{"id":1648,"date":"2023-07-25T12:50:55","date_gmt":"2023-07-25T12:50:55","guid":{"rendered":"https:\/\/statorials.org\/th\/%e0%b8%81%e0%b8%b2%e0%b8%a3%e0%b8%95%e0%b8%b4%e0%b8%94%e0%b8%95%e0%b8%b1%e0%b9%89%e0%b8%87%e0%b9%80%e0%b8%aa%e0%b9%89%e0%b8%99%e0%b9%82%e0%b8%84%e0%b9%89%e0%b8%87%e0%b8%ab%e0%b8%a5%e0%b8%b2%e0%b8%a1\/"},"modified":"2023-07-25T12:50:55","modified_gmt":"2023-07-25T12:50:55","slug":"%e0%b8%81%e0%b8%b2%e0%b8%a3%e0%b8%95%e0%b8%b4%e0%b8%94%e0%b8%95%e0%b8%b1%e0%b9%89%e0%b8%87%e0%b9%80%e0%b8%aa%e0%b9%89%e0%b8%99%e0%b9%82%e0%b8%84%e0%b9%89%e0%b8%87%e0%b8%ab%e0%b8%a5%e0%b8%b2%e0%b8%a1","status":"publish","type":"post","link":"https:\/\/statorials.org\/th\/%e0%b8%81%e0%b8%b2%e0%b8%a3%e0%b8%95%e0%b8%b4%e0%b8%94%e0%b8%95%e0%b8%b1%e0%b9%89%e0%b8%87%e0%b9%80%e0%b8%aa%e0%b9%89%e0%b8%99%e0%b9%82%e0%b8%84%e0%b9%89%e0%b8%87%e0%b8%ab%e0%b8%a5%e0%b8%b2%e0%b8%a1\/","title":{"rendered":"Curve fitting \u0e43\u0e19 python (\u0e1e\u0e23\u0e49\u0e2d\u0e21\u0e15\u0e31\u0e27\u0e2d\u0e22\u0e48\u0e32\u0e07)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u0e1a\u0e48\u0e2d\u0e22\u0e04\u0e23\u0e31\u0e49\u0e07\u0e17\u0e35\u0e48\u0e04\u0e38\u0e13\u0e2d\u0e32\u0e08\u0e15\u0e49\u0e2d\u0e07\u0e01\u0e32\u0e23\u0e1b\u0e23\u0e31\u0e1a\u0e40\u0e2a\u0e49\u0e19\u0e42\u0e04\u0e49\u0e07\u0e43\u0e2b\u0e49\u0e1e\u0e2d\u0e14\u0e35\u0e01\u0e31\u0e1a\u0e0a\u0e38\u0e14\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e43\u0e19 Python<\/span> <\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-16261 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/courbepython3.png\" alt=\"\" width=\"392\" height=\"265\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">\u0e15\u0e31\u0e27\u0e2d\u0e22\u0e48\u0e32\u0e07\u0e17\u0e35\u0e25\u0e30\u0e02\u0e31\u0e49\u0e19\u0e15\u0e2d\u0e19\u0e15\u0e48\u0e2d\u0e44\u0e1b\u0e19\u0e35\u0e49\u0e08\u0e30\u0e2d\u0e18\u0e34\u0e1a\u0e32\u0e22\u0e27\u0e34\u0e18\u0e35\u0e08\u0e31\u0e14\u0e40\u0e2a\u0e49\u0e19\u0e42\u0e04\u0e49\u0e07\u0e43\u0e2b\u0e49\u0e1e\u0e2d\u0e14\u0e35\u0e01\u0e31\u0e1a\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e43\u0e19 Python \u0e42\u0e14\u0e22\u0e43\u0e0a\u0e49\u0e1f\u0e31\u0e07\u0e01\u0e4c\u0e0a\u0e31\u0e19 <a href=\"https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.polyfit.html\" target=\"_blank\" rel=\"noopener\">numpy.polyfit()<\/a> \u0e41\u0e25\u0e30\u0e27\u0e34\u0e18\u0e35\u0e01\u0e32\u0e23\u0e01\u0e33\u0e2b\u0e19\u0e14\u0e27\u0e48\u0e32\u0e40\u0e2a\u0e49\u0e19\u0e42\u0e04\u0e49\u0e07\u0e43\u0e14\u0e17\u0e35\u0e48\u0e40\u0e2b\u0e21\u0e32\u0e30\u0e01\u0e31\u0e1a\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e21\u0e32\u0e01\u0e17\u0e35\u0e48\u0e2a\u0e38\u0e14<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u0e02\u0e31\u0e49\u0e19\u0e15\u0e2d\u0e19\u0e17\u0e35\u0e48 1: \u0e2a\u0e23\u0e49\u0e32\u0e07\u0e41\u0e25\u0e30\u0e41\u0e2a\u0e14\u0e07\u0e20\u0e32\u0e1e\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0e40\u0e23\u0e34\u0e48\u0e21\u0e15\u0e49\u0e19\u0e14\u0e49\u0e27\u0e22\u0e01\u0e32\u0e23\u0e2a\u0e23\u0e49\u0e32\u0e07\u0e0a\u0e38\u0e14\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e1b\u0e25\u0e2d\u0e21 \u0e08\u0e32\u0e01\u0e19\u0e31\u0e49\u0e19\u0e2a\u0e23\u0e49\u0e32\u0e07 Scatterplot \u0e40\u0e1e\u0e37\u0e48\u0e2d\u0e41\u0e2a\u0e14\u0e07\u0e20\u0e32\u0e1e\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n<span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n\n<span style=\"color: #008080;\">#createDataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">x<\/span> ': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],\n                   ' <span style=\"color: #ff0000;\">y<\/span> ': [3, 14, 23, 25, 23, 15, 9, 5, 9, 13, 17, 24, 32, 36, 46]})\n\n<span style=\"color: #008080;\">#create scatterplot of x vs. y\n<\/span>plt. <span style=\"color: #3366ff;\">scatter<\/span> (df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> )<\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-16259 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/courbepython1.png\" alt=\"\" width=\"399\" height=\"269\" srcset=\"\" sizes=\"auto, \"><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u0e02\u0e31\u0e49\u0e19\u0e15\u0e2d\u0e19\u0e17\u0e35\u0e48 2: \u0e1b\u0e23\u0e31\u0e1a\u0e2b\u0e25\u0e32\u0e22\u0e40\u0e2a\u0e49\u0e19\u0e42\u0e04\u0e49\u0e07<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0e08\u0e32\u0e01\u0e19\u0e31\u0e49\u0e19\u0e25\u0e2d\u0e07\u0e43\u0e2a\u0e48\u0e42\u0e21\u0e40\u0e14\u0e25\u0e01\u0e32\u0e23\u0e16\u0e14\u0e16\u0e2d\u0e22\u0e1e\u0e2b\u0e38\u0e19\u0e32\u0e21\u0e2b\u0e25\u0e32\u0e22\u0e15\u0e31\u0e27\u0e40\u0e02\u0e49\u0e32\u0e01\u0e31\u0e1a\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e41\u0e25\u0e30\u0e41\u0e2a\u0e14\u0e07\u0e20\u0e32\u0e1e\u0e40\u0e2a\u0e49\u0e19\u0e42\u0e04\u0e49\u0e07\u0e02\u0e2d\u0e07\u0e41\u0e15\u0e48\u0e25\u0e30\u0e42\u0e21\u0e40\u0e14\u0e25\u0e43\u0e19\u0e1e\u0e25\u0e47\u0e2d\u0e15\u0e40\u0e14\u0e35\u0e22\u0e27\u0e01\u0e31\u0e19:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n\n<span style=\"color: #008080;\">#fit polynomial models up to degree 5\n<\/span>model1 = np. <span style=\"color: #3366ff;\">poly1d<\/span> (np. <span style=\"color: #3366ff;\">polyfit<\/span> (df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> , 1))\nmodel2 = np. <span style=\"color: #3366ff;\">poly1d<\/span> (np. <span style=\"color: #3366ff;\">polyfit<\/span> (df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> , 2))\nmodel3 = np. <span style=\"color: #3366ff;\">poly1d<\/span> (np. <span style=\"color: #3366ff;\">polyfit<\/span> (df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> , 3))\nmodel4 = np. <span style=\"color: #3366ff;\">poly1d<\/span> (np. <span style=\"color: #3366ff;\">polyfit<\/span> (df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> , 4))\nmodel5 = np. <span style=\"color: #3366ff;\">poly1d<\/span> (np. <span style=\"color: #3366ff;\">polyfit<\/span> (df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> , 5))\n\n<span style=\"color: #008080;\">#create scatterplot\n<\/span>polyline = np. <span style=\"color: #3366ff;\">linspace<\/span> (1, 15, 50)\nplt. <span style=\"color: #3366ff;\">scatter<\/span> (df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> )\n\n<span style=\"color: #008080;\">#add fitted polynomial lines to scatterplot \n<\/span>plt. <span style=\"color: #3366ff;\">plot<\/span> (polyline, model1(polyline), color=' <span style=\"color: #ff0000;\">green<\/span> ')\nplt. <span style=\"color: #3366ff;\">plot<\/span> (polyline, model2(polyline), color=' <span style=\"color: #ff0000;\">red<\/span> ')\nplt. <span style=\"color: #3366ff;\">plot<\/span> (polyline, model3(polyline), color=' <span style=\"color: #ff0000;\">purple<\/span> ')\nplt. <span style=\"color: #3366ff;\">plot<\/span> (polyline, model4(polyline), color=' <span style=\"color: #ff0000;\">blue<\/span> ')\nplt. <span style=\"color: #3366ff;\">plot<\/span> (polyline, model5(polyline), color=' <span style=\"color: #ff0000;\">orange<\/span> ')\nplt. <span style=\"color: #3366ff;\">show<\/span> ()\n<\/span><\/span><\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-16260 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/courbepython2.png\" alt=\"\" width=\"408\" height=\"279\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">\u0e40\u0e1e\u0e37\u0e48\u0e2d\u0e1e\u0e34\u0e08\u0e32\u0e23\u0e13\u0e32\u0e27\u0e48\u0e32\u0e40\u0e2a\u0e49\u0e19\u0e42\u0e04\u0e49\u0e07\u0e43\u0e14\u0e17\u0e35\u0e48\u0e40\u0e2b\u0e21\u0e32\u0e30\u0e01\u0e31\u0e1a\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e21\u0e32\u0e01\u0e17\u0e35\u0e48\u0e2a\u0e38\u0e14 \u0e40\u0e23\u0e32\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e14\u0e39\u0e04\u0e48\u0e32 <a href=\"https:\/\/statorials.org\/th\/r-\u0e01\u0e33\u0e25\u0e31\u0e07\u0e2a\u0e2d\u0e07\u0e1e\u0e2d\u0e14\u0e35\/\" target=\"_blank\" rel=\"noopener\">R Square \u0e17\u0e35\u0e48\u0e1b\u0e23\u0e31\u0e1a\u0e41\u0e25\u0e49\u0e27<\/a> \u0e02\u0e2d\u0e07\u0e41\u0e15\u0e48\u0e25\u0e30\u0e23\u0e38\u0e48\u0e19\u0e44\u0e14\u0e49<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0e04\u0e48\u0e32\u0e19\u0e35\u0e49\u0e1a\u0e2d\u0e01\u0e40\u0e23\u0e32\u0e16\u0e36\u0e07\u0e40\u0e1b\u0e2d\u0e23\u0e4c\u0e40\u0e0b\u0e47\u0e19\u0e15\u0e4c\u0e02\u0e2d\u0e07\u0e01\u0e32\u0e23\u0e41\u0e1b\u0e23\u0e1c\u0e31\u0e19\u0e43\u0e19\u0e15\u0e31\u0e27\u0e41\u0e1b\u0e23\u0e15\u0e2d\u0e1a\u0e2a\u0e19\u0e2d\u0e07\u0e17\u0e35\u0e48\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e2d\u0e18\u0e34\u0e1a\u0e32\u0e22\u0e44\u0e14\u0e49\u0e14\u0e49\u0e27\u0e22\u0e15\u0e31\u0e27\u0e41\u0e1b\u0e23\u0e17\u0e33\u0e19\u0e32\u0e22\u0e43\u0e19\u0e41\u0e1a\u0e1a\u0e08\u0e33\u0e25\u0e2d\u0e07 \u0e1b\u0e23\u0e31\u0e1a\u0e15\u0e32\u0e21\u0e08\u0e33\u0e19\u0e27\u0e19\u0e15\u0e31\u0e27\u0e41\u0e1b\u0e23\u0e17\u0e33\u0e19\u0e32\u0e22<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define function to calculate adjusted r-squared\n<span style=\"color: #000000;\"><span style=\"color: #008000;\">def<\/span> adjR(x, y, degree):\n    results = {}\n    coeffs = np. <span style=\"color: #3366ff;\">polyfit<\/span> (x, y, degree)\n    p = np. <span style=\"color: #3366ff;\">poly1d<\/span> (coeffs)\n    yhat = p(x)\n    ybar = np. <span style=\"color: #3366ff;\">sum<\/span> (y)\/len(y)\n    ssreg = np. <span style=\"color: #3366ff;\">sum<\/span> ((yhat-ybar)**2)\n    sstot = np. <span style=\"color: #3366ff;\">sum<\/span> ((y - ybar)**2)\n    results[' <span style=\"color: #ff0000;\">r_squared<\/span> '] = 1- (((1-(ssreg\/sstot))*(len(y)-1))\/(len(y)-degree-1))\n\n    <span style=\"color: #008000;\">return<\/span> results<\/span>\n\n#calculated adjusted R-squared of each model\n<\/span>adjR(df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> , 1)\nadjR(df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> , 2)\nadjR(df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> , 3)\nadjR(df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> , 4)\nadjR(df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> , 5)\n\n{'r_squared': 0.3144819}\n{'r_squared': 0.5186706}\n{'r_squared': 0.7842864}\n{'r_squared': 0.9590276}\n{'r_squared': 0.9549709}\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0e08\u0e32\u0e01\u0e1c\u0e25\u0e25\u0e31\u0e1e\u0e18\u0e4c \u0e40\u0e23\u0e32\u0e08\u0e30\u0e40\u0e2b\u0e47\u0e19\u0e27\u0e48\u0e32\u0e41\u0e1a\u0e1a\u0e08\u0e33\u0e25\u0e2d\u0e07\u0e17\u0e35\u0e48\u0e21\u0e35 R-squared \u0e17\u0e35\u0e48\u0e1b\u0e23\u0e31\u0e1a\u0e2a\u0e39\u0e07\u0e2a\u0e38\u0e14\u0e04\u0e37\u0e2d\u0e1e\u0e2b\u0e38\u0e19\u0e32\u0e21\u0e14\u0e35\u0e01\u0e23\u0e35\u0e17\u0e35\u0e48 4 \u0e0b\u0e36\u0e48\u0e07\u0e21\u0e35 R-squared \u0e17\u0e35\u0e48\u0e1b\u0e23\u0e31\u0e1a\u0e41\u0e25\u0e49\u0e27\u0e40\u0e1b\u0e47\u0e19 <strong>0.959<\/strong><\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u0e02\u0e31\u0e49\u0e19\u0e15\u0e2d\u0e19\u0e17\u0e35\u0e48 3: \u0e40\u0e2b\u0e47\u0e19\u0e20\u0e32\u0e1e\u0e40\u0e2a\u0e49\u0e19\u0e42\u0e04\u0e49\u0e07\u0e2a\u0e38\u0e14\u0e17\u0e49\u0e32\u0e22<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0e2a\u0e38\u0e14\u0e17\u0e49\u0e32\u0e22\u0e19\u0e35\u0e49 \u0e40\u0e23\u0e32\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e2a\u0e23\u0e49\u0e32\u0e07\u0e1e\u0e25\u0e47\u0e2d\u0e15\u0e01\u0e23\u0e30\u0e08\u0e32\u0e22\u0e14\u0e49\u0e27\u0e22\u0e40\u0e2a\u0e49\u0e19\u0e42\u0e04\u0e49\u0e07\u0e02\u0e2d\u0e07\u0e41\u0e1a\u0e1a\u0e08\u0e33\u0e25\u0e2d\u0e07\u0e1e\u0e2b\u0e38\u0e19\u0e32\u0e21\u0e14\u0e35\u0e01\u0e23\u0e35\u0e17\u0e35\u0e48 4 \u0e44\u0e14\u0e49:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit fourth-degree polynomial\n<\/span>model4 = np. <span style=\"color: #3366ff;\">poly1d<\/span> (np. <span style=\"color: #3366ff;\">polyfit<\/span> (df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> , 4))\n\n<span style=\"color: #008080;\">#define scatterplot\n<\/span>polyline = np. <span style=\"color: #3366ff;\">linspace<\/span> (1, 15, 50)\nplt. <span style=\"color: #3366ff;\">scatter<\/span> (df. <span style=\"color: #3366ff;\">x<\/span> , df. <span style=\"color: #3366ff;\">y<\/span> )\n\n<span style=\"color: #008080;\">#add fitted polynomial curve to scatterplot\n<\/span>plt. <span style=\"color: #3366ff;\">plot<\/span> (polyline, model4(polyline), ' <span style=\"color: #ff0000;\">--<\/span> ', color=' <span style=\"color: #ff0000;\">red<\/span> ')\nplt. <span style=\"color: #3366ff;\">show<\/span> ()\n<\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-16261 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/courbepython3.png\" alt=\"\" width=\"392\" height=\"265\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">\u0e40\u0e23\u0e32\u0e22\u0e31\u0e07\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e23\u0e31\u0e1a\u0e2a\u0e21\u0e01\u0e32\u0e23\u0e02\u0e2d\u0e07\u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14\u0e19\u0e35\u0e49\u0e44\u0e14\u0e49\u0e42\u0e14\u0e22\u0e43\u0e0a\u0e49\u0e1f\u0e31\u0e07\u0e01\u0e4c\u0e0a\u0e31\u0e19 <strong>print()<\/strong> :<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #993300;\">print<\/span> (model4)\n\n          4 3 2\n-0.01924x + 0.7081x - 8.365x + 35.82x - 26.52\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0e2a\u0e21\u0e01\u0e32\u0e23\u0e02\u0e2d\u0e07\u0e40\u0e2a\u0e49\u0e19\u0e42\u0e04\u0e49\u0e07\u0e21\u0e35\u0e14\u0e31\u0e07\u0e19\u0e35\u0e49:<\/span><\/p>\n<p> <span style=\"color: #000000;\">y = -0.01924x <sup>4<\/sup> + 0.7081x <sup>3<\/sup> \u2013 8.365x <sup>2<\/sup> + 35.82x \u2013 26.52<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0e40\u0e23\u0e32\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e43\u0e0a\u0e49\u0e2a\u0e21\u0e01\u0e32\u0e23\u0e19\u0e35\u0e49\u0e40\u0e1e\u0e37\u0e48\u0e2d\u0e17\u0e33\u0e19\u0e32\u0e22\u0e04\u0e48\u0e32\u0e02\u0e2d\u0e07 <a href=\"https:\/\/statorials.org\/th\/\u0e15\u0e31\u0e27\u0e41\u0e1b\u0e23\u0e17\u0e35\u0e48\u0e2d\u0e18\u0e34\u0e1a\u0e32\u0e22\u0e01\u0e32\u0e23\u0e15\u0e2d\u0e1a\u0e2a\u0e19\u0e2d\u0e07\/\" target=\"_blank\" rel=\"noopener\">\u0e15\u0e31\u0e27\u0e41\u0e1b\u0e23\u0e15\u0e2d\u0e1a\u0e2a\u0e19\u0e2d\u0e07<\/a> \u0e15\u0e32\u0e21\u0e15\u0e31\u0e27\u0e41\u0e1b\u0e23\u0e17\u0e33\u0e19\u0e32\u0e22\u0e43\u0e19\u0e41\u0e1a\u0e1a\u0e08\u0e33\u0e25\u0e2d\u0e07 \u0e15\u0e31\u0e27\u0e2d\u0e22\u0e48\u0e32\u0e07\u0e40\u0e0a\u0e48\u0e19 \u0e16\u0e49\u0e32 <em>x<\/em> = 4 \u0e40\u0e23\u0e32\u0e01\u0e47\u0e08\u0e30\u0e17\u0e33\u0e19\u0e32\u0e22\u0e27\u0e48\u0e32 <em>y<\/em> = <strong>23.32<\/strong> :<\/span><\/p>\n<p> <span style=\"color: #000000;\">y = -0.0192(4) <sup>4<\/sup> + 0.7081(4) <sup>3<\/sup> \u2013 8.365(4) <sup>2<\/sup> + 35.82(4) \u2013 26.52 = 23.32<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u0e41\u0e2b\u0e25\u0e48\u0e07\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e40\u0e1e\u0e34\u0e48\u0e21\u0e40\u0e15\u0e34\u0e21<\/strong><\/span><\/h3>\n<p> <a href=\"https:\/\/statorials.org\/th\/\u0e01\u0e32\u0e23\u0e16\u0e14\u0e16\u0e2d\u0e22\u0e1e\u0e2b\u0e38\u0e19\u0e32\u0e21-1\/\" target=\"_blank\" rel=\"noopener\">\u0e04\u0e27\u0e32\u0e21\u0e23\u0e39\u0e49\u0e40\u0e1a\u0e37\u0e49\u0e2d\u0e07\u0e15\u0e49\u0e19\u0e40\u0e01\u0e35\u0e48\u0e22\u0e27\u0e01\u0e31\u0e1a\u0e01\u0e32\u0e23\u0e16\u0e14\u0e16\u0e2d\u0e22\u0e1e\u0e2b\u0e38\u0e19\u0e32\u0e21<br \/><\/a> <a href=\"https:\/\/statorials.org\/th\/\u0e2b\u0e25\u0e32\u0e21\u0e01\u0e32\u0e23\u0e16\u0e14\u0e16\u0e2d\u0e22\u0e1e\u0e2b\u0e38\u0e19\u0e32\u0e21\/\" target=\"_blank\" rel=\"noopener\">\u0e27\u0e34\u0e18\u0e35\u0e14\u0e33\u0e40\u0e19\u0e34\u0e19\u0e01\u0e32\u0e23\u0e16\u0e14\u0e16\u0e2d\u0e22\u0e1e\u0e2b\u0e38\u0e19\u0e32\u0e21\u0e43\u0e19 Python<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u0e1a\u0e48\u0e2d\u0e22\u0e04\u0e23\u0e31\u0e49\u0e07\u0e17\u0e35\u0e48\u0e04\u0e38\u0e13\u0e2d\u0e32\u0e08\u0e15\u0e49\u0e2d\u0e07\u0e01\u0e32\u0e23\u0e1b\u0e23\u0e31\u0e1a\u0e40\u0e2a\u0e49\u0e19\u0e42\u0e04\u0e49\u0e07\u0e43\u0e2b\u0e49\u0e1e\u0e2d\u0e14\u0e35\u0e01\u0e31\u0e1a\u0e0a\u0e38\u0e14\u0e02\u0e49\u0e2d\u0e21\u0e39 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-1648","post","type-post","status-publish","format-standard","hentry","category-3"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - 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class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/th\/%e0%b8%81%e0%b8%b2%e0%b8%a3%e0%b8%95%e0%b8%b4%e0%b8%94%e0%b8%95%e0%b8%b1%e0%b9%89%e0%b8%87%e0%b9%80%e0%b8%aa%e0%b9%89%e0%b8%99%e0%b9%82%e0%b8%84%e0%b9%89%e0%b8%87%e0%b8%ab%e0%b8%a5%e0%b8%b2%e0%b8%a1\/\",\"url\":\"https:\/\/statorials.org\/th\/%e0%b8%81%e0%b8%b2%e0%b8%a3%e0%b8%95%e0%b8%b4%e0%b8%94%e0%b8%95%e0%b8%b1%e0%b9%89%e0%b8%87%e0%b9%80%e0%b8%aa%e0%b9%89%e0%b8%99%e0%b9%82%e0%b8%84%e0%b9%89%e0%b8%87%e0%b8%ab%e0%b8%a5%e0%b8%b2%e0%b8%a1\/\",\"name\":\"Curve Fitting \u0e43\u0e19 Python (\u0e1e\u0e23\u0e49\u0e2d\u0e21\u0e15\u0e31\u0e27\u0e2d\u0e22\u0e48\u0e32\u0e07) - \u0e2a\u0e16\u0e34\u0e15\u0e34\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/th\/#website\"},\"datePublished\":\"2023-07-25T12:50:55+00:00\",\"dateModified\":\"2023-07-25T12:50:55+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/th\/#\/schema\/person\/7c9bb3d0f799d0f1b17883495e1de332\"},\"description\":\"\u0e1a\u0e17\u0e0a\u0e48\u0e27\u0e22\u0e2a\u0e2d\u0e19\u0e19\u0e35\u0e49\u0e08\u0e30\u0e2d\u0e18\u0e34\u0e1a\u0e32\u0e22\u0e27\u0e34\u0e18\u0e35\u0e1b\u0e23\u0e31\u0e1a\u0e40\u0e2a\u0e49\u0e19\u0e42\u0e04\u0e49\u0e07\u0e43\u0e2b\u0e49\u0e1e\u0e2d\u0e14\u0e35\u0e43\u0e19 Python 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