ub77c\ub294 \uac83\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc77c\ubc18 CDF \ud50c\ub86f\uc758 \ucd95 \uc0c9\uc0c1\uacfc \ub808\uc774\ube14\ub3c4 \uc790\uc720\ub86d\uac8c \ubcc0\uacbd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
 import<\/span> matplotlib. pyplot<\/span> as<\/span> plt\nimport<\/span> numpy as<\/span> np\nimport<\/span> scipy. stats<\/span> as<\/span> ss\n\n#define x and y values to use for CDF\n<\/span>x = np. linspace<\/span> (-4, 4, 1000)\ny = ss. norm<\/span> . cdf<\/span> (x)\n\n#normal plot CDF\n<\/span>plt. plot<\/span> (x,y,color=' red<\/span> ')\nplt. title<\/span> (' Normal CDF<\/span> ')\nplt. xlabel<\/span> (' x<\/span> ')\nplt. ylabel<\/span> (' CDF<\/span> ')\n<\/strong><\/pre>\n
\"\"<\/p>\n
 \ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\uc5d0\uc11c \ub2e4\ub978 \uc77c\ubc18\uc801\uc778 \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 Python\uc5d0\uc11c \uc815\uaddc \ubd84\ud3ec\ub97c \uc0dd\uc131\ud558\ub294 \ubc29\ubc95<\/a>
 Python\uc5d0\uc11c \uc815\uaddc \ubd84\ud3ec\ub97c \uadf8\ub9ac\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub204\uc801 \ubd84\ud3ec \ud568\uc218( CDF ) \ub294 \uc784\uc758 \ubcc0\uc218\uac00 \ud2b9\uc815 \uac12\ubcf4\ub2e4 \uc791\uac70\ub098 \uac19\uc740 \uac12\uc744 \uac00\uc9c8 \ud655\ub960\uc744 \uc54c\ub824\uc90d\ub2c8\ub2e4. \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\uc5d0\uc11c \uc77c\ubc18 CDF \uac12\uc744 \uacc4\uc0b0\ud558\uace0 \ud50c\ub86f\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4. \uc608\uc81c 1: Python\uc5d0\uc11c \uc77c\ubc18 CDF \ud655\ub960 \uacc4\uc0b0 Python\uc5d0\uc11c \uc77c\ubc18 CDF \ud655\ub960\uc744 \uacc4\uc0b0\ud558\ub294 \uac00\uc7a5 \uc26c\uc6b4 \ubc29\ubc95\uc740 SciPy \ub77c\uc774\ube0c\ub7ec\ub9ac\uc758 norm.cdf() \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \ub2e4\uc74c \ucf54\ub4dc\ub294 \ud45c\uc900 \uc815\uaddc \ubd84\ud3ec\uc5d0\uc11c \ud655\ub960 \ubcc0\uc218\uac00 1.96\ubcf4\ub2e4 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-2997","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nPython\uc5d0\uc11c \uc77c\ubc18 CDF\ub97c \uacc4\uc0b0\ud558\uace0 \ud50c\ub86f\ud558\ub294 \ubc29\ubc95 - 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