{"id":3919,"date":"2023-07-14T18:26:54","date_gmt":"2023-07-14T18:26:54","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%d9%85%d9%83%d8%b9%d8%a8-%d8%a8%d9%8a%d8%ab%d9%88%d9%86\/"},"modified":"2023-07-14T18:26:54","modified_gmt":"2023-07-14T18:26:54","slug":"%d8%a7%d9%84%d8%a7%d9%86%d8%ad%d8%af%d8%a7%d8%b1-%d8%a7%d9%84%d9%85%d9%83%d8%b9%d8%a8-%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%d9%85%d9%83%d8%b9%d8%a8-%d8%a8%d9%8a%d8%ab%d9%88%d9%86\/","title":{"rendered":"\u0643\u064a\u0641\u064a\u0629 \u062a\u0646\u0641\u064a\u0630 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0645\u0643\u0639\u0628 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646"},"content":{"rendered":"

<\/p>\n

\n

\u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0645\u0643\u0639\u0628<\/strong> \u0647\u0648 \u0646\u0648\u0639 \u0645\u0646 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0630\u064a \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 \u0627\u0644\u062a\u0648\u0642\u0639 \u0648\u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u0627\u0633\u062a\u062c\u0627\u0628\u0629 \u0639\u0646\u062f\u0645\u0627 \u062a\u0643\u0648\u0646 \u0627\u0644\u0639\u0644\u0627\u0642\u0629 \u0628\u064a\u0646 \u0627\u0644\u0645\u062a\u063a\u064a\u0631\u0627\u062a \u063a\u064a\u0631 \u062e\u0637\u064a\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\u0643\u0639\u064a\u0628\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\u0645\u0643\u0639\u0628 \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 DataFrame \u0627\u0644\u0628\u0627\u0646\u062f\u0627 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0627\u0644\u062a\u064a \u062a\u062d\u062a\u0648\u064a \u0639\u0644\u0649 \u0645\u062a\u063a\u064a\u0631\u064a\u0646 (x \u0648 y):<\/span><\/p>\n

 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' x<\/span> ': [6, 9, 12, 16, 22, 28, 33, 40, 47, 51, 55, 60],\n                   ' y<\/span> ': [14, 28, 50, 64, 67, 57, 55, 57, 68, 74, 88, 110]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n     xy\n0 6 14\n1 9 28\n2 12 50\n3 16 64\n4 22 67\n5 28 57\n6 33 55\n7 40 57\n8 47 68\n9 51 74\n10 55 88\n11 60 110\n<\/strong><\/pre>\n
 \u0625\u0630\u0627 \u0642\u0645\u0646\u0627 \u0628\u0639\u0645\u0644 \u0645\u062e\u0637\u0637 \u062a\u0634\u062a\u062a \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 \u063a\u064a\u0631 \u062e\u0637\u064a\u0629:<\/span> <\/p>\n
 import matplotlib. pyplot<\/span><\/span> as plt<\/span>\n\n#create scatterplot\n<\/span>plt. scatter<\/span> (df. x<\/span> , df. y<\/span> )<\/span><\/span><\/strong> <\/pre>\n
\"\"<\/p>\n
 \u0643\u0644\u0645\u0627 \u0632\u0627\u062f\u062a \u0642\u064a\u0645\u0629 x\u060c \u062a\u0632\u062f\u0627\u062f y \u0625\u0644\u0649 \u0646\u0642\u0637\u0629 \u0645\u0639\u064a\u0646\u0629\u060c \u062b\u0645 \u062a\u0646\u062e\u0641\u0636\u060c \u062b\u0645 \u062a\u0632\u064a\u062f \u0645\u0631\u0629 \u0623\u062e\u0631\u0649.<\/span><\/p>\n
 \u0647\u0630\u0627 \u0627\u0644\u0646\u0645\u0637 \u0630\u0648 “\u0645\u0646\u062d\u0646\u064a\u064a\u0646” \u0641\u064a \u0627\u0644\u0645\u062e\u0637\u0637 \u0647\u0648 \u0625\u0634\u0627\u0631\u0629 \u0625\u0644\u0649 \u0648\u062c\u0648\u062f \u0639\u0644\u0627\u0642\u0629 \u0645\u0643\u0639\u0628\u0629 \u0628\u064a\u0646 \u0627\u0644\u0645\u062a\u063a\u064a\u0631\u064a\u0646.<\/span><\/p>\n
 \u0648\u0647\u0630\u0627 \u064a\u0639\u0646\u064a \u0623\u0646 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0645\u0643\u0639\u0628 \u0647\u0648 \u0645\u0631\u0634\u062d \u062c\u064a\u062f \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\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062a\u0643\u0639\u064a\u0628\u064a\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0645\u0644\u0627\u0621\u0645\u0629 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0645\u062a\u0639\u062f\u062f \u0627\u0644\u062d\u062f\u0648\u062f \u0645\u0639 \u0627\u0644\u062f\u0631\u062c\u0629 3 \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062f\u0627\u0644\u0629<\/span> numpy.polyfit()<\/a> :<\/span> <\/p>\n
 import<\/span> numpy as<\/span> np\n\n#fit cubic regression model\n<\/span>model = np. poly1d<\/span> (np. polyfit<\/span> (df. x<\/span> , df. y<\/span> , 3))<\/span>\n\n#add fitted cubic regression line to scatterplot\n<\/span>polyline = np. linspace<\/span> (1, 60, 50)\nplt. scatter<\/span> (df. x<\/span> , df. y<\/span> )\nplt. plot<\/span> (polyline, model(polyline))\n\n#add axis labels\n<\/span>plt. xlabel<\/span> (' x<\/span> ')\nplt. ylabel<\/span> (' y<\/span> ')\n\n#displayplot\n<\/span>plt. show<\/span> ()<\/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 \u0627\u0644\u062a\u0643\u0639\u064a\u0628\u064a \u0627\u0644\u0645\u062c\u0647\u0632\u0629 \u0645\u0646 \u062e\u0644\u0627\u0644 \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          3 2\n0.003302x - 0.3214x + 9.832x - 32.01\n<\/strong><\/pre>\n
 \u0645\u0639\u0627\u062f\u0644\u0629 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0645\u0643\u0639\u0628 \u0627\u0644\u0645\u062c\u0647\u0632\u0629 \u0647\u064a:<\/span><\/p>\n
 \u0635 = 0.003302(\u0633) 3<\/sup> – 0.3214(\u0633) 2<\/sup> + 9.832\u0633 – 30.01<\/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 \u0627\u0644\u0642\u064a\u0645\u0629 \u0627\u0644\u0645\u062a\u0648\u0642\u0639\u0629 \u0644\u0640 y \u0628\u0646\u0627\u0621\u064b \u0639\u0644\u0649 \u0642\u064a\u0645\u0629 x.<\/span><\/p>\n
 \u0639\u0644\u0649 \u0633\u0628\u064a\u0644 \u0627\u0644\u0645\u062b\u0627\u0644\u060c \u0625\u0630\u0627 \u0643\u0627\u0646\u062a x \u062a\u0633\u0627\u0648\u064a 30\u060c \u0641\u0625\u0646 \u0627\u0644\u0642\u064a\u0645\u0629 \u0627\u0644\u0645\u062a\u0648\u0642\u0639\u0629 \u0644\u0640 y \u0647\u064a 64.844:<\/span><\/p>\n
 \u0635 = 0.003302(30) 3<\/sup> \u2013 0.3214(30) 2<\/sup> + 9.832(30) \u2013 30.01 = 64.844<\/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<\/span> (x, y, degree)\n    p = np. poly1d<\/span> (coeffs)\n    #calculate r-squared<\/span>\n    yhat = p(x)\n    ybar = np. sum<\/span> (y)\/len(y)\n    ssreg = np. sum<\/span> ((yhat-ybar) **<\/span> 2)\n    sstot = np. sum<\/span> ((y - ybar) **<\/span> 2)\n    results[' r_squared<\/span> '] = ssreg \/ sstot\n\n    return<\/span> results\n\n#find r-squared of polynomial model with degree = 3\n<\/span>polyfit(df. x<\/span> , df. y<\/span> , 3)\n\n{'r_squared': 0.9632469890057967}\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 0.9632<\/strong> .<\/span><\/p>\n
 \u0648\u0647\u0630\u0627 \u064a\u0639\u0646\u064a \u0623\u0646 96.32% \u0645\u0646 \u0627\u0644\u062a\u0628\u0627\u064a\u0646 \u0641\u064a \u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u0627\u0633\u062a\u062c\u0627\u0628\u0629 \u064a\u0645\u0643\u0646 \u062a\u0641\u0633\u064a\u0631\u0647 \u0628\u0648\u0627\u0633\u0637\u0629 \u0627\u0644\u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u0645\u062a\u0646\u0628\u0626.<\/span><\/p>\n
 \u0648\u0628\u0645\u0627 \u0623\u0646 \u0647\u0630\u0647 \u0627\u0644\u0642\u064a\u0645\u0629 \u0639\u0627\u0644\u064a\u0629 \u062c\u062f\u064b\u0627\u060c \u0641\u0625\u0646\u0647\u0627 \u062a\u062e\u0628\u0631\u0646\u0627 \u0623\u0646 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0645\u0643\u0639\u0628 \u064a\u062d\u062f\u062f \u0627\u0644\u0639\u0644\u0627\u0642\u0629 \u0628\u064a\u0646 \u0627\u0644\u0645\u062a\u063a\u064a\u0631\u064a\u0646 \u0628\u0634\u0643\u0644 \u062c\u064a\u062f.<\/span><\/p>\n
 \u0645\u0635\u0627\u062f\u0631 \u0625\u0636\u0627\u0641\u064a\u0629<\/strong><\/span><\/h2>\n
 \u062a\u0634\u0631\u062d \u0627\u0644\u0628\u0631\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0643\u064a\u0641\u064a\u0629 \u062a\u0646\u0641\u064a\u0630 \u0627\u0644\u0645\u0647\u0627\u0645 \u0627\u0644\u0634\u0627\u0626\u0639\u0629 \u0627\u0644\u0623\u062e\u0631\u0649 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646:<\/span><\/p>\n
 \u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0627\u0644\u0628\u0633\u064a\u0637 \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 \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 \u0645\u062a\u0639\u062f\u062f \u0627\u0644\u062d\u062f\u0648\u062f \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0645\u0643\u0639\u0628 \u0647\u0648 \u0646\u0648\u0639 \u0645\u0646 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0630\u064a \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 \u0627\u0644\u062a\u0648\u0642\u0639 \u0648\u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u0627\u0633\u062a\u062c\u0627\u0628\u0629 \u0639\u0646\u062f\u0645\u0627 \u062a\u0643\u0648\u0646 \u0627\u0644\u0639\u0644\u0627\u0642\u0629 \u0628\u064a\u0646 \u0627\u0644\u0645\u062a\u063a\u064a\u0631\u0627\u062a \u063a\u064a\u0631 \u062e\u0637\u064a\u0629. \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\u0643\u0639\u064a\u0628\u064a \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646. \u0645\u062b\u0627\u0644: \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0645\u0643\u0639\u0628 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646 \u0644\u0646\u0641\u062a\u0631\u0636 \u0623\u0646 \u0644\u062f\u064a\u0646\u0627 DataFrame \u0627\u0644\u0628\u0627\u0646\u062f\u0627 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0627\u0644\u062a\u064a \u062a\u062d\u062a\u0648\u064a \u0639\u0644\u0649 \u0645\u062a\u063a\u064a\u0631\u064a\u0646 (x \u0648 y): import pandas as pd […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"yoast_head":"\n\u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0645\u0643\u0639\u0628 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646 - 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