01-Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(2, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.arange(3, 16) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAY50lEQVR4nO3de3yUhZX/8c8pIOGOknBHEbAgchGNFbTrWqmVVqRatVt/Vay2S7s/W9v9bRWoVrYXWy3W1nZ36+Kl0OpqrYD3G9KKWi81gBLuIAKSBAgLCUISkzDn90cGDWPGJHN75pn5vl+vvkhm5sVzHiHfDs885xxzd0REJHw+EXQBIiKSGAW4iEhIKcBFREJKAS4iElIKcBGRkOqYyYMVFhb60KFDM3lIEZHQW758+R53L4p9PKMBPnToUEpKSjJ5SBGR0DOzbS09rksoIiIhpQAXEQkpBbiISEgpwEVEQkoBLiISUgpwEZGQUoCLiISUAlxEJKQy2sgjIpIPHllZxtxnN1BeVcvA3l247ryRXDhhUMqPowAXEUmhR1aWMXtRKbUNhwAoq6pl9qJSgJSHuC6hiIik0NxnN3wQ3ofVNhxi7rMbUn4sBbiISIpEIk5ZVW2Lz5XHeTwZCnARkRTYUnmAy+56Le7zA3t3SfkxFeAiIkloOBThv17YzJQ7XmJtxX6+ctoQCjoeGa1dOnXguvNGpvzY+hBTRCRBpTuqmblwFWsr9vP5Mf350bST6NuzgInD+uguFBGRbFRbf4hfP7+Ru17aQmH3ztx5+alMGdP/g+cvnDAoLYEdSwEuItIOr2zew+zFpWz73xou+9QQZn3+RHp16RRILQpwEZE2qK5p4Oan1vJQyQ6G9unKA/88kUnD+wRakwJcRORjuDtPr97JTY+uYV9NPf9y9nC+O/kECjp1CLo0BbiISDy79tfxw0dW89zaXYwZ1JP5V53GmEG9gi7rAwpwEZEYkYjz4Bvv8vOn1lF/KMLsz4/i658+no4dsuvOawW4iEgzWyoPMHtRKa+/s5dJw/rw8y+NZWhht6DLapECXESEpoacu17awq+f30RBx0/wi4vHcWnxYMws6NLiUoCLSN6L15CT7VoNcDO7F5gK7Hb3MTHPfR+YCxS5+570lCgikjrNZ3X371XAiQN68sKG3S025GS7trwDnw/8B/CH5g+a2RDgXGB76ssSEUm92FndFdV1VFTXMWlYH+684tTAGnIS1epHqu7+IrC3had+BVwPeKqLEhFJh5ZmdQNs31sTuvCGBKcRmtk0oMzd32rDa2eYWYmZlVRWViZyOBGRpLlndlZ3JrQ7wM2sK3ADcFNbXu/u89y92N2Li4qK2ns4EZGk7ayu45t/XB73+XTM6s6ERN6BDweOB94ys63AYGCFmYXnyr+I5IVIxPmf17dz7u3LWLaxkmnjB2ZsVncmtPs2QncvBfoe/j4a4sW6C0VEskm8hpxMbYzPhLbcRvgAcDZQaGY7gDnufk+6CxMRSURrDTmZmtWdCa0GuLtf1srzQ1NWjYhIEsLakJModWKKSOjV1h/iV89v5O44G3JylQJcRELtb5v3MHtRKdv3Br8hJ9MU4CISSs035Bxf2C0rNuRkmgJcREIlmzfkZJoCXERCY2d1HT98dDVLsnRDTqYpwEUk64VlQ06mKcBFJKuFaUNOpinARSRrNO+SHNCrgAnHHs2SdbtCsyEn0xTgIpIVYmd1l1fXUV5awfjBvbhrenFON+QkKr8vIIlI1og3q3vPgXqFdxwKcBHJCrk2qzsTdAlFRAJVXdPAT59cG/f5sM7qzgQFuIgEwt15qnQncx5rasiZfGJf/rZpD3WNkQ9eE+ZZ3ZmgABeRjIvXkJNLs7ozQQEuIhkTiTgPvLGdW55aT0Mkwg++MIqrz/ywISeXZnVnggJcRDJiS+UBZi0q5e/v7OWM4U0NOcf1UUNOMhTgIpJWDYcizHtxC3csbXlDjiROAS4iabNqRxUzF5ayLk825GSaAlxEUq62/hC3L9nAPS+/k1cbcjJNAS4iKfXypj38YPHhDTnHMuvzo/JmQ06mKcBFJCWqauq5+cl1/Hl504acB2dMZOKw/NqQk2kKcBFJSmxDzv89ezjX5umGnExTgItIwmIbchZcfRonDczfDTmZ1mqAm9m9wFRgt7uPiT42F7gAqAfeBq5y96p0FioiwYqd1X3miEKeWb2zxYYcyYy2/NeeD0yJeWwJMMbdxwEbgdkprktEssjhWd1lVbU4TbO6/7x8B/17FfDs985ixlnDFd4BaPW/uLu/COyNeew5d2+MfvsaMDgNtYlIlog3q/vg+43qpgxQKv4v82rg6XhPmtkMMysxs5LKysoUHE5EMi3erO6K6roMVyLNJRXgZnYD0AjcH+817j7P3YvdvbioqCiZw4lIhtXUN3KzZnVnrYTvQjGzK2n6cHOyu3vqShKRbPDypj3MXryKd/fWcsbwPqzYtk+zurNMQgFuZlOAmcA/untNaksSkSDFa8jRrO7s05bbCB8AzgYKzWwHMIemu046A0uiE8Vec/dvpbFOEUkzd+fJ0gr+/bE17Ktp+EhDjmZ1Z59WA9zdL2vh4XvSUIuIBGRndR03PrKa59ftYuygXiy4+lNqyAkBdWKK5LFIxPmfv2/n1qdb3pAj2U0BLpKn3q48wOyFpfx9qzbkhJUCXCTPfGRDziXjuPRUbcgJIwW4SB55690qZi5cxfqd73H+2AHMmTaavj20ISesFOAieaCmvpHbn9vIvX97h6IenZl3xal87iRtyAk7BbhIjmvekPN/Tm/akNOzQBtycoECXCRHVdXU89Mn1/Hw8h0MK+zGn2ZM5HRtyMkpCnCRHBA7q/vc0f14srSixYYcyR0KcJGQOzyr+/C41/LqOha8uo0hR3fhD98+ndEDewZcoaSL7tYXCbl4s7oPRVzhneMU4CIhp1nd+UuXUERCquFQhP9e9nbc5zWrO/cpwEVCqHlDzslDerO+Yr9mdechBbhIiMRryNGs7vykABcJiZc2VfKDxaUtNuRoVnd+UoCLZLmqmnp+8sQ6Fq5QQ44cSQEukqXcnSdWVfCjx9dQVdPANZ8ZznfOUUOOfEgBLpKFKqpr+eEjq3l+3W7GDurFH65WQ458lAJcJItEIs790Q05jZEIN55/Il87Y6g25EiLFOAiWWLz7gPMXrSKN7bu49MjCvnZRWM5tk/XoMuSLKYAFwlYfWOEeS++zW+WbqbLUR2Ye8k4LtGGHGkDBbhIgI7YkDNuAHMu0IYcaTsFuEgAauob+eVzG/l9tCHnrunFnDu6X9BlSci0GuBmdi8wFdjt7mOijx0D/AkYCmwFvuzu+9JXpkh4xXZJXjB+AE+sqmDHvlq+evqxzNSGHElQWz7ang9MiXlsFrDU3U8Alka/F5EYh2d1l1XV4jRNDrxz2Rbeb4zwpxkTufmisQpvSVirAe7uLwJ7Yx7+IrAg+vUC4MIU1yWSE+LN6u70CVM3pSQt0ZtL+7l7BUD0177xXmhmM8ysxMxKKisrEzycSDhpVrekU9q7A9x9nrsXu3txUVFRug8nkhUiEeePr20j3o2AmtUtqZDoXSi7zGyAu1eY2QBgdyqLEgmz5g05n+zXnW3/W8P7mtUtaZDoO/DHgCujX18JPJqackTCq74xwm+XbuILd7zExl0HmHvJOJ793lncevE4BvXuggGDenfh518aq9GvkhJtuY3wAeBsoNDMdgBzgFuAh8zs68B24NJ0FimS7WIbcv79gpMo6tEZ0KxuSZ9WA9zdL4vz1OQU1yISOmrIkSCpE1MkQS9ubNqQs2NfLZdPPJbrp6ghRzJLAS7STvsO1vPTJz/ckPPQNyfxqeOPCbosyUMKcJE2it2Q8+3PjODb54zQhhwJjAJcpA2ab8gZN1gbciQ7KMBFPkZLG3KuOvN4OnxCs7oleApwkTi0IUeynQJcJEZ9Y4T/XvY2v/2LNuRIdlOAS16LndX9T8VDeGp1Bet3vsfUcQOY06whRyTbKMAlbx2e1X143GtZVS23P7+RXl06qSFHQiHt0whFslW8Wd1dj+qg8JZQUIBL3oo3q3unZnVLSCjAJe+4O4+/VU68OwE1q1vCQtfAJa80b8g59piu7Npfp1ndEloKcMkLkYhz/+vbuPWZDUc05Dz+VvkRd6Fcd95IjX6V0FCAS85r3pDzDycUcvOFHzbkaFa3hJkCXHJWbEPObZeO5+JTBqkhR3KGAlxy0pvvVjEruiFHDTmSqxTgklOab8jp26NADTmS0xTgkjO0IUfyjQJcQu+IDTlF2pAj+UMBLqHl7jy+qoIfPbaG6lptyJH8owCXUKqoruXGxatZun434wf34r5vnM6JA7QhR/KLAlxCJV5DjjbkSD5KKsDN7F+BbwAOlAJXubsmAUlKxM7qnj7pOJas3UXJto825Ijko4QD3MwGAdcCo9291sweAr4CzE9RbZLHWprV/fOn19NVDTkiH0j2EkpHoIuZNQBdgfLkSxKJP6u7Z0EnLjl1cAAViWSfhMfJunsZcBuwHagAqt39udjXmdkMMysxs5LKysrEK5W8Em9W9679ukIncljCAW5mRwNfBI4HBgLdzOzy2Ne5+zx3L3b34qKiosQrlbzx4sbKuB9Kala3yIeSWejwWeAdd6909wZgEXBGasqSfLTvYD3/76E3mX7v3+nT7SiO6nDkX0/N6hY5UjLXwLcDE82sK1ALTAZKUlKV5JV4DTnPrN6pWd0iHyPhAHf3183sYWAF0AisBOalqjDJD+VVTRtyWmrI0axukY+X1F0o7j4HmJOiWiSPNG/IORRxNeSIJECdmJJxm3cfYNbCVR805PzsorEMOUYNOSLtpQCXjNGGHJHUUoBLRmhDjkjqKcAlrWrqG7nt2Y38/pV36NejgLunF/NZbcgRSQkFuKRN7IacmVNG0UMbckRSRgEuKbfvYD0/eXIti1aUMayoG3/+1iROG6oNOSKppgCXlIltyPnOOSO45jPakCOSLgpwSVjzed19e3amsHtn1pTv14YckQxRgEtCYud179r/Prv2v8+FJw/kl18+WQ05IhmQzDAryWPx5nW/sXWfwlskQxTg0m71jZG487rL4zwuIqmnAJd2Wbl9Hxf89uW4z2tet0jmKMClTWrqG/nx42v50u9eYX9dA9/49PF0ibm7RPO6RTJLH2JKq5ZtrOSGaEPOFROP4/opI+lR0Ikxg3ppXrdIgBTgEte+g/X85Im1LFrZckOO5nWLBEsBLh/h7jz2Vjk/fnytGnJEspgCXI5QXlXLjY+s5i/rdzN+SG/uv3gso/qrIUckGynABWjakHPf69u49en1RBx+OHU0XztjqO7pFsliCnBh8+73mLmwlOXakCMSKgrwPFbfGOF3L7zNf/51M107d+CXl47nS9qQIxIaCvA8tWL7PmYtXMXGXQe4YPxA5lwwmsLu2pAjEiYK8Dxz8P1GbntuA/Nf2Ur/ngXcc2Uxk0/UhhyRMFKA55FlGyv5waJSyqpqmT7pOK47b6Q25IiEWFIBbma9gbuBMYADV7v7q6koTJLTfFZ3v54FDD66CyXb9jG8qBsPf2sSxdqQIxJ6yb4DvwN4xt0vMbOjAN26kAViZ3Xv3F/Hzv11fG50P35z2QQ15IjkiIQD3Mx6AmcBXwNw93qgPjVlSTLizepeU75f4S2SQ5KZRjgMqAR+b2YrzexuM+sW+yIzm2FmJWZWUllZmcThpC0iEdesbpE8kUyAdwROAX7n7hOAg8Cs2Be5+zx3L3b34qKioiQOJ63ZtOs9LrnzlbjPa1a3SG5JJsB3ADvc/fXo9w/TFOiSYfWNEe54fhPn/+Zltuw5yFdPP5aCjkf+0WpWt0juSfgauLvvNLN3zWyku28AJgNrU1eatEXzhpxp4wdyU7Qh57Shx2hWt0iOS/YulO8A90fvQNkCXJV8SdIWrTXkaFa3SO5LKsDd/U2gOEW1SBu9sGE3NyxeTXl104YcNeSI5Cd1YobI3uiGnMUryxhe1I0/f1MNOSL5TAEeAoc35Pzo8bXsr23g2nNGcM05I+jcUfd0i+QzBXiWK6uq5cbFpfx1QyUnD+nNLdqQIyJRCvAsFYk4f3xtG794pmlDzk1TR3OlNuSISDMK8Cy0add7zFy4ihXbq7QhR0TiUoBnkdgNObd/eTwXTdCGHBFpmQI8S8RryBERiUcBHoDms7r79yrghL7deWnzHgb0LODerxVzzihtyBGR1inAMyx2VndFdR0V1XV8ekQhd15xKt07649ERNommWFWkoB4s7rf2XNQ4S0i7aIAzyB3zeoWkdRRgGdIWVUtV89/I+7zmtUtIu2lAE+zSMRZ8MpWPnf7Ml7bspeLJgzSrG4RSQlddE2j5g05Z32yiJsvHMOQY7ryj58s0qxuEUmaAjwNmjfkdOvcgV/903guPPnDhhzN6haRVFCAp5gackQkUxTgKXLw/UbmPruBBa9uVUOOiGSEAjwFYjfkXD9llO7pFpG0U8okofmGnBF9u2tDjohklAI8Ac035LxX18C1k0/gms8M14YcEckoBXg7xW7IufXicYzs3yPoskQkDynA2+hQxLlPG3JEJIsowNsgXkOOiEiQkg5wM+sAlABl7j41+ZKC1XxW94BeBYwb3Jul63fRvXPHjzTkiIgEKRXvwL8LrANCvyo9dlZ3eXUd5dU7OeXY3sybXqyGHBHJKkkNszKzwcD5wN2pKSdY8WZ179r/vsJbRLJOstMIfw1cD0TivcDMZphZiZmVVFZWJnm49NKsbhEJk4QD3MymArvdffnHvc7d57l7sbsXFxUVJXq4tNp7sJ7vPbgy7vOa1S0i2SiZd+BnAtPMbCvwIHCOmd2XkqoyxN159M0yPnv7Mp4sreC8k/ppVreIhEbCH2K6+2xgNoCZnQ18390vT1FdaRevIaf5XSia1S0i2Szv7gM/FHH++OpWfvHsBryFhhzN6haRsEhJgLv7C8ALqfi90kkNOSKSS/LiHXh9Y4T/emEz//nXzWrIEZGckfMBvnzbPmYv0oYcEck9ORvg2pAjIrkuJwO8+Yac6ROP4zptyBGRHJRTqbb3YD0/fnwNj7xZzoi+3Xn4W5M49ThtyBGR3JQTAa4NOSKSj0If4Dv21XDjI6t5QRtyRCTPhCrAY2d1TxzWh2fW7AS0IUdE8k9oArylWd2LVpYxqn8P7pperIYcEck7yY6TzZh4s7r31zYovEUkL4UmwOPN5K6orstwJSIi2SE0AR5vJrdmdYtIvgpNgF933ki6dDrytkDN6haRfBaaDzEPj3jVrG4RkSahCXDQrG4RkeZCcwlFRESOpAAXEQkpBbiISEgpwEVEQkoBLiISUgpwEZGQUoCLiISUAlxEJKTM3TN3MLNKYFsKfqtCYE8Kfp+g6TyyRy6cA+g8sk2qzuM4dy+KfTCjAZ4qZlbi7sVB15EsnUf2yIVzAJ1Htkn3eegSiohISCnARURCKqwBPi/oAlJE55E9cuEcQOeRbdJ6HqG8Bi4iIuF9By4ikvcU4CIiIRWqADezIWb2VzNbZ2ZrzOy7QdeUKDPrYGYrzeyJoGtJlJn1NrOHzWx99M9kUtA1JcLM/jX692m1mT1gZgVB19QWZnavme02s9XNHjvGzJaY2abor0cHWWNbxDmPudG/V6vMbLGZ9Q6yxta0dA7Nnvu+mbmZFab6uKEKcKAR+Dd3PxGYCFxjZqMDrilR3wXWBV1Eku4AnnH3UcB4Qng+ZjYIuBYodvcxQAfgK8FW1WbzgSkxj80Clrr7CcDS6PfZbj4fPY8lwBh3HwdsBGZnuqh2ms9HzwEzGwKcC2xPx0FDFeDuXuHuK6Jfv0dTYIRux5qZDQbOB+4OupZEmVlP4CzgHgB3r3f3qmCrSlhHoIuZdQS6AuUB19Mm7v4isDfm4S8CC6JfLwAuzGhRCWjpPNz9OXdvjH77GjA444W1Q5w/C4BfAdcDablbJFQB3pyZDQUmAK8HW0lCfk3TH2ok6EKSMAyoBH4fvRR0t5l1C7qo9nL3MuA2mt4hVQDV7v5csFUlpZ+7V0DTGx6gb8D1pMLVwNNBF9FeZjYNKHP3t9J1jFAGuJl1BxYC33P3/UHX0x5mNhXY7e7Lg64lSR2BU4DfufsE4CDh+Of6EaLXiL8IHA8MBLqZ2eXBViWHmdkNNF06vT/oWtrDzLoCNwA3pfM4oQtwM+tEU3jf7+6Lgq4nAWcC08xsK/AgcI6Z3RdsSQnZAexw98P/AnqYpkAPm88C77h7pbs3AIuAMwKuKRm7zGwAQPTX3QHXkzAzuxKYCnzVw9ewMpymNwVvRX/WBwMrzKx/Kg8SqgA3M6Ppmus6d7896HoS4e6z3X2wuw+l6cOyv7h76N7xuftO4F0zGxl9aDKwNsCSErUdmGhmXaN/vyYTwg9jm3kMuDL69ZXAowHWkjAzmwLMBKa5e03Q9bSXu5e6e193Hxr9Wd8BnBL9uUmZUAU4Te9er6DpXeub0f99Ieii8th3gPvNbBVwMvCzgOtpt+i/IB4GVgClNP1MhKKN28weAF4FRprZDjP7OnALcK6ZbaLp7odbgqyxLeKcx38APYAl0Z/zOwMtshVxziH9xw3fv0xERATC9w5cRESiFOAiIiGlABcRCSkFuIhISCnARURCSgEuIhJSCnARkZD6/65bxraoBev+AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array([3,  4,  5,  6,  7,  8,  11, 10, 11, 12, 13, 14, 18])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('base')",
   "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": "3.9.12"
  },
  "vscode": {
   "interpreter": {
    "hash": "6f5583cf1d9466b5c27e75c89cc6b383bed5736d6b16c51c8074d8690011a952"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}