{"cells":[{"cell_type":"code","source":"import pandas as pd \nimport statsmodels.api as sm \nimport statsmodels.formula.api as smf \nimport matplotlib.pyplot as plt \n\n#Link de Interés: https://www.statsmodels.org/devel/","metadata":{"id":"xjwjIoohrYEd","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"c3ed1f104f894218991b9d52873d5d5e","outputId":"bb3f8c3a-7b66-4124-e98e-2cb03f2d4083","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":2414,"user_tz":240,"timestamp":1652019242137},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stderr","text":"/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n import pandas.util.testing as tm\n"}],"execution_count":1},{"cell_type":"markdown","source":"","metadata":{"id":"S48c8B54rpDc","cell_id":"53ae01666a31453dac3303a0599553be","deepnote_cell_type":"markdown"}},{"cell_type":"markdown","source":"**statsmodels** es un módulo de Python que proporciona clases y funciones para la estimación de muchos modelos estadísticos diferentes, así como para realizar pruebas estadísticas y exploración de datos estadísticos.","metadata":{"id":"QwXPv73UrYEg","cell_id":"03529f48dfd04d31b283849d60868753","deepnote_cell_type":"markdown"}},{"cell_type":"code","source":"#Carga de datos\nfile = 'https://raw.githubusercontent.com/fhernanb/Python-para-estadistica/master/03%20Regression/Regresi%C3%B3n%20lineal%20simple/softdrink.csv'\ndf = pd.read_csv(file)","metadata":{"id":"lFAgpQecrYEj","cell_id":"6fa0b78189ca413bb237d3a11022c858","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":398,"user_tz":240,"timestamp":1652019242529},"deepnote_cell_type":"code"},"outputs":[],"execution_count":2},{"cell_type":"code","source":"#Vemos nuestros datos\ndf.head() ","metadata":{"id":"aXmzdqq6rYEk","colab":{"height":206,"base_uri":"https://localhost:8080/"},"cell_id":"0657b77d0836457a93b6048be11e4cde","outputId":"056feb6d-039a-493b-a857-d515c17219a2","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":268,"user_tz":240,"timestamp":1652019246749},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":" Obs y x1 x2\n0 1 16.68 7 560\n1 2 11.50 3 220\n2 3 12.03 3 340\n3 4 14.88 4 80\n4 5 13.75 6 150","text/html":"\n
\n
\n
\n\n
\n \n
\n
\n
Obs
\n
y
\n
x1
\n
x2
\n
\n \n \n
\n
0
\n
1
\n
16.68
\n
7
\n
560
\n
\n
\n
1
\n
2
\n
11.50
\n
3
\n
220
\n
\n
\n
2
\n
3
\n
12.03
\n
3
\n
340
\n
\n
\n
3
\n
4
\n
14.88
\n
4
\n
80
\n
\n
\n
4
\n
5
\n
13.75
\n
6
\n
150
\n
\n \n
\n
\n \n \n \n\n \n
\n
\n "},"metadata":{},"execution_count":3}],"execution_count":3},{"cell_type":"code","source":"#Scatterplot\ndf.plot(kind='scatter', x='x1', y='y')","metadata":{"id":"rqWuKS6frYEm","colab":{"height":296,"base_uri":"https://localhost:8080/"},"cell_id":"9b3cba354dc94a1fbf8c10386182f71d","outputId":"27e69460-2992-4311-ada2-6db7f222be59","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":852,"user_tz":240,"timestamp":1652019248800},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":""},"metadata":{},"execution_count":4},{"output_type":"display_data","data":{"text/plain":"
","image/png":"iVBORw0KGgoAAAANSUhEUgAAAX4AAAEGCAYAAABiq/5QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAVPklEQVR4nO3dbYxc53ne8f+15GpJi7JJUSxBi1KpRIIN25DoZGvYkBG4khU4qSEplUA4aBymEMB+SBO5TiuqQYvEQVpYTGo3QAC3rJWWRf1Gi1Kp+oNrgZaRui0UL22K1ksKya4UkabENUPG2pTcLLV3P+yhTS53pV1yzw5nz/8HEDPzzMzOfTjgtQ/veeY5qSokSd0x0OsCJEmLy+CXpI4x+CWpYwx+SeoYg1+SOmZ5rwuYi6uuuqo2bdrU6zIkqa/s37//h1W1bvp4XwT/pk2bGBkZ6XUZktRXkrw407itHknqGINfkjrG4Jekjmk1+JP8kyRPJ3kqyReSrEhyXZInkjyf5EtJLmuzBknSuVoL/iRXA78JDFfVu4BlwEeAB4BPV9X1wHHgnrZqkCSdr+1Wz3JgZZLlwJuAI8AtwEPN/buAO1uuQZL6zrGxcZ586QTHxsYX/Ge3tpyzqg4n+UPgL4CTwNeA/cCJqjrdPOwQcPVMz0+yDdgGcO2117ZVpiRdcvYeOMz2PQcZHBhgYnKSHXfdyO2bZ4zKC9Jmq2cNcAdwHfBW4HLgQ3N9flXtrKrhqhpet+687x9I0pJ0bGyc7XsOcmpiklfHT3NqYpL79hxc0Jl/m62eDwL/t6pGq2oCeBi4GVjdtH4ANgKHW6xBkvrKoeMnGRw4N5oHBwY4dPzkgr1Gm8H/F8B7k7wpSYBbgWeAx4G7m8dsBfa2WIMk9ZWNa1YyMTl5ztjE5CQb16xcsNdoLfir6gmmPsT9NvDd5rV2AtuBjyd5HlgLPNhWDZLUb9auGmLHXTeyYnCAK4aWs2JwgB133cjaVUML9hrph1MvDg8Pl3v1SOqSY2PjHDp+ko1rVl5w6CfZX1XD08f7YpM2SeqatauGFnSWfza3bJCkjjH4JaljDH5J6hiDX5I6xuCXpI4x+CWpYwx+SeoYg1+SOsbgl6SOMfglqWMMfknqGINfkjrG4JekjjH4JaljDH5J6hiDX5I6xuCXpI4x+CWpY1oL/iRvS3LgrD8/SvKxJFcmeSzJc83lmrZqkCSdr7Xgr6r/U1Wbq2oz8LPA/wMeAe4H9lXVDcC+5rYkaZEsVqvnVuB7VfUicAewqxnfBdy5SDVIkli84P8I8IXm+vqqOtJcfxlYP9MTkmxLMpJkZHR0dDFqlKROaD34k1wG3A58efp9VVVAzfS8qtpZVcNVNbxu3bqWq5Sk7liMGf8vAN+uqlea268k2QDQXB5dhBokSY3FCP5f5idtHoBHga3N9a3A3kWoQZLUaDX4k1wO3AY8fNbwJ4HbkjwHfLC5LUlaJMvb/OFV9dfA2mljx5ha5SNJ6gG/uStJHWPwS1LHGPyS1DEGvyR1jMEvSR1j8EtSxxj8ktQxBr8kdYzBL0kdY/BLUscY/JLUMQa/JHWMwS9JHWPwS1LHGPyS1DEGvyR1jMEvSR1j8EtSx7R9zt3VSR5K8udJnk3yviRXJnksyXPN5Zo2a5AknavtGf8fAV+tqrcDNwHPAvcD+6rqBmBfc1uStEhaC/4kbwF+DngQoKr+pqpOAHcAu5qH7QLubKsGSdL52pzxXweMAv8xyXeSfDbJ5cD6qjrSPOZlYH2LNUiSpmkz+JcDPwN8pqreDfw109o6VVVAzfTkJNuSjCQZGR0dbbFMSeqWNoP/EHCoqp5obj/E1C+CV5JsAGguj8705KraWVXDVTW8bt26FsuUpG5pLfir6mXgpSRva4ZuBZ4BHgW2NmNbgb1t1SBJOt/yln/+bwCfS3IZ8H3gHzL1y2Z3knuAF4EtLdcgSTpLq8FfVQeA4RnuurXN15Ukzc5v7kpSxxj8ktQxBr8kdYzBL0kdY/BLUscY/JLUMQa/JHWMwS9JHWPwS1LHGPyS1DEGvyR1jMEvSR1j8EtSxxj8kjrj2Ng4T750gmNj470upafa3o9fki4Jew8cZvuegwwODDAxOcmOu27k9s1X97qsnnDGL2nJOzY2zvY9Bzk1Mcmr46c5NTHJfXsOdnbmb/BLWvIOHT/J4MC5cTc4MMCh4yd7VFFvGfySlryNa1YyMTl5ztjE5CQb16zsUUW9ZfBLWvLWrhpix103smJwgCuGlrNicIAdd93I2lVDvS6tJ1r9cDfJC8CrwGvA6aoaTnIl8CVgE/ACsKWqjrdZhyTdvvlqbr7+Kg4dP8nGNSs7G/qwODP+v1tVm6vqzEnX7wf2VdUNwL7mtiS1bu2qIW66ZnWnQx960+q5A9jVXN8F3NmDGiSps9oO/gK+lmR/km3N2PqqOtJcfxlYP9MTk2xLMpJkZHR0tOUyJak72v4C1/ur6nCSvwU8luTPz76zqipJzfTEqtoJ7AQYHh6e8TGSpPlrdcZfVYeby6PAI8B7gFeSbABoLo+2WYMk6VytBX+Sy5NcceY68PPAU8CjwNbmYVuBvW3VIEk6X5utnvXAI0nOvM7nq+qrSb4F7E5yD/AisKXFGiRJ07QW/FX1feCmGcaPAbe29bqSpNfnN3clqWMMfknqGINfkjrG4JekjjH4JaljDH5J6hiDX5I6xuCXpI4x+CWpYwx+SeoYg1+SOuYNgz/JbyRZsxjFSJLaN5cZ/3rgW0l2J/lQmu02JUn96Q2Dv6r+BXAD8CDwa8BzSf51kp9uuTZJUgvm1OOvqmLq/LgvA6eBNcBDSXa0WJskqQVvuB9/knuBXwV+CHwW+GdVNZFkAHgOuK/dEiVJC2kuJ2K5Evj7VfXi2YNVNZnkw+2UJUlqyxsGf1X9zuvc9+zCliNJapvr+KULdGxsnCdfOsGxsfFelyLNS5snWwcgyTJgBDhcVR9Och3wRWAtsB/4aFX9Tdt1SAtp74HDbN9zkMGBASYmJ9lx143cvvnqXpclzclizPjvBc5uCT0AfLqqrgeOA/csQg3Sgjk2Ns72PQc5NTHJq+OnOTUxyX17DjrzV99oNfiTbAT+HlOrgWi+/HUL8FDzkF3AnW3WIC20Q8dPMjhw7j+dwYEBDh0/2aOKpPlpe8b/b5la7jnZ3F4LnKiq083tQ8CM/z9Osi3JSJKR0dHRlsuU5m7jmpVMTE6eMzYxOcnGNSt7VJE0P60Ff7PU82hV7b+Q51fVzqoarqrhdevWLXB10oVbu2qIHXfdyIrBAa4YWs6KwQF23HUja1cN9bo0aU7a/HD3ZuD2JL8IrADeDPwRsDrJ8mbWvxE43GINUitu33w1N19/FYeOn2TjmpWGvvpKazP+qvrnVbWxqjYBHwG+XlX/AHgcuLt52FZgb1s1SG1au2qIm65Zbeir7/RiHf924ONJnmeq5/9gD2qQpM5qfR0/QFV9A/hGc/37wHsW43UlSefzm7uS1DEGvyR1jMEvSR1j8EtSxxj8ktQxBr+0CNzCWZeSRVnOKXWZWzjrUuOMX2qRWzjrUmTwSy1yC2ddigx+qUVu4axLkcEvtcgtnHUp8sNdqWVu4axLjcEvLYK1q4YMfF0ybPVIUscY/JLUMQa/JHWMwS9JHWPwS1LHGPyS1DGtBX+SFUn+LMmTSZ5O8olm/LokTyR5PsmXklzWVg2SpPO1OeMfB26pqpuAzcCHkrwXeAD4dFVdDxwH7mmxBknSNK0Ff00Za24ONn8KuAV4qBnfBdzZVg2SpPO12uNPsizJAeAo8BjwPeBEVZ1uHnIImHFj8iTbkowkGRkdHW2zTF0ATywi9a9Wt2yoqteAzUlWA48Ab5/Hc3cCOwGGh4ernQp1ITyxiNTfFmVVT1WdAB4H3gesTnLmF85G4PBi1KCF4YlFpP7X5qqedc1MnyQrgduAZ5n6BXB387CtwN62atDC88QiUv9rs9WzAdiVZBlTv2B2V9VXkjwDfDHJ7wPfAR5ssQYtME8sIvW/1oK/qg4C755h/PvAe9p6XbXrzIlF7pvW43fLYal/uB+/5s0Ti0j9zeDXBfHEIlL/cq8eSeoYg1+SOsbgl6SOMfglqWMMfknqGINfkjrG4NePueOm1A2u4xfgjptSlzjjlztuSh1j8MsdN6WOMfjljptSxxj8+vGOmysGB7hiaDkrBgfccVNawvxwt2XHxsb7YhdLd9yUusPgb1G/rZRxx02pG2z1tGSpr5Rxzb/Uv5zxt+TMSplT/ORD0zMrZfp9Vt1v/5ORdK42T7Z+TZLHkzyT5Okk9zbjVyZ5LMlzzeWatmropaW6Umap/09G6oI2Wz2ngd+qqncA7wV+Pck7gPuBfVV1A7Cvub3kXMhKmTbaJwv9M13zL/W/Nk+2fgQ40lx/NcmzwNXAHcAHmoftAr4BbG+rjl6az0qZNtonbfzMpfo/GalLFuXD3SSbgHcDTwDrm18KAC8D6xejhl5Zu2qIm65Z/YYz/fm0T+Yyi2+rJeOaf6n/tf7hbpJVwB7gY1X1oyQ/vq+qKknN8rxtwDaAa6+9tu0ye2o+HwTPdRZ/IR8uz/U7B675l/pbq8GfZJCp0P9cVT3cDL+SZENVHUmyATg603OraiewE2B4eHjGXw5LxVzbJ2fP4s8E+n17DnLz9VedF77zbcnMty3kmn+pf7W5qifAg8CzVfWps+56FNjaXN8K7G2rhn4x1/bJfD5YnU9L5kLaQq7jl/pXmzP+m4GPAt9NcqAZ+23gk8DuJPcALwJbWqyhb8ylfbJxzUpOnX7tnLFTp1+bdRY/15bMfNtCruOX+lubq3q+CWSWu29t63X72VzaJ6dfq9e9fSE/cz5tofm0myRdmtyyoY88/YMfMT3mqxm/GPNpC7mOX+p/btnQV2ab3V/8Z99zbQu5jl/qf874+8g73/oWlg2c2z1bNhDe+da3LMjPn8t3DlzHL/U/Z/x9JtNm99NvLwbX8Uv9zeDvI4eOn2Tl4HJeHT/947GVg8t7suOn6/il/mWr5xLyRmvj7a9LWggG/wVo48tLew8c5uYHvs6vfPYJbn7g6zx64PB5j7G/Lmkh2OqZpza+vDSftfH21yVdLGf889DWjpfzXRs/l9U3kjQbg38e2vrykr17SYvJ4J+HtgJ67aohtgxvPGdsy/BGZ/SSWmHwz8PaVUNs+dmFD+hjY+PsHjl0ztjukUPufCmpFQb/PBwbG2f3/oUPaPe/kbSYDP55sMcvaSkw+BtzWZvfZo/f9fmSFovr+Jlam3/fQwdZNhBemyz+4O6Z1+afCej7pq3jX4iAdn2+pMXS+eA/NjbOP/3yk0ycdUKT3/ryk7OeWKTNgHb/G0mLofOtnqd/8FfnhD7AxGvF0z/4qx5VJEnt6vyMf/azQ8487vlmJfW71mb8Sf4kydEkT501dmWSx5I811yuaev15+qdb30zy6f9LSwfmBqfrq0tGyRpMbXZ6vlPwIemjd0P7KuqG4B9ze2eWrtqiE9t2czQ8vCmwWUMLQ+f2rLZ881KWrJaa/VU1Z8m2TRt+A7gA831XcA3gO1t1TBXt2++mndseDMHXjrB5mtWc/36K2Z8nOvtJS0Fi/3h7vqqOtJcfxlYP9sDk2xLMpJkZHR0tNWi9h44zIf/+Jt84r89w4f/+Jsz7oUPrreXtDSkqr1ztjYz/q9U1bua2yeqavVZ9x+vqjfs8w8PD9fIyEgrNR4bG+fmB77OqYmfzORXDA7wP7ffMmugHxsbd729pEtekv1VNTx9fLFX9bySZENVHUmyATi6yK9/njN9+zMnQIGf9O1nC3XX20vqZ4vd6nkU2Npc3wrsbfPFerkNgyRdqtpczvkF4H8Db0tyKMk9wCeB25I8B3ywud2KuZzDFuzbS+qeVnv8C2W+PX779pJ06fT4F4V9e0ma3ZLcq8e+vSTNbkkGv317SZrdkmz1gPvbS9Jslmzwg317SZrJkmz1SJJmZ/BLUscY/JLUMQa/JHWMwS9JHdMXWzYkGQVe7HUdF+Eq4Ie9LqIFHlf/WarH5nHN7G9X1brpg30R/P0uychM+2X0O4+r/yzVY/O45sdWjyR1jMEvSR1j8C+Onb0uoCUeV/9Zqsfmcc2DPX5J6hhn/JLUMQa/JHWMwd+iJC8k+W6SA0nmfu7IS1CSP0lyNMlTZ41dmeSxJM81l2t6WeOFmOW4fjfJ4eZ9O5DkF3tZ44VIck2Sx5M8k+TpJPc24339nr3OcS2F92xFkj9L8mRzbJ9oxq9L8kSS55N8KcllF/1a9vjbk+QFYLiq+v6LJUl+DhgD/nNVvasZ2wH8ZVV9Msn9wJqq2t7LOudrluP6XWCsqv6wl7VdjCQbgA1V9e0kVwD7gTuBX6OP37PXOa4t9P97FuDyqhpLMgh8E7gX+DjwcFV9Mcm/A56sqs9czGs549ecVNWfAn85bfgOYFdzfRdT/wD7yizH1feq6khVfbu5/irwLHA1ff6evc5x9b2aMtbcHGz+FHAL8FAzviDvmcHfrgK+lmR/km29LqYF66vqSHP9ZWB9L4tZYP84ycGmFdRX7ZDpkmwC3g08wRJ6z6YdFyyB9yzJsiQHgKPAY8D3gBNVdbp5yCEW4Bedwd+u91fVzwC/APx601ZYkmqqZ7hU+oafAX4a2AwcAf5Nb8u5cElWAXuAj1XVj86+r5/fsxmOa0m8Z1X1WlVtBjYC7wHe3sbrGPwtqqrDzeVR4BGm3sil5JWm53qm93q0x/UsiKp6pfkHOAn8B/r0fWv6xHuAz1XVw81w379nMx3XUnnPzqiqE8DjwPuA1UnOnCZ3I3D4Yn++wd+SJJc3Hz6R5HLg54GnXv9ZfedRYGtzfSuwt4e1LJgzwdj4JfrwfWs+KHwQeLaqPnXWXX39ns12XEvkPVuXZHVzfSVwG1OfYTwO3N08bEHeM1f1tCTJTzE1y4epk9p/vqr+VQ9LuihJvgB8gKltYl8Bfgf4r8Bu4Fqmts3eUlV99UHpLMf1AaZaBgW8APyjs/rifSHJ+4H/AXwXmGyGf5upfnjfvmevc1y/TP+/Zzcy9eHtMqYm5bur6veaLPkicCXwHeBXqmr8ol7L4JekbrHVI0kdY/BLUscY/JLUMQa/JHWMwS9JHWPwSxchyVeTnEjylV7XIs2VwS9dnD8APtrrIqT5MPilOUjyd5oNwFY038p+Osm7qmof8Gqv65PmY/kbP0RSVX0ryaPA7wMrgf9SVX23LYAEBr80H78HfAs4Bfxmj2uRLpitHmnu1gKrgCuAFT2uRbpgBr80d/8e+JfA54AHelyLdMFs9UhzkORXgYmq+nySZcD/SnIL8AmmTpaxKskh4J6q+u+9rFV6I+7OKUkdY6tHkjrG4JekjjH4JaljDH5J6hiDX5I6xuCXpI4x+CWpY/4/aUu4AaegDLcAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}],"execution_count":4},{"cell_type":"code","source":"#Creacion del modelo con statsmodels.api.\n\nX = df[\"x1\"] # X: variables independientes\nX = sm.add_constant(X)# Se agrega una intercepción (beta_0) al modelo\ny = df[\"y\"] # Y: variable respuesta (o variable dependiente)\n\n# Fiteamos el modelo\nmod1 = sm.OLS(y, X).fit() \npredictions = mod1.predict(X)","metadata":{"id":"TEkxSLcDrYEn","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"c99ce7ec07674551854cf6ec8b157507","outputId":"176881d7-400b-4ce1-ebcd-8e6ab9837246","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":338,"user_tz":240,"timestamp":1652019250428},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stderr","text":"/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/tsatools.py:117: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n x = pd.concat(x[::order], 1)\n"}],"execution_count":5},{"cell_type":"code","source":"#Estadisticas del modelo\nmod1.summary() ","metadata":{"id":"-kSysq_CrYEp","colab":{"height":437,"base_uri":"https://localhost:8080/"},"cell_id":"001eed9a8c19461f81eeb840068017dd","outputId":"9742ec8f-2c05-466b-97a9-7dc7d32442cf","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":282,"user_tz":240,"timestamp":1652019252405},"deepnote_cell_type":"code"},"outputs":[{"output_type":"execute_result","data":{"text/plain":"\n\"\"\"\n OLS Regression Results \n==============================================================================\nDep. Variable: y R-squared: 0.930\nModel: OLS Adj. R-squared: 0.927\nMethod: Least Squares F-statistic: 307.8\nDate: Sun, 08 May 2022 Prob (F-statistic): 8.22e-15\nTime: 14:14:12 Log-Likelihood: -70.197\nNo. Observations: 25 AIC: 144.4\nDf Residuals: 23 BIC: 146.8\nDf Model: 1 \nCovariance Type: nonrobust \n==============================================================================\n coef std err t P>|t| [0.025 0.975]\n------------------------------------------------------------------------------\nconst 3.3208 1.371 2.422 0.024 0.484 6.157\nx1 2.1762 0.124 17.546 0.000 1.920 2.433\n==============================================================================\nOmnibus: 2.383 Durbin-Watson: 0.946\nProb(Omnibus): 0.304 Jarque-Bera (JB): 1.037\nSkew: 0.388 Prob(JB): 0.595\nKurtosis: 3.627 Cond. No. 18.2\n==============================================================================\n\nWarnings:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n\"\"\"","text/html":"
\n
OLS Regression Results
\n
\n
Dep. Variable:
y
R-squared:
0.930
\n
\n
\n
Model:
OLS
Adj. R-squared:
0.927
\n
\n
\n
Method:
Least Squares
F-statistic:
307.8
\n
\n
\n
Date:
Sun, 08 May 2022
Prob (F-statistic):
8.22e-15
\n
\n
\n
Time:
14:14:12
Log-Likelihood:
-70.197
\n
\n
\n
No. Observations:
25
AIC:
144.4
\n
\n
\n
Df Residuals:
23
BIC:
146.8
\n
\n
\n
Df Model:
1
\n
\n
\n
Covariance Type:
nonrobust
\n
\n
\n
\n
\n
coef
std err
t
P>|t|
[0.025
0.975]
\n
\n
\n
const
3.3208
1.371
2.422
0.024
0.484
6.157
\n
\n
\n
x1
2.1762
0.124
17.546
0.000
1.920
2.433
\n
\n
\n
\n
\n
Omnibus:
2.383
Durbin-Watson:
0.946
\n
\n
\n
Prob(Omnibus):
0.304
Jarque-Bera (JB):
1.037
\n
\n
\n
Skew:
0.388
Prob(JB):
0.595
\n
\n
\n
Kurtosis:
3.627
Cond. No.
18.2
\n
\n
Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."},"metadata":{},"execution_count":6}],"execution_count":6},{"cell_type":"code","source":"#Ajuste del modelo - statsmodels.formula.api\nmod2 = smf.ols('y ~ x1', data=df).fit()","metadata":{"id":"ggt323bcrYEq","cell_id":"38e316007e6241478dadd722c6599a61","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":311,"user_tz":240,"timestamp":1652019254834},"deepnote_cell_type":"code"},"outputs":[],"execution_count":7},{"cell_type":"code","source":"#Vemos las estadisticas del modelo\nprint(mod2.summary())","metadata":{"id":"J109dpbhrYEr","colab":{"base_uri":"https://localhost:8080/"},"cell_id":"60a462767bb445d9a02ec9596c89159d","outputId":"68741448-194f-48f3-9934-08c90cf974e0","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":10,"user_tz":240,"timestamp":1652019255511},"deepnote_cell_type":"code"},"outputs":[{"output_type":"stream","name":"stdout","text":" OLS Regression Results \n==============================================================================\nDep. Variable: y R-squared: 0.930\nModel: OLS Adj. R-squared: 0.927\nMethod: Least Squares F-statistic: 307.8\nDate: Sun, 08 May 2022 Prob (F-statistic): 8.22e-15\nTime: 14:14:15 Log-Likelihood: -70.197\nNo. Observations: 25 AIC: 144.4\nDf Residuals: 23 BIC: 146.8\nDf Model: 1 \nCovariance Type: nonrobust \n==============================================================================\n coef std err t P>|t| [0.025 0.975]\n------------------------------------------------------------------------------\nIntercept 3.3208 1.371 2.422 0.024 0.484 6.157\nx1 2.1762 0.124 17.546 0.000 1.920 2.433\n==============================================================================\nOmnibus: 2.383 Durbin-Watson: 0.946\nProb(Omnibus): 0.304 Jarque-Bera (JB): 1.037\nSkew: 0.388 Prob(JB): 0.595\nKurtosis: 3.627 Cond. No. 18.2\n==============================================================================\n\nWarnings:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"}],"execution_count":8},{"cell_type":"code","source":"#Recta de regresion estimada\nfig, ax = plt.subplots(figsize=(8,6)) # Ajuste del gráfico\nax.plot(df[\"x1\"] , df[\"y\"] , 'o', label=\"Datos\") # Ajuste para las etiquetas del gráfico\nax.plot(df[\"x1\"], mod2.fittedvalues, 'r--.', label=\"Ajustado\") # Ajuste para las etiquetas del gráfico\nlegend = ax.legend(loc=\"best\") # Ajustes para las leyendas del gráfico\nplt.xlabel('Cantidad de cajas') # Etiqueta del eje X\nplt.ylabel('Tiempo (min)') # Etiqueta del eje Y\nplt.title('Diagrama de dispersión con la recta del modelo ajustado'); # Título del gráfico","metadata":{"id":"eSF7-LqzrYEs","colab":{"height":404,"base_uri":"https://localhost:8080/"},"cell_id":"ce423e63f2d343b692d3f5f176d6b943","outputId":"c3b9537e-1497-4817-90a1-f40cb2a03096","executionInfo":{"user":{"userId":"09471607480253994520","displayName":"David Francisco Bustos Usta"},"status":"ok","elapsed":1126,"user_tz":240,"timestamp":1652019258255},"deepnote_cell_type":"code"},"outputs":[{"output_type":"display_data","data":{"text/plain":"","image/png":"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\n"},"metadata":{"needs_background":"light"}}],"execution_count":9},{"cell_type":"markdown","source":"\n \nCreated in Deepnote","metadata":{"created_in_deepnote_cell":true,"deepnote_cell_type":"markdown"}}],"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Regresión - Ejemplo 4.ipynb","provenance":[],"collapsed_sections":[]},"deepnote":{},"kernelspec":{"name":"python3","language":"python","display_name":"Python 3"},"varInspector":{"cols":{"lenVar":40,"lenName":16,"lenType":16},"kernels_config":{"r":{"library":"var_list.r","varRefreshCmd":"cat(var_dic_list()) ","delete_cmd_prefix":"rm(","delete_cmd_postfix":") "},"python":{"library":"var_list.py","varRefreshCmd":"print(var_dic_list())","delete_cmd_prefix":"del ","delete_cmd_postfix":""}},"window_display":false,"types_to_exclude":["module","function","builtin_function_or_method","instance","_Feature"]},"language_info":{"name":"python","version":"3.8.5","mimetype":"text/x-python","file_extension":".py","pygments_lexer":"ipython3","codemirror_mode":{"name":"ipython","version":3},"nbconvert_exporter":"python"},"deepnote_notebook_id":"beb872cdfa4944a59e290a34b5b3e1ee","deepnote_execution_queue":[]}}
\nCreated in Deepnote","metadata":{"created_in_deepnote_cell":true,"deepnote_cell_type":"markdown"}}],"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Regresión - Ejemplo 4.ipynb","provenance":[],"collapsed_sections":[]},"deepnote":{},"kernelspec":{"name":"python3","language":"python","display_name":"Python 3"},"varInspector":{"cols":{"lenVar":40,"lenName":16,"lenType":16},"kernels_config":{"r":{"library":"var_list.r","varRefreshCmd":"cat(var_dic_list()) ","delete_cmd_prefix":"rm(","delete_cmd_postfix":") "},"python":{"library":"var_list.py","varRefreshCmd":"print(var_dic_list())","delete_cmd_prefix":"del ","delete_cmd_postfix":""}},"window_display":false,"types_to_exclude":["module","function","builtin_function_or_method","instance","_Feature"]},"language_info":{"name":"python","version":"3.8.5","mimetype":"text/x-python","file_extension":".py","pygments_lexer":"ipython3","codemirror_mode":{"name":"ipython","version":3},"nbconvert_exporter":"python"},"deepnote_notebook_id":"beb872cdfa4944a59e290a34b5b3e1ee","deepnote_execution_queue":[]}}