Commit f42cc00d by Paktalin

class 10 is done

parent 93ead254
......@@ -47,7 +47,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 5,
"metadata": {},
"outputs": [
{
......@@ -71,7 +71,7 @@
},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 6,
"metadata": {},
"outputs": [
{
......@@ -97,7 +97,7 @@
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
......@@ -107,7 +107,7 @@
},
{
"cell_type": "code",
"execution_count": 28,
"execution_count": 8,
"metadata": {},
"outputs": [
{
......
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"X = []\n",
"Y = []"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"for line in open('../csv_files/data_1d.csv'):\n",
" x, y = line.split(',')\n",
" X.append(float(x))\n",
" Y.append(float(y))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"X = np.array(X)\n",
"Y = np.array(Y)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Nu7vKKGXkUepGRI4TS08bGTkU6EXkOJF2h9IGISOXAr1ImhvM1oCRdofSytaRS4FeZAQZaNCOZWvAaLToKX2o143ICDGYHjKxNCGTkWsothIUkSHQNYq/6qqraGlzOo4ejHmXplh3hJL0pkAvkiIipWV67LV6+3YKL7yFQ88+zNGaZ4D+g7aqZwRURy+SEsLTMqXzymmpr2HxsuWMzsnq3q0JCDUWm7uU5qc3kl9+br9Bu6udAb3SPdrwI7Mo0IukgPDt9yAU0JmzhP3//C2mRki9tDXVd5c89hW01TJYIIbUjZk9aGYHzOylsGOrzOwVM9ttZtvNbHxwfLqZtZjZi8HXxmRevEi6iJRLbz/SiI3Ki5h6sZxRMZc8qnpGYsnRPwRc2OvYU8CZ7v5x4I/AN8Lue83dZwZfixJzmSLprbB4Em8/sJi6uy+hfuP11N9/LU3/dy2WfcJxjcWadqzmke8/0G/QHkz9vKSnflM37v6smU3vdeznYTd/A/x1Yi9LJHNUVW3h6AcdFF64tDuP3vjEWgr/Yhk5Y4to+Mk/0rhzHR3vNpBbUEL70YP9jsrDtwPMLSiluXhmKFcPGtFnoJjq6INAv8Pdz4xw30+BH7j7o8F5vyc0yn8XuMPdn+vv+VVHL5ksWq174851ZOWOoq1pL1mj8im4YDHZ+RP6rYGvqtrCtYtuofDi2z/c4HvnOsaUn0dBw4uqn08jQ7JnrJl9C2gHqoJD+4Cp7t5kZp8GfmxmH3P3dyM8diGwEGDq1KnxXIbIiNbVKTLcqJJyOg4foOiKu3qM8nO9nTUb1vf5fHdU3knhxbcfX6nz1EbqDtYn7XVI6hp0Hb2ZLQAuBio8+LPA3VvdvSn4+QXgNeD0SI93903uPsvdZxUXFw/2MkRGvGi17jnjJ/XYr7XoomWMPTG/39RLtEVSbc17VT+foQYV6M3sQuBrwCXufizseLGZZQc/zwDKgD2JuFCRdDX38+fTtGN1jwnXhsfvZtxZV/Y4b1RJOY373+73+aJ9cGSPGqPukxmq39SNmW0FzgOKzKwe+J+EqmxGAU+ZGcBvggqbc4A7zawd6AAWuXtzkq5dZMSrqtrCo/+ynTH/7QKan9pIW/Nesk4Yg3e2kzO2qMe5sa5ojbRIqmnHahZ++VpNxGYoNTUTGUbRJmI7frGe1k4G1MAsXFXVltAiqT2hRVIrK1coyKehWCdjFehFhlFWdjalt27Dsj/847pry75HHn5EwVr6NCRVNyISn2kzymipr+kxou9K0VRUzFdgl4RQ90qRIdR7tercz5+vLfsk6RToReIUa6uBSLs9Pfov27nqS5dqyz5JKuXoReIwkF2ftNuTJJomY0WGwECCd18Tr50dHUN2zZI+tJWgyAAMttPjQLbq025PMlwU6CUjhQf24klTuPGWW3vkzhcvW95nsO96PNmjYg7eKytXaOJVhoUCvWSc3pOih1qdk76wtEdfmb423Q5/fMHnFx/XLz5a8K6omM+Gtas08SpDTjl6yTi98+p1d1/C1Ntiz533fvzRmmc49NyjtB96h+kfOV0Lm2TIKEcvEkXvvHpuYcmAcue9H59ffi6n3LAByzJt1ScpSYFeMk7vSdFxsy+POf0S6fEQ/YNB2/lJKlCgl4zTe1I0O38Cud5O5zP3x5Q7j3VSNdICqf4meUWSQTl6yUjxdncMf3xh8SQsK4vG/W/3eC4tkJJkU1MzkT4MpmFYpA8HILQy9rM3UVpSTkt9Tfcm3NG2CKx77Pgae5FkUqAXiUF4q4PSeaGAfu2iW8jLgTEX9tyflaA0s6/OlCJDSTl6kRjcUXknY+Ys6VFrX3jx7bx39FjUlbFaICWpIqZAb2YPmtkBM3sp7FiBmT1lZrXB9wnBcTOzb5vZq2a228w+layLF+lPoqpeorU68LbWqBU4WiAlqSLWEf1DwIW9jn0d2OXuZcCu4DbAXEKbgpcBC4EN8V+myMAlsuol6obbJxXT8PjdUUftFRXzeb32FTo7OlRjL8MmpkDv7s8CvTf5/iKwOfh5MzAv7PjDHvIbYLyZTU7ExYoMRKR0y5g5S1i2/Gs9zotl1L+ycgVNO1b3COhNO9eR/7HPUjB2jEbtktLimYyd6O77ANx9n5mdHByfAuwNO68+OLYvjt8lMmDRql7e3PcWVVVbqKiYH3GStatqJjxYV1TM51e//jWbHryLjtZj5BaUMqb8PPzV57hXgV1SXDImYy3CseOK9c1soZlVm1l1Q0NDEi5DMkW0EXm0dEvO+EndDcuijfojNTS7/zvr2fzAJqafdjrtB+spaHhRo3cZEWJeMGVm04Ed7n5mcPsPwHnBaH4y8Et3P8PM/in4eWvv86I9txZMyWD1tcMTwDULb6b4kq9239e0cx3j/ryC5p330tnREX0zkDWX0dmpzUAktQ1FU7PHgQXBzwuAn4QdvyaovvkMcLivIC8Sj75G5BUV8ykYO4amJ+/jzTWX0fz0Rsafcw05Y4u6a9mjjfqzRuWpVYGkjVjLK7cCzwNnmFm9mV0P/ANwgZnVAhcEtwGeAPYArwLfBW5K+FWLBPrb4eneNavIyzUmXr6Sydd+m+z8CT2qYqJOsn7y4qj96EVGGvW6kREtln4y/fW1sawscgtKaWuuJ7ewhHGzL2fMGWdpL1dJeep1IxlhZeWKUJVMrxz9miBHD/33tZl+2ukRPyzUqkDShVogSMrrq849EatP1apA0p1SN5LS+qqqSWRZY7xti0WGQ6ypGwV6SWnq6S4SnfaMlbTQX1WNiPRPgV5S2kD2Zw2nvVpFPqRALykjUnAezESp9moV6Uk5ekkJ/bUyGMhEqfL6kik0GSsjSrTgfOBHf8+0aVMHVAUTtX+NFkBJmtFkrIwoUXdwam+lZdYCrl10C5aVFVO+fbB5fZF0pUAvKSFacM4tLOnenzW3oDSmfLsWQIn0pEAvKWFl5Qre/dm645qLjZt9ORAa3bc11/fZL76L9moV6Uk5ekkZxZOmcKjVaT/0DjnjJzH+7KvILz8XCOXrm5/eyCnX3698u0hAOXpJeb3LKRsP7OOUGzZQ9Je3g3eSnT8h4uhe+XaRgVH3ShkWkfZqzXlnNYd+tZUJ51wNQPPTG2lr2ovl5jH203/JmDPO6s63h3enFJG+aUQvCRXritRIO0MVXnw7R373U96v282YM86i4PxFjC2azKIbrqOg4UXl20UGSSN6SZhIo/TFy5YDHBeY6/bUUjovQjnlBy00/vguOltbmHZaGWsU1EXiNujJWDM7A/hB2KEZwApgPHAj0BAc/6a7P9HXc2kyNj0MZEVqtHObn95IwfmLtIpVJAZJn4x19z+4+0x3nwl8GjgGbA/uXtt1X39BXtJH1E6Trx3faTJSrXvXhKu6U4okVqJSN3OA19y9zswS9JQy0kybUUZLfU2PUXprfQ12Qh5VVVt6pGC6fr5u4SLaWt4jt7CU8edcQ375udrGTyTBEjUZewWwNez2EjPbbWYPmtmEBP0OSXErK1fQ+NNVx43ST/zUxSxb/rXjzq+omM/3N21kbNFkCs5f1KOqRqtYRRIn7gVTZnYC8DbwMXffb2YTgUbAgb8HJrv7lyM8biGwEGDq1Kmfrquri+s6JDWYZZFbWEpbcz25hSWMm305Y844izdXX4p7Z8THaBs/kcGJNUefiNTNXOB37r4foOt7cBHfBXZEepC7bwI2QWgyNgHXISnAckdTcMGi4yZZLXf0cef2DvCPPPyIArxIEiQi0F9JWNrGzCa7+77g5qXASwn4HTJCnJg3mgPb78I/OEZuQSl5p8/m6O//lSwLtQ/uGrEDMZdiikh84krdmNkYYC8ww90PB8ceAWYSSt28AfxtWOCPSOWV6aGqags33nIrJ31haffmIQ0/XUVn6zFO+u/zGH/Wld0biozOySLr3Ju0OYhIHLTxiAy5aLXxjTvWUHLz5h7H9v/zt5h6+3ZtDiISBzU1kyEXrY6+4+jB445Z7mhtDiIyRBToJW5d/W2803n7gcUcrXmm+77W+hpyxk3scX5rfQ2FBRO0OYjIEFGvG4lLeH+bqfNCefnGJ9binR3kjC2i8Ym1dLa18n7d7uibfj8WqrpRXxuR5FCOXiKKtba9r029c8YVM2725XhnB80/3wAdraqTF0mgoayjlzSTkC6U7a2ccv39QCjwT5s2VdU0IsNEOfo0Fmtv+N4i9YqPtk9rtE29c8ZNVO5dJEUo0KeprlF5y6wFlN66jZZZC1i8bHlMwT5qF8oIHSUjdaF892frGD/KtFGISIpQoE9TsYzKo434o43SI5U+VlTMZ8PaVeRVb+4O7N+97x4a3nmLzo4OXq99RUFeZJhpMjZNZWVnU3rrtqgLksLz8JGqYaLdp6Atkjo0GZvhovWG7xqVh4/4gdD3YMTfNWmq0keR9KDUTZqKlDsPnxTtLw9fUTGf12tfUfpFJA1oRJ+mugJztFF5fyN+EUkfGtGnsb5G5f2N+EUkfWhEn6H6G/GLSPrQiH6EGOzip74oDy+SGTSiHwEG0pJARKQ31dGPANEah2k3JpHMNmQbj5jZG2b2X2b2oplVB8cKzOwpM6sNvk+I9/dksoG0JOgtGSkfERlZEpWj/6y7zwz7ZPk6sMvdy4BdwW0ZhKqqLeSMzh/Ubkw33byEBTcs5I3X/kjOhBKai2fG3O9GRNJHsiZjvwh0bRK6GZiXpN+T1rpy83mfuIjGnesGVApZVbWF7z68laJ532LqbdspuGARx2p+iX3k7IhdKEUkfcWdozez14GDgAP/5O6bzOyQu48PO+egu0dN3yhHH1l4bv5ozTMcfv4HtDXtJTfvRL6/aWOfE7HR8vrNT22k/WC9NuAWSQNDuTn4We7+KWAucLOZnRPLg8xsoZlVm1l1Q0NDAi5jZOjOmWdlc0L+SVhWVtTceXhuPr/8XE65/n6m3rad9veP9lttEy2v39a8V6tfRTJM3IHe3d8Ovh8AtgN/Cuw3s8kAwfcDER63yd1nufus4uLieC9jROjRI/62bRRc8g2yTzo5au58IO2Ce4v22OxRY7T6VSTDxBXozSzfzMZ2/Qx8HngJeBxYEJy2APhJPL8nXdxReSf2kbNpfnojb665jOanN5Jffh4ttc9H3MEpnjYFkR7btGM1C798rWrvRTJMvAumJgLbzazruba4+5Nm9h/AY2Z2PfAm8KU4f09aeOO1P5LdcIiiuUu7+7w37lxHx+EDoXLJx3qWS8bTpiDSYx/aeJ+CvEgG0oKpIXRC/kkUXPKN4yZIG378vyme900tgBKRAdHGIymoveVoxAnSztajHNu1njXB7k4iIomkpmZDaNppkSdIc/NO1DZ9IpI0CvRDKNrkan818SIi8VDqZgipB7yIDAdNxoqIjFBDuTJWRERSmAK9iEiaU6BPEvWBF5FUocnYJNDWfyKSSjSij1OkkfsdlXcyZs4SRk/7OJadw+hpH2fMnCVcfd0NGt2LyJDTiD4O0Ubu7zW+Q+m841fAensrLbMWaHQvIkNKI/o4RBu55+RF3vovt7Ck+5xly782TFctIplGgT4OUTf3aHnv+BbBO9cxbvbl3ec07HtLKRwRGRIK9HGItrnH9NNOZ8PaVeRVb+bN1ZfS9OR9jD/nGvLLz+0+J2f8JO3dKiJDQoE+Dn1tDFJRMZ/Xa1/h0UcfpfODFrLzJ/Qc3Z91JXV7avv/JSIicVILhEHoqqyp21NL0cRT8M5OmhreYdqMsu4gH6540hQOtTrth/eTW1jCuNmXk50/Qf3nRSQuaoGQAJFKJ3vs+3rrNrLOvYnWTnjk4Ud4vfaViJU0965ZRV6uMfHylUy+9ttk50+IeUtAEZF4DXpEb2alwMPAJKAT2OTu68ysErgRaAhO/aa7P9HXc6XiiD68dLJr279ju9YzOieLrHNvOm6XqP5G5+F/BUQb+YuIDESsI/p4Av1kYLK7/y7YIPwFYB7wN8B77r461udKxUB/atlHaS6eSUvt87Q11ZNbWEJe2Wzeff4xpt6+Hcv+cAmCd7Sz957L6OzoGMYrFpFMk/StBN19H7Av+PmImb0MTBns86WaaBt5W+4oWutreozoW+trmDajbBivVkQkuoTk6M1sOvBJ4LfBoSWobXscAAAHSElEQVRmttvMHjSzCYn4HUMtN+9EiuYu7bEYqmjuUrLMolbaiIikorgDvZmdCPwI+Iq7vwtsAE4DZhIa8a+J8riFZlZtZtUNDQ2RThlWUTfybmvtrpHfe89l5FVv1n6vIpLS4iqvNLNcYAfwM3e/J8L904Ed7n5mX8+Tqjn6llkLBjzpKiIyVJJeXmlmBnwPeDk8yAeTtF0uBV4a7O8YTn0thhIRGUniSd2cBVwNfM7MXgy+LgLuNrP/MrPdwGeBZYm40GTrXTMPKEUjImkhnqqbfwMswl191synokjthq9ddAs3XnOl0jQiMuJpZSyR2w0XXnw7mx58SB0mRWTEy5hA39certHaDXe0HlOHSREZ8TJih6n+9nCdNqOMlgiLoHILStVhUkRGvLQb0Q9kD9eu0frKyhU07Vh93EYheafP1opXERnx0mpEH23kfqRxH1Mj7OFa91hotF5RMZ9f/frXbHrwLjpaj5FbUMqY8vPwV59j5dpVw/FSREQSJq1G9NFG7tmjIu/hWjTxlO7b939nPZsf2MT0006n/WA9BQ0vqpxSRNJCWm08kpWdTemt247rLPnm6kvJPqmIoouWfdig7Im1TMjLpeGdt+L+vSIiwyHp3StTUbRJVcsdzfizr6b56Y3dLYfHn301TTvvHcarFREZGmkV6FdWrghV0/TaLKSwYALZY4s45fr7u899v263JlpFJCOM6Bx9rG0L7l2zSn1rRCRjjdgRfbQKmw1rV0VtW3BH5Z3UPRbaym+NJlpFJEOM2MnYwbQR1r6tIpJOkt6meLhFa1sQbSVr118ALbMWUHrrNlpmLWDxsuXqZSMiaW/EBvppM8oi1sZHm2Dtb3WsiEi6GrGBfqAbgwz0LwARkXQxYidju3LrsU6wRquxV4mliKS7ETuih1Cwf732FTo7Oni99pU+J1a1NaCIZKqkjejN7EJgHZANPODu/5Cs3xWLgf4FICKSLpJSXmlm2cAfgQuAeuA/gCvdvSbS+YnqdSMikkmGu7zyT4FX3X2Pu38A/DPwxST9LhER6UOyAv0UYG/Y7frgmIiIDLFkBXqLcKxHjsjMFppZtZlVNzQ0JOkyREQkWYG+HigNu10CvB1+grtvcvdZ7j6ruLg4SZchIiLJCvT/AZSZ2almdgJwBfB4kn6XiIj0IWlNzczsIuBeQuWVD7r7XX2c2wDU9TpcBDQm5eJSn157ZtJrz0zxvPZp7t5vSiQluldGYmbVsZQNpSO9dr32TKPXntzXPqJXxoqISP8U6EVE0lwqB/pNw30Bw0ivPTPptWempL/2lM3Ri4hIYqTyiF5ERBIgJQO9mV1oZn8ws1fN7OvDfT3JZGalZvYLM3vZzH5vZkuD4wVm9pSZ1QbfJwz3tSaDmWWb2X+a2Y7g9qlm9tvgdf8gWIeRlsxsvJn90MxeCd7/2ZnwvpvZsuDf+ktmttXMRqfz+25mD5rZATN7KexYxPfZQr4dxL7dZvapRFxDygX6oPPld4C5QDlwpZmV9/2oEa0duM3d/wT4DHBz8Hq/Duxy9zJgV3A7HS0FXg67/Y/A2uB1HwSuH5arGhrrgCfd/aPAJwj9d0jr993MpgD/A5jl7mcSWmdzBen9vj8EXNjrWLT3eS5QFnwtBDYk4gJSLtCTYZ0v3X2fu/8u+PkIof/ZpxB6zZuD0zYD84bnCpPHzEqAvwAeCG4b8Dngh8Epafm6AczsJOAc4HsA7v6Bux8iA953Qvtg5JlZDjAG2Ecav+/u/izQ3OtwtPf5i8DDHvIbYLyZTY73GlIx0Gds50szmw58EvgtMNHd90HowwA4efiuLGnuBb4KdAa3C4FD7t4e3E7n934G0AB8P0hdPWBm+aT5++7ubwGrgTcJBfjDwAtkzvveJdr7nJT4l4qBvt/Ol+nIzE4EfgR8xd3fHe7rSTYzuxg44O4vhB+OcGq6vvc5wKeADe7+SeAoaZamiSTIRX8ROBU4BcgnlK7oLV3f9/4k5f+BVAz0/Xa+TDdmlksoyFe5+7bg8P6uP9mC7weG6/qS5CzgEjN7g1B67nOERvjjgz/pIb3f+3qg3t1/G9z+IaHAn+7v+/nA6+7e4O5twDbgz8ic971LtPc5KfEvFQN9RnW+DPLS3wNedvd7wu56HFgQ/LwA+MlQX1syufs33L3E3acTeo//1d0rgF8Afx2clnavu4u7vwPsNbMzgkNzgBrS/H0nlLL5jJmNCf7td73ujHjfw0R7nx8Hrgmqbz4DHO5K8cTF3VPuC7iI0J6zrwHfGu7rSfJr/XNCf5rtBl4Mvi4ilK/eBdQG3wuG+1qT+N/gPGBH8PMM4N+BV4F/AUYN9/Ul8XXPBKqD9/7HwIRMeN+B/wW8ArwEPAKMSuf3HdhKaD6ijdCI/fpo7zOh1M13gtj3X4Sqk+K+Bq2MFRFJc6mYuhERkQRSoBcRSXMK9CIiaU6BXkQkzSnQi4ikOQV6EZE0p0AvIpLmFOhFRNLc/wegbpAeAsTEcQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(X, Y, edgecolors='k')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"denominator = X.dot(X) - X.mean()*X.sum()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.9726121674845993"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = ( X.dot(Y) - Y.mean() * X.sum() ) / denominator\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2.8644240756601382"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b = ( X.dot(X) * Y.mean() - X.dot(Y) * X.mean() ) / denominator\n",
"b"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"Yhat = a*X + b"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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/7FwXRaS7RCsj3Fy9NSLAQ6B8wbHqrdT9YjnTPnNJa3usCpaN772rm7A9pMMcvZk9ClwM5JhZDfD3wMVmNplAWuZN4OsA7v4XM/sZsA1oBG5x96bEdF1EutrS8sXcNG8BTJ0bSK1Eudk6ZMoM6qte5PiKq0kfns/ASZ/h2V8/1/p8rAqWuaPGkFm5Xtv99QBtJSgiYW6+ZS6nrfshqxrDtwNZfvog7jzyAWPveApLPTFG9KbGwPTJpsCYLnRmTssNXZUySIx4txJUCQQRCfPAAz+IaCucvylQcGzzvbz320cZduH1rc+1zbVrs+7eR4FeRAKilBGe8IXFNE44DwMyCicx/PL51G36LpmF54SN1lesXB52Xsv8eekdFOhFJGqQN0th7LiPhc2ZHpBfQnNDvXLtfYwCvUh/FiXAV2yo4KZ5C0itb6Ah2raAZxSzq2p7d/ZSOknVK0X6qyhBHnfKymZx3d9ehR9voO6ZlWFTIg//arWmRPZBGtGL9DcxAnyoZ3/9HLlX3U3TkYMceO5Bju+vIW3ISIYOSFWapg9SoBfpL15/HSZOjGyPMsW6emcVBdNLsNQ0BpZcFDgsOI1S+h6lbkT6A7PIIO8eNchD9BWyKlnQdynQiySzj340MlWzZUvMAN9iaflijm5Zo5IFSUKpG5FkFSUXP27CRJbueZeyDk7VoqfkohG9SB8S136uZhFBPm/Gdymcv+mktgYsK5ul/VuThAK9SB8R136uUUbxeTOXkVF0TsytASX5KdCL9HIto/jrrruO+uNO05GDkUE7yiged1JSUqKWDNaGH/2LAr1ILxEtLRM6ih87/ymGX/oN3nvhEY5sex6ArNET2bXjtciLBW+2avaMgMoUi/QKsUr7ZqSlkHLRzWFlCI5Vb+XAcw/yYd1bkRdq8+9ZJYOTW7xlijWiF+kFom2/lzV1LrV73o5IvXx5/+7IID9jRtQpk2Vls1i7cjmZlevZfd/VZFauV5Dvh+LZYeph4HJgn7ufHWxbDnwe+BB4A/iyu79nZkXAq0DLZ8nfu/uNCei3SFJpWYkaqvH9OmxAZlhhsTfvuTzy5A4+latksHSYujGzC4EPgEdCAv1ngH9190YzuwfA3RcGA/3mluPipdSN9He5eWN4r8FpPLSX1MG50NxE0/v7SckagqWl03i4NvKkvXthxIio16uo2Mi8BQup3fM2lp7B8OxhrFqhkXyy6bIdptz9hWAAD237dcjD3wNfPNkOikhARcVGjnzYxPBLb23No9c9s5Lhn5tH2qAc3n3s7siT2hmgVVRs5GvfuJ3Bn72VsSHX++pNcwEU7PuhuG7GtjdSN7NfAI+7+4bgcX8BXgcOA4vc/T86ur5G9NKfjSs+i/rS2RE3XKMF+LyZy8isXN9uPfhY19v/y/vJzxmsWvJJpFtuxprZ/wEagYpg0x5grLt/FLgd2Ghmg2OcO8fMKs2ssrY2ysdSkX6iemdVxA3XaEE+bXAuh/7f8g7rzUS73oD8EhoP7dX8+X7qlGvdmNlsAjdpp3rwY4G7NwANwZ9fMrM3gDOBiOG6u68D1kFgRH+q/RDp63JGjm694RrtZmvRws2B46q30vz8Ax2mXgrHF1MfZWeotCEjyc+JOu6SJHdKI3ozuxRYCFzh7kdD2nPNLDX483igGNjZFR0VSUYVFRt5/4MjZG2+t90gD4FRed3edzq85tLyxRz+1eqwypN1z6wkremYqk/2U/FMr3wUuBjIMbMa4O+BbwEDgN9YYNl1yzTKC4ElZtYINAE3uvuBBPVdpM9bVL6E+vcPRrSnpA1gxBf/noyQtnhXtLaM+OctWMhbYbNuVulGbD+llbEiPeUzn4Hf/Cas6etX3c0vzziPt1ZcxaCcUVrRKu3qsumVIpIAUapMtqRpGqq3UnTGmSwtX6x68NIlFOhFulOUAD84ZxRZl3yDAU2NrSP3lqCuwC5dQbVuRDoprs1AIHqQzx3NdddcrVo0klAa0Yt0Qmh1yILpJdTXbOOmeQuAkBWo7aRpsqq38uyv218AJdJZuhkr0gmxVqFmVq5n12vbIDU14pzQKZPe1Mju+66muampW/oryUVlikW6QaxVqLt2vBYR5MdNmEjezGVhbdoERLqDAr30S23z6jffMje+PHub80kdELaD0+WvvkD1vdPDD/7858GdpeWLObplTdhCpqNb1mgRkySeu/f417nnnusi3WXDhgoflDvaR85c5mPnb/LBU2Z46sBhrY9Hzlzmg3JH+4YNFR2eP/xzt3vq4FwfOXOZe6CmZPhXlHOLJkx0S0nxogkTY/4OkXgAlR5HjFWOXvqdtnn1dx66mexLboyeZ49yk7Tt+VE3A6mpgTFjEvMCRIKUoxeJoW1e/fj+mqh59liVHkPPj7njk4K89CIK9NLvFI4vDsurpw/PD3sM7d8kLRxfTPW90yOC/LgJE6Nuzn0yuX+RRFCgl36n7U3RzOIp1P1iedw3SXfteC2ibXDu6IjjW+bY15fOpuD2J6kvnc1N8xYo2Eu3U45e+qWKio2BOjI7A3Vkpn3mEp799XOtj5eWL45cnRpl4VNKSkrM49udY68FUtIF4s3RK9CLdKSuDnJzI9uD/3ba/tFoCfopqakU3P4klnpiAboWSElXUvVKka4QZRRvWKDGe94Y/vYLV7Hh509FLYEQa6cnLZCS7qYcvUg08+ZFBPlrBg4hb+Yyxs5/ihFf+DYH64/z4A9/RNbUuWQUTsJS08gonETW1LksKl+iBVLSa2hEL9JWlFH8uAkTw/LtGYWTyLlsHvueWBJ9aubPqlpz9qopLz0trhG9mT1sZvvM7JWQtmwz+42ZVQW/Dwu2m5l938x2mNlWM/tYojov0pGTmt5oFhnkm5rAPWZNGz/e0O7UzLKyWeyq2k5zUxO7qrYryEuPiDd18xPg0jZtdwFb3L0Y2BJ8DDCNwKbgxcAcYG3nuyly8k5qemOUUTzukBL4J9J27j0EAnrq6dns33yv0jPSq8UV6N39BaDtJt9XAuuDP68Hpoe0PxIsxfB7YKiZjeqKzoqcjEXlS6Lmz+ctWHjioCij+IoNFRELn5aWL+bwr1aHBfS6Z1aSbs187YZrtXGI9GqdydGPdPc9AO6+x8xGBNvHALtDjqsJtu0JPdnM5hAY8TN27NhOdEMkuuqdVRRMj0y3vLXnbSo2VFB2/XUR5+TNXMbRthuHhPw8b8FC3trzdmDWTfYwVq1YpaAuvV4iZt1E+QxMxGR9d1/n7qXuXpobbY6ySJxi5eFjpVscjwjyRQs3U7Rwc/RRf1BZ2Sz2vbMb92aaPzxK7btvK8hLn9CZQL+3JSUT/L4v2F4DFIQclw+804nfIxJTe3n4peWLqX36e63plrNf+gXvPnZ32PnrgML5m8LaBuSXULvnbZUqkKQR98pYMysCNrv72cHHy4H97v6PZnYXkO3ud5rZ54C5wGXAx4Hvu/t57V1bK2PlVHVUZiA3bwzvNTjH39sTebJ7zPP3//J+8nMGq1SB9GpdWqbYzB4FXgQmmlmNmX0F+Efg02ZWBXw6+BjgGWAnsAP4IXDzKfRfJC6xpj22lBh+wyKD/MTskYEbrhAx6j9WvZX9z65myAXXxixTLNLXqNaN9GntjuijVJkcN2FiRAGyllF/46G9pA/PZ8iUGaQOHKbiY9LraeMRSRrtLXqKVmbg3cfujgzywc39oi1aWrViOZnpxsgZSxn1pe+TOnCY5sJLUlEJBOnVWm62RisaVlY2K6LMQHNzc+RFOvjUqlIFkuyUupFeLe6a7rFWtookMaVuJCl0dLOVQ4cU5EU6oEAvvVqsRU+F44sDAX7o0PATgrl47dUqcoICvfQa0YJztJuttzz9D5E3Wx9/PGzHJ+3VKnKCcvTSK4TedB2QX0JDzTaOblnD2pXLAVq36ovnZqv2apX+QnvGSp8SKzjve+I7FBaOjTonnuPHIS1y4pj2apX+QjdjpU+JubFHY0P0IO8eNchDB3l9kX5IgV56hWjBufre6biHp2ryZi5jcO7odvPt2qtVJJwCvfQKbTf2ePOeyyOOMUsJ23w7lrKyWaxduVybgYgEKUcvvUZu3hhq90ZWtC5auJlj1Vs58NyDjP7KA8q3iwQpRy+9Xuh0yisLCiOC/Kaxf03h/E0nKkpOmQEo3y5yslTrRnpE6HTK5h13Q81bYc/nfn4Bh158nOMrrsLSMxl07ufJmnhBa759RXDapYh0TCN66VLxrkhdVL6E/2wkYsen4tMyyJu5jKyJF5B9yY0MyhnFjV/9Mtm1LyvfLnKKNKKXLtNRpclQ0aZMFs7fxFsrriJ103dpbqin8AxVkRTpCqd8M9bMJgKPhzSNBxYDQ4GvAbXB9rvd/Zn2rqWbsckhrhWpUQqQFS3c3HrsgeceJPuSG7WKVSQOCb8Z6+6vuftkd58MnAscBZ4KPr2y5bmOgrwkj5iVJt8IVpqMEuTzZi4L38Jvyozw6pQi0mldlbqZCrzh7tUWrWSs9AuF44upr9kWNqJvqNlGszdHBvlghckDc27keP0HpA8vYOiFNzCw5CKOVW/VrBqRLtRVN2NnAo+GPJ5rZlvN7GEzG9ZFv0N6uaXli6n7xfLWRU/2RmXEzVagtQhZWdksfrzuQQbljCL7khvDZtVoFatI1+n0gikzOw14B/iIu+81s5FAHeDAd4BR7v53Uc6bA8wBGDt27LnV1dWd6of0DmYppA8v4MP9b0U+GeP/tYqKja3VKQvHF0ds3i0i0XXngqlpwJ/dfS+Au+919yYPFCn5IXBetJPcfZ27l7p7aW5ubhd0Q3qDv0tNiwjyN3/iGlLSMyKmXLZMxbz+husB+OkjP426ebeIdE5X5OivJSRtY2aj3H1P8OFVwCtd8DukLzDjoTZNQ6bM4Mhf/hVLzyD/1sdap1z+9ne/Y8PPn4prKqaIdE6nUjdmlgXsBsa7+6Fg20+ByQRSN28CXw8J/FFpemUfF+UG/IR5P+eDPVXU/mI5zR8eI+fSuQwsuQgITqN8+h/IvuJb2hxEpBPiTd10akTv7keB4W3aru/MNaWPaWdefEbhJHI/v4C6Z1a1BnkITLk8fvSD6FMxf6ZplSJdTSUQ5NSYRQT5lJQUCudvCmsbkF9C0/u1YW0NNdtIzzpdm4OIdBMFejl5UUbx4yZMxJudd350E0e2Pd/a3lCzDTstK2ITkK9+6QZtDiLSTVTrRuIXJcBXbKgI1Lcpnc3Y6YFNveueWYk3N5E2KIf9z65m4F9dxL4nvgNNDRSOP1G/5oLzzw9Mq/xZVVi7iHQtbTwiUYXObb8wv5B/f2tX+AHXXw+PPNLupt5pQ3IZMmUGqQOH6SarSAJo4xE5ZS1VKOtLZ9Pc3BwZ5N3hkUeA9jb1PsaoL32f1IHDlJIR6WEK9Eks3trwbS0qX8L9g/MjyhdcPHZcxOrWaJt6N9RsIz3zdNWPF+klFOiTVOiovOD2J6kvnc1N8xaEBftYfwh27XiN2W/8Mex6hfM38UJNZJmKpeWLo95U/fG6B2luatJKV5FeQDn6JNVRbfjQTUIG5AduokYrQBZaKz5Wnl21akR6Rrw5egX6JJWSmkrB7U9iqScmVnlTI7vvu5rmpqaIPwRv3nN5xDXyZi5r/SNwdMsapWBEepluWRkrvVes2vAtC5Kqd1ZRML0kaoBvqRWfqamPIklBOfokFSt33jL7ZcK4CVTfOz3snEZLYdyEiUCgsNiuqu3Ks4skAY3ok1RLYI66IMmM19scnzdzWSA9o2mQIklHI/okFjEqzx4Wsbr1przRpKSkaBqkSBJToO8jTnVOfCszuOyy8DZ31u55W+kZkSSn1E0fEDoV8qQ36TjvPPjTn8Lb6ushIyNBvRWR3kbTK/uAjubExxSlCFmsfVtFpO9RrZskEqueTPXOGJt0RKkVj7uCvEg/1elAb2Zvmtn/mNnLZlYZbMs2s9+YWVXw+7DOd7V/qqjYSFrGwPg36YgyijeMlNOyyM0bc/K5fRHp87pqRP+/3X1yyEeIu4At7l4MbAk+lpPUkpvPPOcy6p5d3f4mHVFG8VnDRpI3cxlj5z/FiC98m4P1x/nqTXMV7EX6mU7n6M3sTaDU3etC2l4DLnb3PWY2Cvh3d59cximOAAANJUlEQVQY6xrK0UcXmps/su15Dr34OMf37yY983R+vO7BwI3YvXshLy/8xAULGPfU01Hz+vt/eT/5OYNVG14kCXRbrRsz2wUcBBz4J3dfZ2bvufvQkGMOuvuwNufNAeYAjB079tzq6sjKiP1dR/Vq2rvZGuvct1ZcjRmB80WkT+vOm7EXuPvHgGnALWZ2YTwnufs6dy9199Lc3Nwu6Ebf0DofPiWV0wYOxlJSYs6Lj1Xrfemw4ZFBfufOsJutsc5NGzJSG3CL9DOdDvTu/k7w+z7gKeA8YG8wZUPw+77O/p5kEFYj/o4nyb7iW6QOHsGB3MkRteIher2adx+7m7v314Zf2B3GjYs49/CvwvP6dc+sJK3pmHZ7Eulv3P2Uv4CBwKCQn38HXAosB+4Ktt8FfK+965x77rneHxRNmOiDp8zw9JyxjqV4es7Y1scjZy7zogkTI87ZsKEi0H5iguSJrw5s2FDhuaPyHcwtPdNzRo72DRsqEvHSRKQHAJUeR6zuVI7ezMYTGMVDYJXtRnf/rpkNB34GjAXeAv7W3Q/Euk5/uRlrKSmkDh5BzrRbW+u81z27mqZD+xg7/6kTufeIE7XwSUQidUs9enffCZwTpX0/MLUz105G6Zmnkz3t1taZMBmFk8iZdiu1m5ZFnxevAC8iXUArY7tRY/2RqCtcmxuOhM+LjzajZvJkBXkROSUqataNCs+IvutTeubpJ0oEaxQvIl1MI/puFGvXpx+ve5CyM4sjg/zmzQryItJpGtF3o5i7Pl1XFnmwAryIdBGN6LtZ2K5P5388MsgfPaogLyJdSiP6nqJcvIh0E43oEyTm1n+qFS8i3UyBPgHCSh3c/iT1pbMDW/9pFC8iPUCBvpOijdwXlS8ha+pcMgonYalpvPvY3RyufSf8RI3iRaSbKNB3QqyRe/Ubga3/BjYc5c17Lg875zvnXMrg3NHa/ENEuo02B++EWJt2H3j6H/jw6PsRx5+WM5bRX3mAY9VbaX7+Afa9s7s7uysiSUabg3eDaJt2XwARQf6cK+8ifchIhkyZAQTKHtTueVujehHpFgr0ndB2c48377mcJx67O+wYw9j2/HqGXngDA0suAoIbgAzNY1H5km7tr4j0Twr0ndBS0uD/PrEkIhdPczO4s2HDBpo/rCd14LDWsgf7n13NkAuupXpnVc90XET6FeXoT0HLzJrqnVU0NzdHHtDmv2lu3hjea3AaD+0lfXg+Q6bMIHXgMDIr12uTbhE5ZcrRd4FoUydbZtrs2vFaZJCPMWVy1YrlZKYbI2csZdSXvk/qwGHhZYlFRBLolEsgmFkB8AiQBzQD69x9tZmVA18DWjY2vdvdn+lsR7tbS0DPmjqXgukl1Nds46Z5C8hItYg58WvOupAVjXvZFeNaMYuZBdtFRBLplFM3wU2/R7n7n81sEPASMB24BvjA3e+N91q9MXUzrvgsDuROpr7qRY7vryF9eD4f1r0VcVzRws14U2PsbQBFRBIk4VsJuvseYE/w5/fN7FVgzKler7d5843XSa19j5xpt1IwNI8/Pfh3Yc9f+uX72T5iHED0bQBFRHqJLqleaWZFwEeBPxCYSj7XzG4AKoE73P1gV/ye7tSyv+u7baZLAgzOHU1W/fsMaGqkoWYbR7esYcXK5T3QSxGRjnX6ZqyZnQ48Adzm7oeBtcAZwGQCI/4VMc6bY2aVZlZZW1sb7ZAedcnRDyKCfPFtPyfFUli7cjmZlevZfd/VZFauP7ENoIhIL9Sp6ZVmlg5sBn7l7vdFeb4I2OzuZ7d3nV6Xo49SZbJo4WaOVW/VlEgR6TUSPr3SzAx4CHg1NMgHb9K2uAp45VR/R7crL48I8nkzl1E4f1Pr/q6aEikifU1ncvQXANcD/2NmLwfb7gauNbPJgANvAl/vVA+7S5sA//rUS/jTl79MpqZEikgf15lZN/8JRNlJg741Z/7KK+Hpp8Oa8mYuY//me/naxIlK04hIn9d/94w9fhxOOy2sadqXv8+rI8aTAQy/fD7rHv4uF5x/vkbxItKn9ZsSCKHlDDCLCPKF8zfx6ojxrY8H5JfQ1HBUFSZFpM/rF4G+tZzB2V+IrE9z6BDjJkwMKzcMgUVQ6dkFqjApIn1e0gX6WHu4/pIB/GXTstbjdp4+nHETJsLgwSwtX8z+zfdyrHprWCnhzDOnaMWriPR5SZWjj1aIbPk357HrwL6w44ru/AXe3MTu+64GAkXHfvu737Hu4e/S1HCU9OwCskouxnf8B0u14lVE+rikGtEvKl9C1tS5ZBROwlLTePexu3k5JMhfd813KFq4GcxoqNlGzsjRrc898IM1rP/ROorOOJPGgzVk176sFa8ikhSSakRfvbOKguklnP/my2x8fFHYc2mDc8lJSW2tT1P3zEqGZaaHHVNWNkuBXUSSTlIF+sJxE1j1+Le5cvf/tLZN+dzt/OHXa8n+5PUceO7B1pLDQz95PfufXdWDvRUR6R7JE+j/+Ed2vfF668Pfjv1rvnD+tRzdsobh2cNIHZTD6K880Pr8seqtutEqIv1Cn87RV1Rs5IwJE3nZDD7+cQDqhwxh4vhiPlnzl9bKkqtWLOfoljVhs2pUt0ZE+os+O6KvqNjI3Nvmc7BuT2vblUOyueYH9/NajDy7tvITkf6oz47oF5UvYdjFX2f3kJH8vuBsxt35NH+YNj/mStayslksLV9M4fhiqndWsah8CRUVG7u51yIi3a/PjugDM2wm88kbH2ptG5BfQvXPoq9kjbXZN6CRvYgktT47oi8cXxy1bEGsG6xt59hnFE4ia+pc1bIRkaTXZwP90vLFJ3WDtXpnFQPyS8LaBuSXqJaNiCS9Ppu6aUm3xHuDtXB8MfU128gonNTa1t4nABGRZNFnR/QQCPa7qrbT3NTErqrt7ebaT/YTgIhIskjYiN7MLgVWA6nAj9z9HxP1u+Jxsp8ARESShbl711/ULBV4Hfg0UAP8CbjW3bdFO760tNQrKyu7vB8iIsnMzF5y99KOjktU6uY8YIe773T3D4HHgCsT9LtERKQdiQr0Y4DdIY9rgm2tzGyOmVWaWWVtbW2CuiEiIokK9BalLSxH5O7r3L3U3Utzc3MT1A0REUlUoK8BCkIe5wPvJOh3iYhIOxIV6P8EFJvZODM7DZgJPJ2g3yUiIu1IyKwbADO7DFhFYHrlw+7+3XaOrQWq2zTnAHUJ6Vzvp9feP+m190+dee2F7t5h7jthgb6zzKwynmlDyUivXa+9v9FrT+xr79MrY0VEpGMK9CIiSa43B/p1Pd2BHqTX3j/ptfdPCX/tvTZHLyIiXaM3j+hFRKQL9MpAb2aXmtlrZrbDzO7q6f4kkpkVmNm/mdmrZvYXM7s12J5tZr8xs6rg92E93ddEMLNUM/svM9scfDzOzP4QfN2PB9dhJCUzG2pm/2xm24Pv/5T+8L6b2bzg/+uvmNmjZpaRzO+7mT1sZvvM7JWQtqjvswV8Pxj7tprZx7qiD70u0AcrX/4AmAaUANeaWUn7Z/VpjcAd7v5XwCeAW4Kv9y5gi7sXA1uCj5PRrcCrIY/vAVYGX/dB4Cs90qvusRr4pbufBZxD4L9DUr/vZjYG+CZQ6u5nE1hnM5Pkft9/Alzapi3W+zwNKA5+zQHWdkUHel2gp59VvnT3Pe7+5+DP7xP4xz6GwGteHzxsPTC9Z3qYOGaWD3wO+FHwsQGfAv45eEhSvm4AMxsMXAg8BODuH7r7e/SD953APhiZZpYGZAF7SOL33d1fAA60aY71Pl8JPOIBvweGmtmozvahNwb6DitfJiszKwI+CvwBGOnueyDwxwAY0XM9S5hVwJ1Ac/DxcOA9d28MPk7m9348UAv8OJi6+pGZDSTJ33d3fxu4F3iLQIA/BLxE/3nfW8R6nxMS/3pjoO+w8mUyMrPTgSeA29z9cE/3J9HM7HJgn7u/FNoc5dBkfe/TgI8Ba939o8ARkixNE00wF30lMA4YDQwkkK5oK1nf944k5N9Abwz0/a7ypZmlEwjyFe7+ZLB5b8tHtuD3fT3VvwS5ALjCzN4kkJ77FIER/tDgR3pI7ve+Bqhx9z8EH/8zgcCf7O/7JcAud6919+PAk8D59J/3vUWs9zkh8a83Bvp+VfkymJd+CHjV3e8LeeppYHbw59nAv3R33xLJ3b/l7vnuXkTgPf5Xdy8D/g34YvCwpHvdLdz9XWC3mU0MNk0FtpHk7zuBlM0nzCwr+P9+y+vuF+97iFjv89PADcHZN58ADrWkeDrF3XvdF3AZgT1n3wD+T0/3J8Gv9X8R+Gi2FXg5+HUZgXz1FqAq+D27p/uawP8GFwObgz+PB/4I7AB+Dgzo6f4l8HVPBiqD7/0mYFh/eN+B/wtsB14BfgoMSOb3HXiUwP2I4wRG7F+J9T4TSN38IBj7/ofA7KRO90ErY0VEklxvTN2IiEgXUqAXEUlyCvQiIklOgV5EJMkp0IuIJDkFehGRJKdALyKS5BToRUSS3P8HQRgB165xVM0AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(X, Y, edgecolors='k')\n",
"plt.plot(X, Yhat, color='red')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2479.6099146071415"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# calculate difference between Y actual and Y hat\n",
"d1 = Y - Yhat\n",
"# calculate sum of squared residuals\n",
"ssr = d1.dot(d1)\n",
"ssr"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"281256.7345902547"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# calculate difference between Y actual and Y mean\n",
"d2 = Y - Y.mean()\n",
"# calculate total sum of squares\n",
"sst = d2.dot(d2)\n",
"sst"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R-squared is equal 0.9911838202977805\n"
]
}
],
"source": [
"# calculate r2\n",
"r2 = 1 - ssr / sst\n",
"print('R-squared is equal', r2)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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