{"id":981,"date":"2023-07-28T02:35:28","date_gmt":"2023-07-28T02:35:28","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%8b%e1%85%b5%e1%84%8e%e1%85%a1-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab\/"},"modified":"2023-07-28T02:35:28","modified_gmt":"2023-07-28T02:35:28","slug":"%e1%84%8b%e1%85%b5%e1%84%8e%e1%85%a1-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%8b%e1%85%b5%e1%84%8e%e1%85%a1-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab\/","title":{"rendered":"Python\uc5d0\uc11c 2\ucc28 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

2\ucc28 \ud68c\uadc0<\/strong> \ub294 \uc2e4\uc81c \uad00\uacc4\uac00 2\ucc28(\uadf8\ub798\ud504\uc5d0\uc11c “U” \ub610\ub294 \ubc18\uc804\ub41c “U” \ubaa8\uc591)\uc77c \ub54c \uc608\uce21 \ubcc0\uc218\uc640 \uc751\ub2f5 \ubcc0\uc218 \uac04\uc758 \uad00\uacc4\ub97c \uc218\ub7c9\ud654\ud558\ub294 \ub370 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ud68c\uadc0 \uc720\ud615\uc785\ub2c8\ub2e4.<\/span><\/p>\n

\uc989, \uc608\uce21\ubcc0\uc218\uac00 \uc99d\uac00\ud558\uba74 \ubc18\uc751\ubcc0\uc218\ub3c4 \uc99d\uac00\ud558\ub294 \uacbd\ud5a5\uc774 \uc788\uc73c\ub098, \uc77c\uc815 \uc2dc\uc810 \uc774\ud6c4\uc5d0\ub294 \uc608\uce21\ubcc0\uc218\uac00 \uacc4\uc18d \uc99d\uac00\ud558\uba74\uc11c \ubc18\uc751\ubcc0\uc218\uac00 \uac10\uc18c\ud558\uae30 \uc2dc\uc791\ud55c\ub2e4.<\/span><\/p>\n

\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\uc5d0\uc11c 2\ucc28 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n

\uc608: Python\uc758 2\ucc28 \ud68c\uadc0<\/span><\/strong><\/h2>\n

\uc8fc\ub2f9 \uadfc\ubb34 \uc2dc\uac04\uc5d0 \ub300\ud55c \ub370\uc774\ud130\uac00 \uc788\uace0 16\uba85\uc758 \uc11c\ub85c \ub2e4\ub978 \uc0ac\ub78c\ub4e4\uc5d0 \ub300\ud574 \ubcf4\uace0\ub41c \ud589\ubcf5 \uc218\uc900(0~100\uc810)\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

 import<\/span> numpy as<\/span> np\nimport<\/span> scipy.stats as<\/span> stats\n\n#define variables<\/span>\nhours = [6, 9, 12, 12, 15, 21, 24, 24, 27, 30, 36, 39, 45, 48, 57, 60]\nhapp = [12, 18, 30, 42, 48, 78, 90, 96, 96, 90, 84, 78, 66, 54, 36, 24]<\/strong><\/pre>\n
 \uc774 \ub370\uc774\ud130\uc758 \uac04\ub2e8\ud55c \uc0b0\uc810\ub3c4\ub97c \uc791\uc131\ud558\uba74 \ub450 \ubcc0\uc218 \uac04\uc758 \uad00\uacc4\uac00 “U”\uc790 \ubaa8\uc591\uc784\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
 import matplotlib.pyplot<\/span> as plt<\/span>\n\n#create scatterplot\n<\/span>plt.scatter(hours, happ)<\/span><\/span><\/strong> <\/pre>\n
\"\"<\/p>\n
 \uadfc\ubb34 \uc2dc\uac04\uc774 \ub298\uc5b4\ub0a0\uc218\ub85d \ud589\ubcf5\ub3c4 \ub192\uc544\uc9c0\uc9c0\ub9cc, \uadfc\ubb34 \uc2dc\uac04\uc774 \uc8fc\ub2f9 \uc57d 35\uc2dc\uac04\uc744 \ucd08\uacfc\ud558\uba74 \ud589\ubcf5\ub3c4\uac00 \ub5a8\uc5b4\uc9c0\uae30 \uc2dc\uc791\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \uc774 “U” \ubaa8\uc591\uc73c\ub85c \uc778\ud574 \uc774\ub294 2\ucc28 \ud68c\uadc0\uac00 \ub450 \ubcc0\uc218 \uac04\uc758 \uad00\uacc4\ub97c \uc815\ub7c9\ud654\ud558\ub294 \ub370 \uc801\ud569\ud55c \ud6c4\ubcf4\uc77c \uac00\ub2a5\uc131\uc774 \ub192\ub2e4\ub294 \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \uc2e4\uc81c\ub85c 2\ucc28 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub824\uba74 numpy.polyfit()<\/a> \ud568\uc218\ub97c<\/span> \uc0ac\uc6a9\ud558\uc5ec 2\ucc28 \ub2e4\ud56d\uc2dd \ud68c\uadc0 \ubaa8\ub378\uc744 \ub9de\ucd9c \uc218 \uc788\uc2b5\ub2c8\ub2e4<\/span> . <\/p>\n
 import<\/span> numpy as<\/span> np\n\n#polynomial fit with degree = 2\n<\/span>model = np.poly1d(np.polyfit(hours, happ, 2))\n\n#add fitted polynomial line to scatterplot\n<\/span>polyline = np.linspace(1, 60, 50)\nplt.scatter(hours, happ)\nplt.plot(polyline, model(polyline))\nplt.show()<\/strong> <\/pre>\n
\"Python\uc758<\/p>\n
 \ubaa8\ub378 \uacc4\uc218\ub97c \uc778\uc1c4\ud558\uc5ec \uc801\ud569 \ub2e4\ud56d\uc2dd \ud68c\uadc0 \ubc29\uc815\uc2dd\uc744 \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 print<\/span> (model)\n\n-0.107x 2<\/sup> + 7.173x - 30.25\n<\/strong><\/pre>\n
 \uc801\ud569\ub41c 2\ucc28 \ud68c\uadc0 \ubc29\uc815\uc2dd\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ud589\ubcf5 = -0.107(\uc2dc\uac04) 2<\/sup> + 7.173(\uc2dc\uac04) \u2013 30.25<\/strong><\/span><\/p>\n
 \uc6b0\ub9ac\ub294 \uc774 \ubc29\uc815\uc2dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \uadfc\ubb34 \uc2dc\uac04\uc744 \uae30\uc900\uc73c\ub85c \uac1c\uc778\uc758 \uae30\ub300 \ud589\ubcf5 \uc218\uc900\uc744 \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc8fc\ub2f9 30\uc2dc\uac04 \uc77c\ud558\ub294 \uc0ac\ub78c\uc758 \uae30\ub300 \ud589\ubcf5 \uc218\uc900\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ud589\ubcf5 = -0.107(30) 2<\/sup> + 7.173(30) \u2013 30.25 = 88.64<\/strong> .<\/span><\/p>\n
 \uc608\uce21 \ubcc0\uc218\ub85c \uc124\uba85\ud560 \uc218 \uc788\ub294 \uc751\ub2f5 \ubcc0\uc218\uc758 \ubd84\uc0b0 \ube44\uc728\uc778 \ubaa8\ub378\uc758 R-\uc81c\uacf1\uc744 \uc5bb\uae30 \uc704\ud574 \uc9e7\uc740 \ud568\uc218\ub97c \uc791\uc131\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #define function to calculate r-squared<\/span>\ndef<\/span> polyfit(x, y, degree):\n    results = {}\n    coeffs = np.polyfit(x, y, degree)\n    p = np.poly1d(coeffs)\n    #calculate r-squared<\/span>\n    yhat = p(x)\n    ybar = np.sum(y)\/len(y)\n    ssreg = np.sum((yhat-ybar)**2)\n    sstot = np.sum((y - ybar)**2)\n    results['r_squared'] = ssreg \/ sstot\n\n    return<\/span> results\n\n#find r-squared of polynomial model with degree = 3\n<\/span>polyfit(hours, happ, 2)\n\n{'r_squared': 0.9092114182131691}\n<\/strong><\/pre>\n
 \uc774 \uc608\uc5d0\uc11c \ubaa8\ub378\uc758 R \uc81c\uacf1\uc740 0.9092<\/strong> \uc785\ub2c8\ub2e4.<\/span><\/p>\n
 \uc774\ub294 \ubcf4\uace0\ub41c \ud589\ubcf5 \uc218\uc900\uc758 \ubcc0\ub3d9 \uc911 90.92%\uac00 \uc608\uce21 \ubcc0\uc218\ub85c \uc124\uba85\ub420 \uc218 \uc788\uc74c\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h2>\n
 Python\uc5d0\uc11c \ub2e4\ud56d\uc2dd \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a>
 R\uc5d0\uc11c 2\ucc28 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a>
 Excel\uc5d0\uc11c 2\ucc28 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
2\ucc28 \ud68c\uadc0 \ub294 \uc2e4\uc81c \uad00\uacc4\uac00 2\ucc28(\uadf8\ub798\ud504\uc5d0\uc11c “U” \ub610\ub294 \ubc18\uc804\ub41c “U” \ubaa8\uc591)\uc77c \ub54c \uc608\uce21 \ubcc0\uc218\uc640 \uc751\ub2f5 \ubcc0\uc218 \uac04\uc758 \uad00\uacc4\ub97c \uc218\ub7c9\ud654\ud558\ub294 \ub370 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ud68c\uadc0 \uc720\ud615\uc785\ub2c8\ub2e4. \uc989, \uc608\uce21\ubcc0\uc218\uac00 \uc99d\uac00\ud558\uba74 \ubc18\uc751\ubcc0\uc218\ub3c4 \uc99d\uac00\ud558\ub294 \uacbd\ud5a5\uc774 \uc788\uc73c\ub098, \uc77c\uc815 \uc2dc\uc810 \uc774\ud6c4\uc5d0\ub294 \uc608\uce21\ubcc0\uc218\uac00 \uacc4\uc18d \uc99d\uac00\ud558\uba74\uc11c \ubc18\uc751\ubcc0\uc218\uac00 \uac10\uc18c\ud558\uae30 \uc2dc\uc791\ud55c\ub2e4. \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\uc5d0\uc11c 2\ucc28 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4. \uc608: Python\uc758 2\ucc28 \ud68c\uadc0 \uc8fc\ub2f9 \uadfc\ubb34 […]<\/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-981","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nPython\uc5d0\uc11c 2\ucc28 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95 - 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