{"id":981,"date":"2023-07-28T02:35:28","date_gmt":"2023-07-28T02:35:28","guid":{"rendered":"https:\/\/statorials.org\/ar\/%d8%a7%d9%84%d8%a7%d9%86%d8%ad%d8%af%d8%a7%d8%b1-%d8%a7%d9%84%d8%aa%d8%b1%d8%a8%d9%8a%d8%b9%d9%8a-%d8%a8%d9%8a%d8%ab%d9%88%d9%86\/"},"modified":"2023-07-28T02:35:28","modified_gmt":"2023-07-28T02:35:28","slug":"%d8%a7%d9%84%d8%a7%d9%86%d8%ad%d8%af%d8%a7%d8%b1-%d8%a7%d9%84%d8%aa%d8%b1%d8%a8%d9%8a%d8%b9%d9%8a-%d8%a8%d9%8a%d8%ab%d9%88%d9%86","status":"publish","type":"post","link":"https:\/\/statorials.org\/ar\/%d8%a7%d9%84%d8%a7%d9%86%d8%ad%d8%af%d8%a7%d8%b1-%d8%a7%d9%84%d8%aa%d8%b1%d8%a8%d9%8a%d8%b9%d9%8a-%d8%a8%d9%8a%d8%ab%d9%88%d9%86\/","title":{"rendered":"\u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062a\u0631\u0628\u064a\u0639\u064a \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646"},"content":{"rendered":"

<\/p>\n

\n

\u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062a\u0631\u0628\u064a\u0639\u064a<\/strong> \u0647\u0648 \u0646\u0648\u0639 \u0645\u0646 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u064a\u0645\u0643\u0646\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645\u0647 \u0644\u062a\u062d\u062f\u064a\u062f \u0627\u0644\u0639\u0644\u0627\u0642\u0629 \u0628\u064a\u0646 \u0645\u062a\u063a\u064a\u0631 \u0645\u062a\u0646\u0628\u0626 \u0648\u0645\u062a\u063a\u064a\u0631 \u0627\u0633\u062a\u062c\u0627\u0628\u0629 \u0639\u0646\u062f\u0645\u0627 \u062a\u0643\u0648\u0646 \u0627\u0644\u0639\u0644\u0627\u0642\u0627\u062a \u0627\u0644\u062d\u0642\u064a\u0642\u064a\u0629 \u062a\u0631\u0628\u064a\u0639\u064a\u0629\u060c \u0648\u0627\u0644\u062a\u064a \u0642\u062f \u062a\u0628\u062f\u0648 \u0645\u062b\u0644 “U” \u0623\u0648 “U” \u0645\u0642\u0644\u0648\u0628 \u0639\u0644\u0649 \u0627\u0644\u0631\u0633\u0645 \u0627\u0644\u0628\u064a\u0627\u0646\u064a.<\/span><\/p>\n

\u0623\u064a \u0623\u0646\u0647 \u0645\u0639 \u0632\u064a\u0627\u062f\u0629 \u0627\u0644\u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u0645\u062a\u0646\u0628\u0626\u060c \u064a\u0645\u064a\u0644 \u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u0627\u0633\u062a\u062c\u0627\u0628\u0629 \u0625\u0644\u0649 \u0627\u0644\u0632\u064a\u0627\u062f\u0629 \u0623\u064a\u0636\u064b\u0627\u060c \u0648\u0644\u0643\u0646 \u0628\u0639\u062f \u0646\u0642\u0637\u0629 \u0645\u0639\u064a\u0646\u0629 \u064a\u0628\u062f\u0623 \u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u0627\u0633\u062a\u062c\u0627\u0628\u0629 \u0641\u064a \u0627\u0644\u0627\u0646\u062e\u0641\u0627\u0636 \u0645\u0639 \u0627\u0633\u062a\u0645\u0631\u0627\u0631 \u0627\u0644\u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u0645\u062a\u0646\u0628\u0626 \u0641\u064a \u0627\u0644\u0632\u064a\u0627\u062f\u0629.<\/span><\/p>\n

\u064a\u0634\u0631\u062d \u0647\u0630\u0627 \u0627\u0644\u0628\u0631\u0646\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a \u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062a\u0631\u0628\u064a\u0639\u064a \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646.<\/span><\/p>\n

\u0645\u062b\u0627\u0644: \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062a\u0631\u0628\u064a\u0639\u064a \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646<\/span><\/strong><\/h2>\n

\u0644\u0646\u0641\u062a\u0631\u0636 \u0623\u0646 \u0644\u062f\u064a\u0646\u0627 \u0628\u064a\u0627\u0646\u0627\u062a \u0639\u0646 \u0639\u062f\u062f \u0633\u0627\u0639\u0627\u062a \u0627\u0644\u0639\u0645\u0644 \u0623\u0633\u0628\u0648\u0639\u064a\u064b\u0627 \u0648\u0645\u0633\u062a\u0648\u0649 \u0627\u0644\u0633\u0639\u0627\u062f\u0629 \u0627\u0644\u0645\u064f\u0628\u0644\u063a \u0639\u0646\u0647 (\u0639\u0644\u0649 \u0645\u0642\u064a\u0627\u0633 \u0645\u0646 0 \u0625\u0644\u0649 100) \u0644\u0640 16 \u0634\u062e\u0635\u064b\u0627 \u0645\u062e\u062a\u0644\u0641\u064b\u0627:<\/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
 \u0625\u0630\u0627 \u0642\u0645\u0646\u0627 \u0628\u0639\u0645\u0644 \u0645\u062e\u0637\u0637 \u0645\u0628\u0639\u062b\u0631 \u0628\u0633\u064a\u0637 \u0644\u0647\u0630\u0647 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u0646 \u0646\u0631\u0649 \u0623\u0646 \u0627\u0644\u0639\u0644\u0627\u0642\u0629 \u0628\u064a\u0646 \u0627\u0644\u0645\u062a\u063a\u064a\u0631\u064a\u0646 \u0639\u0644\u0649 \u0634\u0643\u0644 \u062d\u0631\u0641 “U”:<\/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
 \u0648\u0645\u0639 \u0632\u064a\u0627\u062f\u0629 \u0633\u0627\u0639\u0627\u062a \u0627\u0644\u0639\u0645\u0644\u060c \u062a\u0632\u062f\u0627\u062f \u0627\u0644\u0633\u0639\u0627\u062f\u0629 \u0623\u064a\u0636\u064b\u0627\u060c \u0648\u0644\u0643\u0646 \u0628\u0645\u062c\u0631\u062f \u0623\u0646 \u062a\u062a\u062c\u0627\u0648\u0632 \u0633\u0627\u0639\u0627\u062a \u0627\u0644\u0639\u0645\u0644 \u062d\u0648\u0627\u0644\u064a 35 \u0633\u0627\u0639\u0629 \u0623\u0633\u0628\u0648\u0639\u064a\u064b\u0627\u060c \u062a\u0628\u062f\u0623 \u0627\u0644\u0633\u0639\u0627\u062f\u0629 \u0641\u064a \u0627\u0644\u0627\u0646\u062e\u0641\u0627\u0636.<\/span><\/p>\n
 \u0648\u0628\u0633\u0628\u0628 \u0647\u0630\u0627 \u0627\u0644\u0634\u0643\u0644 “U”\u060c \u0641\u0647\u0630\u0627 \u064a\u0639\u0646\u064a \u0623\u0646 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062a\u0631\u0628\u064a\u0639\u064a \u0645\u0646 \u0627\u0644\u0645\u062d\u062a\u0645\u0644 \u0623\u0646 \u064a\u0643\u0648\u0646 \u0645\u0631\u0634\u062d\u064b\u0627 \u062c\u064a\u062f\u064b\u0627 \u0644\u0642\u064a\u0627\u0633 \u0627\u0644\u0639\u0644\u0627\u0642\u0629 \u0628\u064a\u0646 \u0627\u0644\u0645\u062a\u063a\u064a\u0631\u064a\u0646.<\/span><\/p>\n
 \u0644\u0625\u062c\u0631\u0627\u0621 \u0627\u0646\u062d\u062f\u0627\u0631 \u062a\u0631\u0628\u064a\u0639\u064a \u0641\u0639\u0644\u064a\u064b\u0627\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0645\u0644\u0627\u0621\u0645\u0629 \u0646\u0645\u0648\u0630\u062c \u0627\u0646\u062d\u062f\u0627\u0631 \u0645\u062a\u0639\u062f\u062f \u0627\u0644\u062d\u062f\u0648\u062f \u0628\u062f\u0631\u062c\u0629 2 \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645<\/span> \u0627\u0644\u062f\u0627\u0644\u0629<\/span> numpy.polyfit()<\/a> : <\/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
\"\u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631<\/p>\n
 \u064a\u0645\u0643\u0646\u0646\u0627 \u0627\u0644\u062d\u0635\u0648\u0644 \u0639\u0644\u0649 \u0645\u0639\u0627\u062f\u0644\u0629 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0645\u062a\u0639\u062f\u062f \u0627\u0644\u062d\u062f\u0648\u062f \u0627\u0644\u0645\u062c\u0647\u0632\u0629 \u0639\u0646 \u0637\u0631\u064a\u0642 \u0637\u0628\u0627\u0639\u0629 \u0645\u0639\u0627\u0645\u0644\u0627\u062a \u0627\u0644\u0646\u0645\u0648\u0630\u062c:<\/span><\/p>\n
 print<\/span> (model)\n\n-0.107x 2<\/sup> + 7.173x - 30.25\n<\/strong><\/pre>\n
 \u0645\u0639\u0627\u062f\u0644\u0629 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062a\u0631\u0628\u064a\u0639\u064a\u0629 \u0627\u0644\u0645\u062c\u0647\u0632\u0629 \u0647\u064a:<\/span><\/p>\n
 \u0627\u0644\u0633\u0639\u0627\u062f\u0629 = -0.107(\u0633\u0627\u0639\u0627\u062a) 2<\/sup> + 7.173(\u0633\u0627\u0639\u0627\u062a) \u2013 30.25<\/strong><\/span><\/p>\n
 \u064a\u0645\u0643\u0646\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0647\u0630\u0647 \u0627\u0644\u0645\u0639\u0627\u062f\u0644\u0629 \u0644\u062d\u0633\u0627\u0628 \u0645\u0633\u062a\u0648\u0649 \u0627\u0644\u0633\u0639\u0627\u062f\u0629 \u0627\u0644\u0645\u062a\u0648\u0642\u0639 \u0644\u0644\u0641\u0631\u062f \u0628\u0646\u0627\u0621\u064b \u0639\u0644\u0649 \u0633\u0627\u0639\u0627\u062a \u0639\u0645\u0644\u0647. \u0639\u0644\u0649 \u0633\u0628\u064a\u0644 \u0627\u0644\u0645\u062b\u0627\u0644\u060c \u0645\u0633\u062a\u0648\u0649 \u0627\u0644\u0633\u0639\u0627\u062f\u0629 \u0627\u0644\u0645\u062a\u0648\u0642\u0639 \u0644\u0644\u0634\u062e\u0635 \u0627\u0644\u0630\u064a \u064a\u0639\u0645\u0644 30 \u0633\u0627\u0639\u0629 \u0623\u0633\u0628\u0648\u0639\u064a\u0627\u064b \u0647\u0648:<\/span><\/p>\n
 \u0627\u0644\u0633\u0639\u0627\u062f\u0629 = -0.107(30) 2<\/sup> + 7.173(30) \u2013 30.25 = 88.64<\/strong> .<\/span><\/p>\n
 \u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u064a\u0636\u064b\u0627 \u0643\u062a\u0627\u0628\u0629 \u062f\u0627\u0644\u0629 \u0642\u0635\u064a\u0631\u0629 \u0644\u0644\u062d\u0635\u0648\u0644 \u0639\u0644\u0649 \u0645\u0631\u0628\u0639 R \u0644\u0644\u0646\u0645\u0648\u0630\u062c\u060c \u0648\u0647\u064a \u0646\u0633\u0628\u0629 \u0627\u0644\u062a\u0628\u0627\u064a\u0646 \u0641\u064a \u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u0627\u0633\u062a\u062c\u0627\u0628\u0629 \u0627\u0644\u062a\u064a \u064a\u0645\u0643\u0646 \u062a\u0641\u0633\u064a\u0631\u0647\u0627 \u0628\u0648\u0627\u0633\u0637\u0629 \u0627\u0644\u0645\u062a\u063a\u064a\u0631\u0627\u062a \u0627\u0644\u0645\u062a\u0648\u0642\u0639\u0629.<\/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
 \u0641\u064a \u0647\u0630\u0627 \u0627\u0644\u0645\u062b\u0627\u0644\u060c \u0645\u0631\u0628\u0639 R \u0644\u0644\u0646\u0645\u0648\u0630\u062c \u0647\u0648 0.9092<\/strong> .<\/span><\/p>\n
 \u0648\u0647\u0630\u0627 \u064a\u0639\u0646\u064a \u0623\u0646 90.92% \u0645\u0646 \u0627\u0644\u062a\u0628\u0627\u064a\u0646 \u0641\u064a \u0645\u0633\u062a\u0648\u064a\u0627\u062a \u0627\u0644\u0633\u0639\u0627\u062f\u0629 \u0627\u0644\u0645\u0628\u0644\u063a \u0639\u0646\u0647\u0627 \u064a\u0645\u0643\u0646 \u062a\u0641\u0633\u064a\u0631\u0647 \u0645\u0646 \u062e\u0644\u0627\u0644 \u0627\u0644\u0645\u062a\u063a\u064a\u0631\u0627\u062a \u0627\u0644\u0645\u062a\u0646\u0628\u0626\u0629.<\/span><\/p>\n
 \u0645\u0635\u0627\u062f\u0631 \u0625\u0636\u0627\u0641\u064a\u0629<\/strong><\/span><\/h2>\n
 \u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0645\u062a\u0639\u062f\u062f \u0627\u0644\u062d\u062f\u0648\u062f \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646<\/a>
 \u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062a\u0631\u0628\u064a\u0639\u064a \u0641\u064a R<\/a>
 \u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062a\u0631\u0628\u064a\u0639\u064a \u0641\u064a \u0625\u0643\u0633\u064a\u0644<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
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