python

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import math as math" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "Bernoulli= [0,1]\n", "p_Bernoulli= [0.6,0.4]\n", "Bernoulli_dict={'outcome': Bernoulli, 'prob':p_Bernoulli}\n", "Bernoulli_df=pd.DataFrame(data=Bernoulli_dict,index=('miss', 'make'))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>outcome</th>\n", " <th>prob</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>miss</th>\n", " <td>0</td>\n", " <td>0.6</td>\n", " </tr>\n", " <tr>\n", " <th>make</th>\n", " <td>1</td>\n", " <td>0.4</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " outcome prob\n", "miss 0 0.6\n", "make 1 0.4" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Bernoulli_df" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#######Problem 2 Create pdf ###########################" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def choose(x,n):\n", " return math.factorial(n)/(math.factorial(x)*math.factorial(n-x))\n", "def pdf(x,n,df):\n", " return choose(x,n)*(df.loc['make']['prob']**x)*(df.loc['miss']['prob']**(n-x))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "###############Problem 3 ##################################" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.15973847800416832" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pdf(9,20,Bernoulli_df)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "###############Problem 4 ##################################" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "##Get complete pdf###\n", "def all_pdfs(n,df):\n", " PDF=[]\n", " for i in range(n+1):\n", " PDF.append(pdf(i,n,df))\n", " PDF=[round(num,6) for num in PDF]\n", " return PDF" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "P=all_pdfs(20,Bernoulli_df)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1c67ad4f5e0>]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "plt.plot(P)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "###Get CDF using complete PDF" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def cdf(p):\n", " C=[]\n", " C.append(p[0])\n", " for i in range(len(p)-1):\n", " C.append(p[i+1]+C[i])\n", " C=[round(num,4) for num in C]\n", " return C" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1c67b0962b0>]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "CDF=cdf(P)\n", "plt.plot(CDF)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.013140145129042638" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from scipy import stats\n", "stats.binom_test(96,200,p=0.4,alternative=\"greater\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.2645540682891903" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "SE=(0.48*0.52/200)**.5\n", "SE\n", "Tstat=(0.48-0.40)/SE\n", "Tstat" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "MADE=np.random.binomial(500,0.45,size=100)\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([ 3., 6., 10., 20., 18., 15., 9., 10., 5., 4.]),\n", " array([203. , 208.1, 213.2, 218.3, 223.4, 228.5, 233.6, 238.7, 243.8,\n", " 248.9, 254. ]),\n", " <BarContainer object of 10 artists>)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.hist(MADE)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "227.46" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.std(MADE,ddof=1)\n", "SE=np.std(MADE,ddof=1)/10\n", "SE\n", "np.mean(MADE)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0x1c67ce06fa0>" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAFgCAYAAACVLS/VAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAARWUlEQVR4nO3de4zld1nH8c/DbkuRiwWp2G53LShBq4lChqLFGERi1oqghljwHi+LMRBQQVESo3+YiBhFjVE2FeV+EYvRilxU6iVcZFuhUooKCHZptVsNgpoAWx7/OGd1WGd3zn5nfuecKa9XcrLn8ju/7zMzp33PuU51dwDgXN1j1QMAsDcJCABDBASAIQICwBABAWDI/lUPsNnhw4f7DW94w6rHANiJWvUAy7JW90DuvPPOVY8AwILWKiAA7B0CAsAQAQFgiIAAMERAABgiIAAMERAAhggIAEMEBIAhAgLAEAEBYMikH6ZYVR9K8vEkdyU52d0bU64HwPIs49N4v667fUoiwN2Mh7AAGDJ1QDrJm6rqhqo6stUGVXWkqo5V1bETJ05MPM56O3DwUKpq8sOBg4dW/aUCdwPV3dPtvOqS7r6tqj4/yZuTPL27//JM229sbPSxY8cmm2fdVVWufuFbJ1/n1U+9MlP+3OGznD8otRu6+7b5v3ckeV2SK6ZcD4DlmSwgVXXvqrrvqeNJviHJe6ZaD4DlmvJVWA9K8rqqOrXOK7rbHzwHuJuYLCDd/cEkXzHV/gFYLS/jBWCIgAAwREAAGCIgAAwREACGCAgAQwQEgCECAsAQAQFgiIAAMERAABgiIAAMERAAhggIAEMEBIAhAgLAEAEBYIiAADBEQAAYIiAADBEQAIYICABDBASAIQICwBABAWCIgAAwREAAGCIgAAwREACGCAgAQwQEgCECAsAQAQFgiIAAMERAABgiIAAMERAAhggIAEMEBIAhAgLAEAEBYIiAADBEQAAYIiAADBEQAIYICABDBASAIQICwBABAWCIgAAwREAAGCIgAAyZPCBVta+q/raqrpt6LQCWZxn3QJ6R5JYlrAPAEk0akKq6NMk3JblmynUAWL6p74G8IMlPJPn0mTaoqiNVdayqjp04cWLicUiS3GN/qmrSw4GDh1b9VQIT2z/Vjqvq8Unu6O4bquoxZ9quu48mOZokGxsbPdU8bPLpk7n6hW+ddIlXP/XKSfcPrN6U90AeneQJVfWhJK9K8tiqetmE6wGwRJMFpLt/qrsv7e7Lkjw5yZ9393dNtR4Ay+V9IAAMmew5kM26+/ok1y9jLQCWwz0QAIYICABDBASAIQICwBABAWCIgAAwREAAGCIgAAwREACGCAgAQwQEgCECAsAQAQFgiIAAMERAABgiIAAMERAAhggIAEMEBIAhAgLAEAEBYIiAADBEQAAYIiAADBEQAIYICABDBASAIQICwBABAWCIgAAwREAAGCIgAAwREACGCAgAQwQEgCECAsAQAQFgiIAAMERAABgiIAAMERAAhggIAEMEBIAhAgLAEAEBYIiAADBEQAAYIiAADBEQAIYICABDBASAIQICwJDJAlJVF1TV31TVu6vq5qr6uanWAmD59k+4708keWx3/2dVnZfkr6vqT7r77ROuCcCSTBaQ7u4k/zk/ed780FOtB8ByTfocSFXtq6p3JbkjyZu7+x1bbHOkqo5V1bETJ05MOQ4Au2jSgHT3Xd39lUkuTXJFVX35Ftsc7e6N7t646KKLphwHgF20lFdhdfdHk1yf5PAy1gNgelO+CuuiqrpwfvxeSR6X5H1TrQfAck35KqyLk7y4qvZlFqrXdPd1E64HwBJN+Sqsm5I8fKr9A7Ba3okOwBABAWCIgAAwREAAGCIgAAxZKCBV9ehFzgPgs8ei90B+fcHzAPgscdb3gVTVVye5MslFVfVjmy66X5J9Uw4GwHrb7o2E5ye5z3y7+246/2NJnjTVUACsv7MGpLv/IslfVNXvdveHlzQTAHvAoh9lcs+qOprkss3X6e7HTjEUAOtv0YD8XpLfSnJNkrumGweAvWLRgJzs7t+cdBIA9pRFX8b7R1X1I1V1cVU94NRh0skAWGuL3gP53vm/z950Xid5yO6OA8BesVBAuvvBUw8CwN6yUECq6nu2Or+7X7K74wCwVyz6ENYjNx2/IMnXJ7kxiYAAfJZa9CGsp28+XVWfm+Slk0wEwJ4w+nHu/53kobs5CAB7y6LPgfxRZq+6SmYfovilSV4z1VAArL9FnwP5pU3HTyb5cHcfn2AeAFagqg4n+dXM7iRc092/sN11FnoIa/6hiu/L7BN575/kkzuYE4CzqP3n3VZVvWuH/efddtb1qvYl+Y0k35jk8iRPqarLt5tz0Yewvj3J85Ncn6SS/HpVPbu7X7vI9QE4B3edvPgLf/K663drdx9+3uMfs80mVyR5f3d/MEmq6lVJnpjkvWe70qIPYT03ySO7+475zi9K8qdJBARg7zuQ5NZNp48nedR2V1r0VVj3OBWPuX87h+sCsN5qi/N6i/M+w6L3QN5QVW9M8sr56auTvH7B6wKw3o4nObjp9KVJzvq8SbL930T/4iQP6u5nV9W3JfmazEr1tiQvH58VgDXyziQPraoHJ/lIkicn+Y7trrTdPZAXJPnpJOnua5NcmyRVtTG/7JuHxwVgLXT3yap6WpI3ZvYy3hd1983bXW+7gFzW3TdtsdixqrpsaFIAzm7f/tsXeOXUOe1vu026+/U5x6cmtgvIBWe57F7nshAAi+mTn7pk1TMsYrtXUr2zqn7o9DOr6geS3DDNSADsBdvdA3lmktdV1Xfm/4KxkeT8JN864VwArLmzBqS7/zXJlVX1dUm+fH72H3f3n08+GQBrbdG/B/KWJG+ZeBYA9hDvJgdgiIAAMERAABgiIAAMERAAhggIAEMEBIAhAgLAEAEBYIiAADBEQAAYIiAADBEQAIYICABDBASAIQICwBABAWDIZAGpqoNV9ZaquqWqbq6qZ0y1FgDLt9CftB10MsmPd/eNVXXfJDdU1Zu7+70TrgnAkkx2D6S7b+/uG+fHP57kliQHploPgOVaynMgVXVZkocneccWlx2pqmNVdezEiRND+z9w8FCqatLDgYOHdvZNALibmfIhrCRJVd0nye8neWZ3f+z0y7v7aJKjSbKxsdEja9x2/NZc/cK37mjO7bz6qVdOun+AvWbSeyBVdV5m8Xh5d1875VoALNeUr8KqJL+d5Jbu/uWp1gFgNaa8B/LoJN+d5LFV9a754aoJ1wNgiSZ7DqS7/zpJTbV/AFbLO9EBGCIgAAwREACGCAgAQwQEgCECAsAQAQFgiIAAMERAABgiIAAMERAAhggIAEMEBIAhAgLAEAEBYIiAADBEQAAYIiAADBEQAIYICABDBASAIQICwBABAWCIgAAwREAAGCIgAAwREACGCAgAQwQEgCECAsAQAQFgiIAAMERAABgiIAAMERAAhggIAEMEBIAhAgLAEAEBYIiAADBEQAAYIiAADBEQAIYICABDBASAIQICwBABAWCIgAAwREAAGCIgAAwREACGCAgAQyYLSFW9qKruqKr3TLUGAKsz5T2Q301yeML9A7BCkwWku/8yyb9PtX8AVstzIAAM2b/qAarqSJIjSXLo0KEVT3MW99ifqlr1FHvHEr5f+867Z+761CcmXSNJLrn0YD5y6z9Pvg6LO3DwUG47fuuka/i5b2/lAenuo0mOJsnGxkaveJwz+/TJXP3Ct066xKufeuWk+1+qJX2/pl7j1Dqsl9uO3+q/xzXgISwAhkz5Mt5XJnlbkodV1fGq+oGp1gJg+SZ7CKu7nzLVvgFYPQ9hATBEQAAYIiAADBEQAIYICABDBASAIQICwBABAWCIgAAwREAAGCIgAAwREACGCAgAQwQEgCECAsAQAQFgiIAAMERAABgiIAAMERAAhggIAEMEBIAhAgLAEAEBYIiAADBEQAAYIiAADBEQAIYICABDBASAIQICwBABAWCIgAAwREAAGCIgAAwREACGCAgAQwQEgCECAsAQAQFgiIAAMERAABgiIAAMERAAhggIAEMEBIAhAgLAEAEBYIiAADBEQAAYIiAADBEQAIYICABDJg1IVR2uqr+vqvdX1XOmXAuA5ZosIFW1L8lvJPnGJJcneUpVXT7VegAs15T3QK5I8v7u/mB3fzLJq5I8ccL1AFii6u5pdlz1pCSHu/sH56e/O8mjuvtpp213JMmR+cmHJfn7BXb/wCR37uK4O7FOsyTm2c46zbNOsyTm2c6i89zZ3YenHmYd7J9w37XFef+vVt19NMnRc9px1bHu3hgdbDet0yyJebazTvOs0yyJebazbvOsgykfwjqe5OCm05cmuW3C9QBYoikD8s4kD62qB1fV+UmenOQPJ1wPgCWa7CGs7j5ZVU9L8sYk+5K8qLtv3qXdn9NDXhNbp1kS82xnneZZp1kS82xn3eZZucmeRAfg7s070QEYIiAADFnLgFTVA6rqzVX1j/N/73+G7c76USlV9ayq6qp64CrnqarnV9X7quqmqnpdVV04OMd2X29V1a/NL7+pqh6x6HWXNUtVHayqt1TVLVV1c1U9Y6ez7GSeTZfvq6q/rarrVj1PVV1YVa+d32ZuqaqvXvE8Pzr/Wb2nql5ZVRdMPMuXVNXbquoTVfWsc7nuMueZ6ra8p3T32h2S/GKS58yPPyfJ87bYZl+SDyR5SJLzk7w7yeWbLj+Y2RP4H07ywFXOk+QbkuyfH3/eVtdfYIazfr3zba5K8ieZvQfnq5K8Y9HrLnGWi5M8Yn78vkn+YSez7HSeTZf/WJJXJLluF26/O5onyYuT/OD8+PlJLlzVPEkOJPmnJPean35Nku+beJbPT/LIJD+f5Fnnct0lz7Prt+W9dljLeyCZfeTJi+fHX5zkW7bYZruPSvmVJD+RLd68uOx5uvtN3X1yvt3bM3tPzLla5KNhnpjkJT3z9iQXVtXFC153KbN09+3dfWOSdPfHk9yS2f+kdmIn35tU1aVJvinJNTucY8fzVNX9knxtkt9Oku7+ZHd/dFXzzC/bn+ReVbU/yedkZ+/n2naW7r6ju9+Z5FMDX8fS5pnotrynrGtAHtTdtyezH1JmvwGc7kCSWzedPj4/L1X1hCQf6e53r8M8p/n+zH7TO1eL7P9M2yw62zJm+V9VdVmShyd5xw5m2Y15XpDZLxuf3uEcuzHPQ5KcSPI784fUrqmqe69qnu7+SJJfSvLPSW5P8h/d/aaJZ5niupPucxdvy3vKygJSVX86f0z19MOiv1Fs+VEpVfU5SZ6b5GfWYZ7T1nhukpNJXn4usy26/7Nss9DHyixpltmFVfdJ8vtJntndH9vBLDuap6oen+SO7r5hhzPsyjyZ/bb/iCS/2d0PT/JfmT1supJ5avZ83xOTPDjJJUnuXVXfNfEsU1x3sn3u8m15T5nys7DOqrsfd6bLqupfTz3cMb8bfccWm53po1K+KLMb+7ur6tT5N1bVFd39LyuY59Q+vjfJ45N8fXeP3OgX+WiYM21z/gLXXdYsqarzMvsP7uXdfe0O5tiNeZ6U5AlVdVWSC5Lcr6pe1t07+Z/kTubpJMe7+9Rvsq/NzgOyk3kel+SfuvtEklTVtUmuTPKyCWeZ4rqT7HOC2/LesuonYbY6JHl+PvNJ61/cYpv9ST6YWSxOPfn1ZVts96Hs/En0Hc2T5HCS9ya5aAczbPv1ZvY4/uYnQv/mXL5XS5qlkrwkyQt28fYyPM9p2zwmu/Mk+o7mSfJXSR42P/6zSZ6/qnmSPCrJzZk991GZPQf49Cln2bTtz+Yzn7Te1dvxLsyz67flvXZY+QBn+EF9XpI/S/KP838fMD//kiSv37TdVZm98uEDSZ57hn19KDsPyI7mSfL+zB5nfdf88FuDc/y//Sf54SQ/PD9emf0Rrw8k+bskG+fyvVrGLEm+JrPfsm/a9P24alXznLaPx2QXArILP6uvTHJs/j36gyT3X/E8P5fkfUnek+SlSe458SxfkNk9g48l+ej8+P2muB3vZJ6pbst76eCjTAAYsq6vwgJgzQkIAEMEBIAhAgLAEAEBYIiAADBEQAAY8j8Jpkdhjv27aAAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 402.375x360 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "outperformance=np.random.normal(loc=0.01,scale=0.05,size=(15,1))\n", "outperformance\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "sns.displot(outperformance)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "test_auto=stats.ttest_1samp(outperformance,0.005, axis=0)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Ttest_1sampResult(statistic=array([0.41091271]), pvalue=array([0.68735696]))" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_auto" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1c67d1dbfd0>]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAA51ElEQVR4nO3deXicV3nw/+89MxrtsqzNlrXYsrzvkeU9tmMSgrOAyQIkFEIJvMYlodDCD+jbvtBCNwqlpSUhDQEaoG0IJCEhceOEkMTxbtmW902WF63WYlmStWvm/P54RrEiy9ZImplnZnR/rkvXM8uz3OPlnqP7nOccMcaglFIqejnsDkAppVRwaaJXSqkop4leKaWinCZ6pZSKcprolVIqyrnsDmAwGRkZZsqUKXaHoZRSEWPfvn0NxpjMwd4Ly0Q/ZcoUSkpK7A5DKaUihoicv957WrpRSqkop4leKaWinCZ6pZSKcprolVIqyvmV6EVkvYicFJEyEfn6DfZbIiIeEbl/uMcqpZQKjiETvYg4gceAO4A5wIMiMuc6+30H2DLcY5VSSgWPPy36pUCZMabcGNMNPANsGGS/LwDPAXUjOFYppVSQ+JPoc4CKfs8rfa+9S0RygHuAJ4Z7rFJqBNovQflbsPNxqD5gdzQqzPlzw5QM8trASez/FfiaMcYj8p7d/TnW2lFkI7ARID8/34+wlBqj3vk+vPEt3v2vJE5Y9xdw85+Dw2lraCo8+ZPoK4G8fs9zgeoB+xQDz/iSfAZwp4j0+nksAMaYJ4EnAYqLi3U1FKUGs+9peONvYPYHofgzkFZgJf0//C2c3Qof/zXExNkdpQoz/iT6vcB0ESkAqoAHgI/338EYU9D3WET+E3jZGPNbEXENdaxSyk8nXoGXvwTTboP7fwbOGOv1+34CBWvgd1+E7f8Kt+jgNvVeQ9bojTG9wKNYo2mOA88aY46KyCYR2TSSY0cftlJjTFsjPL8RshfBR56+muQBRGDxH8O8++Gdf4aGMruiVGFKwnHN2OLiYqOTmqlI09TWzdHqFk5dbOV8YxvrZmVxy8yswJz89W/C9h/A53dB1qzB92m9CD9cApMWwkMvWV8AaswQkX3GmOLB3gvL2SuVijSvH7vII/+9n+5eLwBup4Ond57n7gXZfOODc8hKHkXd/Eo97HkS5t9//SQPkDwBbvsGvPJlOPwbWPCRkV9TRRVN9EqN0suHqvnSM6XMzRnHVz8wk+kTkhgXH8MTb5Xz2JtlbD1Vzw8euIl1s0bYut/xA+jthLVfG3rfxQ9Dyc9g279YXwzaqlfoXDdKjcpz+yr50/85wE35qfzyM0tZNS2DrOQ4Yl1OvnjbdP73S6uZlBrPF585QPXljuFf4Eod7HkK5n8EMqYPvb/DAUv/D9QdhYrdw7+eikqa6JUaoX3nL/GV3xxk+dR0nn54KclxMdfsU5iZxBOfWIzHa/izX5Xi8Q6zT2zvU1Zrfs1X/T9m3v0QmwIlPx3etVTU0kSv1Ah4vIZvvnSUCclx/PihYhLc16+CTslI5K8/NJfdZy/xH1vP+H8RY+DQr2DqWsiY5v9xsUmw4GNw9AVrtI4a8zTRKzUCz5ZUcKSqhb+4cxaJsUN3dd2/OJe75mfz/ddOcbDisn8XqdwLTeespD1cxQ+DpxtKfzn8Y1XU0USv1DA1d/Tw3S0nWTJlPB9aOMmvY0SEv79nPhlJsfzN747i17Dmg8+AKx5m3T38ICfMgfwVVses1zv841VU0USv1DD94PenaWrv5psfnIsMY1TLuIQYPr+ukP0XLrP77KUb79zbDUefh1l3QlzKyAJd/MfQdBaq9J6UsU4TvVLDcLahjad3nuPBpfnMyxk37OM/WpxHRpKbx98aolZf9nvoaBpZ2abPjPXgcMGJl0d+DhUVNNErNQxP7ziHQ+BLt/kx1HEQcTFOHr65gK2n6jlS1Xz9HQ/9ChLSofB9I4wUiE+FKavhxOaRn0NFBU30SvmprauX5/ZVctf87FHd6fqJ5ZNJjnXxo+u16rvb4dSrMPee985pMxKz7oLG01B/anTnURFNE71SfnrhQBWtXb18csWUUZ0nJS6GT66YzOYjNZypv3LtDue2WWPnZ94xqusAMPNOa6vlmzFNE71SfjDG8Iud55k7KYWi/NRRn+/hmwtwOx38eGv5tW+WvW6Ntpl886ivw7gcmFRkTXGsxixN9Er5Yc/ZS5y82MpDKyYPa6TN9WQkxbJh0SR+d7Cajm7P1TeMgdOvWfPLB2oBkVl3WSNvWmoCcz4VcTTRK+WHn+86z7j4GD60MHBLHt9blEtbt4fXjtVefbHxjHWT1PT3B+w6zLrL2p7UTtmxShO9UkO42NLJliO1fGRxLvHuwK3JunRKGjmp8Ty3v+rqi2WvW9tptwXsOmTOgvFT4PTrgTuniih+JXoRWS8iJ0WkTESuWadMRDaIyCERKRWREhG5ud9750TkcN97gQxeqVB4sbSKXq/h48sCu2i9wyHcc1MO207XU9fSab14+nVIn26tBRsoIjD1Fji/HTy9gTuvihhDJnoRcQKPAXcAc4AHRWTOgN3eABYaYxYBDwNPDXh/nTFm0fVWP1EqnL18qIb5OeOYmpkU8HPfU5SD18CLpdXWsMpz2wJbtulTsBa6WqCmNPDnVmHPnxb9UqDMGFNujOkGngE29N/BGHPFXJ28IxEIv/UJlRqBC43tHKps5u4F2UE5f2FmEgvzUnn+QBWcewc8XUFK9Gus7dm3A39uFfb8SfQ5QEW/55W+195DRO4RkRPAK1it+j4GeE1E9onIxutdREQ2+so+JfX19f5Fr1SQvXLYGqly5/zgJHqA+4pyOF7TQuPh18AVB/krA3+RxAyYMA/KNdGPRf4k+sHGkl3TYjfGvGCMmQV8GPh2v7dWGWOKsEo/j4jImsEuYox50hhTbIwpzszM9CMspYLvlcPVLMpLJS8tIWjXuHvBJFwOofvMO5C7JHDDKgcqWGOtOtXTGZzzq7DlT6KvBPL6Pc8Fqq+3szFmK1AoIhm+59W+bR3wAlYpSKmwd66hjSNVLUEr2/RJS3Rzx/R4stpPYyYHoTXfp2Ctdcdt5Z7gXUOFJX8S/V5guogUiIgbeAB4qf8OIjJNfHeRiEgR4AYaRSRRRJJ9rycCtwNHAvkBlAqWUJRt+nwkqxonXs4lLgreRSavBHHC2a3Bu4YKS0MmemNML/AosAU4DjxrjDkqIptEZJNvt/uAIyJSijVC52O+ztkJwDYROQjsAV4xxrwahM+hVMC9fKiGxZPHMyk1PujXWsIxuo2Tly/lBu8icSmQU6R1+jFo6DXQAGPMZmDzgNee6Pf4O8B3BjmuHFg4yhiVCrkz9Vc4XtPCN+4eOJI4OOKrd3HCPZPNJ5v5wvogXqhgDWz7V+hqhdjkIF5IhRO9M1apQbx6xJqWIBRlG7quQPUBOiYt43hNCxWX2oN3rSk3g/FY69GqMUMTvVKDePNEHfNyUpg4LkgjYPqr3APGQ/YCa/z8749fDN61copBHFChHbJjiSZ6pQa43N7N/gtNvG9mVmgueG47iJOJ89YwLSuJ148FMdHHpUDWXLiwK3jXUGFHE71SA7x9qh6vgXWzQpToz2+H7IUQm8z750xg99lLNLf3BO96eUuhsgS8nqH3VVFBE71SA7x5oo60RDcLclODf7HeLqjaZw19BG6bPQGP1/DmybrgXTN/OXS3Qt2x4F1DhRVN9Er14/Ea3j5Vzy0zMnE6Rr/AyJBqD4On22plAzflpZKRFBvc8o3vWlTsDt41VFjRRK9UP6UVTTS194SubNPXKZprJV+HQ7h1VhZbT9XT4/EG55qpkyFpIlzQRD9WaKJXqp8/nKjD6RDWzAjRfEuVeyElF1KuDuNcOzOT1q5eDlZcDs41RaxWvbboxwxN9Er18+aJehZPHs+4+JjQXLCyBHLfu0zDqsIMHAJbTwVxFtf85XD5PLTWDr2vinia6JXyqW3u5FhNC+tCNayytRaaL1gzVvYzLiGGhXmpbD3dELxr5y2ztjrMckzQRK+Uz1u+kS7vC1V9vtK3suaARA+wZnomhyovc7m9OzjXnrjAmvteyzdjgiZ6pXzeOd3AxJQ4ZkwI/JKBg6rcC44Yawz9AGtmZOA1sL2sMTjXdrkhe5E1tFNFPU30SgFer2H7mQZunp6Bb8bt4KssgewFgy40sjA3leQ4V3Dr9DmLoeYQeIJ4c5YKC5rolQKO1bRwub2Hm6dlhOaCnl6o3j9o2QbA5XSwqjCDd07Xc3U55gDLKYLeDqg7Hpzzq7ChiV4prLINwMpp6aG5YN0x6Gm/bqIHWDMjk+rmTs7UXwlODDlF1lbLN1FPE71SwPayBmZOSCYrOQSzVcLVaYJvkOhXT7d+u9h6Kkijb8YXQPx46zcLFdX8SvQisl5ETopImYh8fZD3N4jIIREpFZESEbnZ32OVsltnj4c95y6xKlRlG7CSa0I6pOZfd5e8tASmZiSy9XSQ6vQiMKkIqjTRR7shE72IOLGWB7wDmAM8KCIDl915A1hojFkEPAw8NYxjlbLVvvNNdPd6321Bh0TVASvJDtHxu3p6BrvKG+nqDdJMkzmLrTJSd1twzq/Cgj8t+qVAmTGm3BjTDTwDbOi/gzHmirnaY5QIGH+PVcpu75xuwOUQlhakheaC3e1Qfxwm3TTkriunZdDZ4+VgRXNwYslZDMZrjb5RUcufRJ8DVPR7Xul77T1E5B4ROQG8gtWq9/tY3/EbfWWfkvr6IA4pU2qA7WUNFOWPJzHWryWUR6/2sJVc+zpDb2B5QToisONMkOr02iE7JviT6Af73fKa8V7GmBeMMbOADwPfHs6xvuOfNMYUG2OKMzNDNKGUGvOa2ro5Ut0c+vo8WDcsDWFcQgzzJo1jx5kg3TiVlAXj8jTRRzl/En0lkNfveS5Qfb2djTFbgUIRyRjusUqF2s7yRoyBm0NZn68+AMnZ75mx8kZWFqZz4EITHd3BqtMX6cibKOdPot8LTBeRAhFxAw8AL/XfQUSmie92QhEpAtxAoz/HKmWnbWUNJMW6WJg7LnQXrfZ1xPppRWE6PR5DyflLwYlnUhE0nYO2IP3WoGw3ZKI3xvQCjwJbgOPAs8aYoyKySUQ2+Xa7DzgiIqVYo2w+ZiyDHhuEz6HUiOw608jSgjRczhDdUtLZAg2n/eqI7bNkShouhwSvfNMXS82B4Jxf2c6v3idjzGZg84DXnuj3+DvAd/w9VqlwcLGlk/KGNh5Ymjf0zoFScxAww0r0ibEuFualsjNYib5vUrWagzDttuBcQ9lK74xVY9aucitxrpga4vo8DCvRg1WnP1R5mZbOIExAFp9q3SVbXRr4c6uwoIlejVk7zzSSEudizqSU0F20+oB1N2zi8ObUWVGYjtfA3rPBqtMvgprS4Jxb2U4TvRqzdpU3srQgHacjRNMSgzW6ZZiteYCi/PG4XY7g1emzF8HlC9AepC8SZStN9GpMqmnu4FxjO8unhuhuWICOJmt0ix/j5weKi3FSPHl8EDtkF1lbbdVHJU30akzq69hcURiiaYnh6jQDfUl1mFZMTed4TQuX2oKwvGBfh6zW6aOSJno1Ju0qbyQ1IYbZE0NYn685aG0nXrt0oD/6vpT2BKNOHz8exk/RFn2U0kSvxqSd5Y0sK0jDEcr6fE2pNd3AMDti+yzITSUuxvHuaKGAy16oLfoopYlejTmVTe1UXOpg+dQQlm3AatEPshC4v9wuB4snjw9iol8El89rh2wU0kSvxhxb6vOdLdBYNqKO2P6WF6Rz8mIrl9uDUKd/t0P2YODPrWyliV6NObvKL5GW6GZGVnLoLnrxiLUdRYseYHlhOsbA7mDU6fu+hLROH3U00asxZ/dZG+rzfbXvUSb6BbnjiHUFqU6fkGbdzKV1+qijiV6NKZVN7VQ2dbAsVKtJ9ak5CEkTIXnCqE4T63L66vRBqqNnL4RaXW0q2miiV2PKbl+CXB7K+jxYiX6E4+cHWj41nRO1LcGp02cvhEvl0BmkpQuVLTTRqzGlb/x8SOvz3e3QcHLUZZs+y6dadfqgjKfvq9PXHgn8uZVtNNGrMWX32UssnRLi+vzFI9YasQFK9Avz+ur0QUj0ExdYWx15E1U00asxo/pyBxcutdszfh4CluhjXU6K8sez+2wQOmSTJ1h9CZroo4pfiV5E1ovISREpE5GvD/L+H4nIId/PDhFZ2O+9cyJyWERKRaQkkMErNRx9iXFZKCcyA2u4YkI6pOQE7JTLp6ZzrKaF5vYgzE+vHbJRZ8hELyJOrOUB7wDmAA+KyJwBu50F1hpjFgDfBp4c8P46Y8wiY0xxAGJWakR2l18iJc7FrFDObwPWZGbZC0ECVy5aNjXNqtOfC0adfiHUn7D6FlRU8KdFvxQoM8aUG2O6gWeADf13MMbsMMY0+Z7uAnIDG6ZSo2fL/PO93VB3/GrtO0AW5aXidjnYHYzx9NkLrD6FumOBP7eyhT+JPgeo6Pe80vfa9XwG+N9+zw3wmojsE5GN1ztIRDaKSImIlNTX1/sRllL+u9jSGfr55wHqj4O3J2D1+T5xMU5uyktlVzDq9O+uIVsa+HMrW/iT6Adr/phBdxRZh5Xov9bv5VXGmCKs0s8jIrJmsGONMU8aY4qNMcWZmZl+hKWU//ruJI30jtj+lk1N51h1S+DXkR2XZ01bXKN1+mjhT6KvBPL6Pc8FqgfuJCILgKeADcaYd5sZxphq37YOeAGrFKRUSO0qv0RynIvZ2aGuzx8Ed7K1+HaALZ+ahtdASaDr9CLWF5OOvIka/iT6vcB0ESkQETfwAPBS/x1EJB94HvikMeZUv9cTRSS57zFwO6B3YqiQ2322kSVT0kJbnwdfR+wCcAR+JHNR/njcziCOp687Bp4gjOpRITfkvz5jTC/wKLAFOA48a4w5KiKbRGSTb7dvAOnA4wOGUU4AtonIQWAP8Iox5tWAfwqlbqCupZPy+jZWhLps4/VYN0sFuCO2T1yMk0V5qUHqkF0IHl9Hsop4Ln92MsZsBjYPeO2Jfo8/C3x2kOPKgcAXJ5Uahl2+qQJCPn6+sQx62oNSn++zbGoaj71ZRmtnD8lxMYE78btTIfh+I1ERTe+MVVFvV3kjybEu5thRn4egJsrlU9OtOv35pqF3Ho60qeBO0jp9lNBEr6Le7vJGlhSk4XKG+J97zUFwxUHGzKBdoih/PDFOCfz89A4HTJyviT5KaKJXUa2utZMz9W2hHz8PVpKcMBecflVIRyTe7WRBbuq70y8HVPZCaxZLryfw51YhpYleRbV3558PdUesMVZ9O0gdsf0tn5rG4apmrnT1BvbE2Quhpw0azwT2vCrkNNGrqLarvJEkO+rzTeesxTuC2BHbZ/nUdDxeE/jx9DplcdTQRK+i2q7yRpZMGW9PfR5CkugXT7bq9DsDXafPnAnOWJ0KIQpooldR62p9PsRlG7ASvcMFWQMneg28BLeLhbmpgb9xyhlj9THolMURTxO9ilp9S+3ZluizZkNMXEgut6IwnSNVzbQGet6bvqkQzKDTW6kIoYleRa2++vzcSSGuzxtjJccQlG36rPDV6fcGuk6fvcDqa7h8PrDnVSGliV5FrZ1nGim2oz7fUg3tDVfvLg2BosnWvDc7zwS4Tv/ulMXaIRvJNNGrqHSxxarPryy0qWwDIW3Rx8U4WZQfhDp91lwQpyb6CKeJXkWlvjtFVxZmhP7iNaUgDqsjM4RWTE3naHUzzR0BrNPHxFl9DZroI5omehWVdpQ1Mi4+JvTzz4OVFDNmgDsxpJddUWjNe9PXCR0w2QuhulQ7ZCOYJnoVlXaUN7CswIb55yHkHbF9FuWlEusKRp1+kdXn0FIV2POqkNFEr6JOxaV2Ki512FOfb70IrTW2JPq4GCdF+eMDP8HZpEXWtro0sOdVIaOJXkWdvjtEV06zoT7fd3ORDYkerPLN8doWLrd3B+6kE+ZZfQ5ap49YfiV6EVkvIidFpExEvj7I+38kIod8PztEZKG/xyoVaDvPNJKR5GZ6VlLoL97X6p04P/TXxkr0xhDYVr07ATJn6VQIEWzIRC8iTuAx4A5gDvCgiAy8r/sssNYYswD4NvDkMI5VKmCMMew408DyqemI2FGfL4W0QogbF/prY9XpE91OtpcFoU6vHbIRy58W/VKgzBhTbozpBp4BNvTfwRizwxjTt8TNLiDX32OVCqTyhjYutnTZM6wSoPoATLrJnmsDMU4HSwvS2F7WENgTT1oEbXVW/4OKOP4k+hygot/zSt9r1/MZ4H+He6yIbBSREhEpqa+v9yMspa7VN+LElo7YK3XWyBQbEz3AqmkZlDe0UX25I3An7etz0A7ZiORPoh/s999Bf38TkXVYif5rwz3WGPOkMabYGFOcmZnpR1hKXWvnmUYmjYtjcnpC6C/elwTDINEDgW3VT5zv65AtDdw5Vcj4k+grgbx+z3OB6oE7icgC4ClggzGmcTjHKhUIXq+vPl9oU32++gAgQV0M3B8zJySTnuhmRyDH07sTrZvAtEUfkfxJ9HuB6SJSICJu4AHgpf47iEg+8DzwSWPMqeEcq1SgHK1uoam9h9XTbarP15RCxnSITbbn+j4Oh7ByWgbbyhowgew8zV6kQywj1JCJ3hjTCzwKbAGOA88aY46KyCYR2eTb7RtAOvC4iJSKSMmNjg3C51CKd8qsvp1VdoyfB9s7YvtbVZhOfWsXZXVXAnfSSYvgSi20aIdspPFreXpjzGZg84DXnuj3+LPAZ/09Vqlg2Ha6gVkTk8lKDs1iH+/RWuu7I3ZR6K89iP51+ukTAvQbRt+XWPUBSMkOzDlVSOidsSoqdHR7KDnXxM22teZLrW2YtOjz0hLIT0tgWyDH009cYE1ZXL0/cOdUIaGJXkWFPecu0e3xcrNd9fnqA9aoFJvuiB3Mqmnp7C5vpNfjDcwJ3QnWlMVVmugjjSZ6FRW2na7H7XSwrMCG8fPg64idAbE2TLtwHaumZdDa1cuhqubAnXTSTVaLXu+QjSia6FVUeOd0A4snjyfe7Qz9xY0Jq47YPisLMxCBd04FcDx9ThF0NEHTucCdUwWdJnoV8epbuzhR22pf2aalGq5cDLtEn5boZkHOON4+VRe4k04qsrZV+wJ3ThV0muhVxOu7A9S28fNVJdY2p9ie69/A2hmZlFZcprk9QMsLTpgLzljfzWEqUmiiVxHvndMNpCbEMHeSPTNGUrUPnG6YOM+e69/A2pmZeA1sC9R0CM4Y685f7ZCNKJroVUQzxrCtrJ5VhRn2LBsIULnPGm3jirXn+jewMDeV5DhX4Ms3NQfB6wncOVVQaaJXEe14TSsXW7pYO9OmifC8HquMkbPYnusPweV0sHp6Bm+fqg/cdAg5RdDTBvUnA3M+FXSa6FVEe8vXUr1lhk2Jvv6ElfTCsD7fZ+2MTC62dHHqYoCmQ+jrkNUbpyKGJnoV0d46Uc/cSSlkpdgw7QFcHX0Spi16gDW+L8GAlW/Sp0Fsio68iSCa6FXEam7vYd+FJtbNzLIviMoSa9nA9EL7YhhC9rh4ZkxIYmugxtM7HNZQ0sqSwJxPBZ0mehWx3imrx+M1rJtl40I1Vfut1rwd898Pw9oZmew5e4n27t7AnDB3CVw8Ct1tgTmfCipN9CpivXmintSEGBbljbcngO42qDsa1mWbPmtmZNLt8b671OKo5S0F49Hx9BFCE72KSF6v4e1TdayZnmnfsMqag2C8Yd0R22dpQRoJbid/OBGgOn3fZ67cG5jzqaDyK9GLyHoROSkiZSLy9UHenyUiO0WkS0S+MuC9cyJyuP+CJEqN1pHqZhqudNtbtumrUUdAiz7W5WTN9EzeOF4XmGGWiemQVggVmugjwZCJXkScwGPAHcAc4EERmTNgt0vAnwLfu85p1hljFhljwr/poyLCmyfqEYE10+1M9HsgdTIkRcZi9rfOzqK2pZOj1S2BOWHuEqtFrzNZhj1/WvRLgTJjTLkxpht4BtjQfwdjTJ0xZi8QoAk1lLqxt07VsTA3lfQkm+5GNQYq9kD+cnuuPwLvm5WFCPz++MXAnDBvCbTVweXzgTmfChp/En0OUNHveaXvNX8Z4DUR2SciG4cTnFKDqW/torTiMrfYdTcsWMntykWrUzJCpCfFUpQ/PnCJPneJtdVhlmHPn0Q/WE/XcH5XW2WMKcIq/TwiImsGvYjIRhEpEZGS+vr6YZxejTVvHL+IMXD7nIn2BXFht7XNW2ZfDCNw6+wsjlS1UNPcMfqTZc2FmETrNxsV1vxJ9JVAXr/nuUC1vxcwxlT7tnXAC1iloMH2e9IYU2yMKc7MjIyap7LHlqO15KXFMzs7QItej0TFbnAnQ9bA7qrw9v7ZEwB443gARt84Xda8NzryJuz5k+j3AtNFpEBE3MADwEv+nFxEEkUkue8xcDtwZKTBKnWlq5ftZY3cPmciYudNShV7ILcYHDasaDUK07KSyE9L4I2AlW+KofYQ9ATgNwQVNEMmemNML/AosAU4DjxrjDkqIptEZBOAiEwUkUrgz4G/EpFKEUkBJgDbROQgsAd4xRjzarA+jIp+b5+sp9vj5QNzbSzbdLZYN0pFWNkGQES4bfYEtp9pDMxdsrlLwdurN06FOZc/OxljNgObB7z2RL/HtVglnYFagIWjCVCp/rYcrSUt0c3iyTbdDQvWilLGC/mRl+gBbpudxU+3n+Wd0w2j/8LsG3V0fgdMXjn64FRQ6J2xKmJ093p580Qdt83Osu9uWPB1PkpE3BE7mCUFaYyLj2HLkdrRnywhDTJnw4Wdoz+XChpN9Cpi7CxvpLWr196yDcCFXdbaqXEp9sYxQjFOB7fPmcDrxy7S1RuAVaImr7BGIemKU2FLE72KGK8drSXB7WTVNJsWAQcrmVWWRNT4+cHcuSCb1q5etp0OwNTF+SuhuxVqD4/+XCooNNGriOD1Gl4/dpG1MzKJi7FxpEvtYSup5a+wL4YAWFWYQUqci82HA1C+mez7s9DyTdjSRK8iQsn5Jupau1g/z+ayzblt1nbKzfbGMUpul4P3z5nI68dq6e71ju5k43JhXL7VIavCkiZ6FRF+d7CauBgHt/lu+LHNuW2QNhVSJtkbRwDctWAiLZ29bC8LQPlm8gqrRa8TnIUlTfQq7PV6vGw+XMOtsyeQGOvXiODg8Hrgwo6Ib833uXlaJslxLl45XDP6k+WvgLZ6aCwb/blUwGmiV2Fvx5lGGtu6+eACm1vRF49AZzNMjo5Eb5VvJvDa0QCUbyavsrZavglLmuhV2HvpYDXJsS57Z6uEfvX5VfbGEUB3zsumpbOXHWdGWb7JmA4JGZrow5QmehXWuno9bDlSy+1zJ9o72gbg3HYYX2B1PkaJ1TMySI5z8dJBv+cpHJyI9QV47h2t04chTfQqrL11sp7Wrl4+tMjmso3XC+e3R1VrHqwlBu9ekM2rR2pp6xrl3DdTb4GWKq3ThyFN9Cqs/e5gNWmJblYWptsbyMUj0HkZpqy2N44guLcol/ZuD1uOjnJMfcFaa1v+1qhjUoGliV6FrbauXt44Xsed8ycS47T5n+r57dZ2cnS16AGKJ48nLy2e5/dXje5EaVNhXB6cfTswgamA0USvwtYrh2vo6PGwYdFwVq4MkjNvWvX51Lyh940wIsK9N+Wy/UzD6FaeEoGpa+HsVp33Jsxooldh69m9FUzNTKTYzimJAXq7rE7GabfaG0cQ3VuUgzHw2wOj7JQtuMUaglpzMBBhqQDRRK/CUlndFUrON/HR4jx7V5ICa9nAnnYojN5EPznd+kJ9bn8lZjSjZgp8S0Jr+Sas+JXoRWS9iJwUkTIR+fog788SkZ0i0iUiXxnOsUoN5tf7KnA6hHuLwqFs8wdwuKLmjtjrubcol7K6Kxyuah75SZInWOvoaodsWBky0YuIE3gMuAOYAzwoIgNXRL4E/CnwvREcq9R79Hi8PLevivfNyiIrOc7ucKDsDWvZwAidf95fdy3Ixu1y8OuSytGdqGCtNWd/T2dgAlOj5k+LfilQZowpN8Z0A88AG/rvYIypM8bsBXqGe6xSA715oo6GK118rDgMOj6v1FuLXxeuszuSoBsXH8Nd87N54UDV6MbUT70FejuteYFUWPAn0ecAFf2eV/pe84ffx4rIRhEpEZGS+vp6P0+votGzJRVkJsfaP+UBQPmb1jaK6/P9fWJ5Ple6enmxdBSdsgVrwBUHp14LXGBqVPxJ9IP1hPnbW+P3scaYJ40xxcaY4szMMPgPrmxR19LJmyfrua8oF5fdY+fBqs/Hp0H22Fjjvih/PLOzU/j5znMj75R1J1g3lp3eEtjg1Ij58z+pEuj/O3Qu4O/X/WiOVWPQL3dfwGsMDywJg7KNMVaiL1wHDpvn2QkREeGTyydzoraV/ReaRn6iGR+AS+XQoNMhhAN/Ev1eYLqIFIiIG3gAeMnP84/mWDXGdPZ4+K9d57l1VhZTMhLtDgeqD8CVizDtNrsjCakNiyaRHOviFzvPj/wkMz5gbU+9Gpig1KgMmeiNMb3Ao8AW4DjwrDHmqIhsEpFNACIyUUQqgT8H/kpEKkUk5XrHBuvDqMj20sFqGtu6+fSqArtDsZx4BcQBM9bbHUlIJca6uG9xLpsP19JwpWtkJ0nNt4ZZavkmLPhVBDXGbDbGzDDGFBpj/s732hPGmCd8j2uNMbnGmBRjTKrvccv1jlVqIGMMP9t+jpkTku2fwKzPyc2QvxIS0uyOJOQ+sTyfbo+XX+2tGHrn65l+uzU/fecoxuWrgAiD3i6lYFf5JY7XtPDpVVPsvxMWrPpy3TGYdZfdkdhiWpb1hfvznefo6h3hvDUz1oO315onSNlKE70KCz/dfpbxCTF8+KYwuBMW4MRmazvrTnvjsNGmtYVcbOnitwdGOKtl7hKIS9U6fRjQRK9sd76xjd8fv8jHl+Xbv4pUnxOvwIR5MH6K3ZHYZvX0DOZOSuE/3i7H4x3BUEunC2beYZXAersDH6DymyZ6ZbsfvXWGGIeDh1ZMsTsUS1sDVOyCmWO3NQ/WUMs/uaWQ8oY2XhvpoiRz77Fq9Dr3ja000StbVVxq5zf7KnlgaR4TUsJgXhuwSg3GO2br8/3dMS+bKekJPP7WmZHdQDV1HcSOg6MvBD445TdN9MpWj791Boev5Rg2jjwP4/LHzN2wN+J0CJ9bW8jhqma2lzUO/wQut/WFeeIVa15/ZQtN9Mo2lU3t/Lqkgo8tySN7XLzd4Viu1Fllhvn3WSsmKe4tyiErOZZ//8PpkbXq594DXc06+sZGmuiVbR5/6wwihFdr/ugLYDww/6N2RxI2Yl1OPn9LIbvPXuLtUyOYcHDqLRA3Do79NtChKT9pole2qL7cwa9LKvhocR6TUsOkNQ9w6FlrtM0EXTahv48vm0xeWjzfefUk3uGOwHG5YdYHtXxjI030yhbf23ISQfj8uml2h3JV4xmoKoH5H7E7krDjdjn4yu0zOV7TwksHRzAv4bx7oKtFx9TbRBO9CrnSiss8f6CKz6wuICecWvOHfwMIzL/f7kjC0gcXTGLupBS+99rJ4d8tO3UdJE+C/b8ITnDqhjTRq5AyxvCt3x0lIymWR8KpNW8MHP61tS7suFy7owlLDofwtfWzqGzq4L92XRjmwU5Y9HE48wY0j/BOWzVimuhVSP3uUA37L1zmqx+YSVKsy+5wrrqwCxpPwwLthL2R1dMzWDUtnR+8cXr4M1ve9Anr/oTS/w5OcOq6NNGrkOns8fCPm48zd1IK9y0Os1bz3qesG3vm3Wd3JGFNRPjrD86lvbuXv3/l+PAOTiuwVp468AvweoMToBqUJnoVMj/8QxnVzZ38v7vn4HSE0Rj1K/Vw7EWrtOAOgwVPwtz0Ccl8bk0hzx+oYkdZw/AOLnoILp+Hc+8EJzg1KE30KiSOVDXzo7fPcF9RLsunhsl8830O/By8PbDkM3ZHEjEefd80Jqcn8Fe/PUJnzzA6Zmd/0PrNaf/TwQtOXcOvRC8i60XkpIiUicjXB3lfROTffO8fEpGifu+dE5HDIlIqIiWBDF5Fhu5eL1/59UHSE9184+4wG5/u9UDJz6BgDWRMtzuaiBEX4+TbG+ZR3tDGj9464/+BMfFQ9Ek4+ltorgxafOq9hkz0IuIEHgPuAOYAD4rIwP+tdwDTfT8bgR8NeH+dMWaRMaZ49CGrSPPDN8s4UdvK398zn3EJMXaH816nX4PmCljyWbsjiThrZmTyoYWTePytMg5XDmMVqWWbrO2ugWlCBYs/LfqlQJkxptwY0w08A2wYsM8G4OfGsgtIFZHsAMeqItCRqmYef7OMe27K4bY5E+wO51o7/t0a3z3GpyQeqW9tmEtGUixf+J/9XOnq9e+g1Dxr/pt9T+sygyHiT6LPAfovHFnpe83ffQzwmojsE5GN17uIiGwUkRIRKamvH8F8GirstHT28IX/OUBaoptvfjDMSjYA57bD+e2w6ovgDLPfNCJEaoKbf/nYIs5fauebLx71/8CVj0J3K+z7z6DFpq7yJ9EPNjxi4GQXN9pnlTGmCKu884iIrBnsIsaYJ40xxcaY4szMTD/CUuHMGMNXf32IC5fa+eHHi0hNcNsd0rW2fhcSM2Hxp+yOJKItn5rOF9ZN47n9lbxY6ufNUJNusoZa7npCV58KAX8SfSWQ1+95LjBwsovr7mOM6dvWAS9glYJUlHvqnbO8erSWr6+fxdKCNLvDuVZlCZS/CSu/YHUQqlH501uns3jyeP7yhSOcrG3176BVX4TWamtcvQoqfxL9XmC6iBSIiBt4AHhpwD4vAQ/5Rt8sB5qNMTUikigiyQAikgjcDhwJYPwqDO0qb+QfXz3B+rkT+ezqArvDGdzW70L8eCjWIZWB4HI6+OHHbyLB7eTh/9xLfasfd81Ouw3ylsPb34HutuAHOYYNmeiNMb3Ao8AW4DjwrDHmqIhsEhFf9zmbgXKgDPgx8Hnf6xOAbSJyENgDvGKM0enrotjJ2lY2/ryEyekJfPcjC5BwXLzj/E5rFsXlj0Bskt3RRI3scfH85FNLaGzrYuMvSoYeXy8C7/8buHIRdj0emiDHKBnRijFBVlxcbEpKdMh9pKlsaue+H+0A4DebVpKXlmBzRIPweuDJW6C9ER7dq3fCBsGrR2rY9Mv93L0gm3974CYcQ90F/T8fh7Nb4YsHITHMbqaLICKy73pD2PXOWBUQl9q6eeine2jv9vD0w0vDM8mDVQ+uPQTv/5Ym+SBZPy+bv7hjFi8fquFrzx0aeqGS274JPW1WCUcFRRhNH6giVX1rFw/9dA9VTR384jPLmDUxxe6QBtdxGd74FuSv1MnLguxzawtp7/bwgzdOA/Cd+xZcv2WfORMWfxr2/hgWfAxyF4cw0rFBW/RqVCqb2vnIEzs419DGjx8qDs8RNn3e+Ba0X4I7/lEX/g6BP3v/DL5463R+va+Srz13CM+NWva3fROSJsJLj+pwyyDQRK9GrKyulft/tJNLbd388rNLWTMjjO9/OLUFSn4CKx6B7IV2RzNm9E/2n316L62dPYPvGDcOPvivUHcMtn0/pDGOBZro1Yj8/thF7nlsB71ew68+t4LFk8O4JX+lHl58xFr0+9Zv2B3NmPNn75/B3354HltPN3D/j3ZS2dQ++I4zPmCt17v1e1BdGtIYo50mejUsHq/hn187yWd/XsKUjER++8hKZmeHaU0erAUuXnoUOlvg3h+DK9buiMakTyyfzH9+egnVzR18+LHtvHP6OtOcrP8OJE2AX30C2hpDG2QU00Sv/FZ1uYNP/XQP//6HMj6yOJdfb1pB7vgwHV3T582/tcbMv/9bMCEM59sZQ1ZPz+SFz68kNcHNJ3+yh799+di1i4wnpsPHfgFX6uA3nwaPnxOlqRvSRK+G5PUafrnrPLd//232X2jiH+6dzz/dv4C4GKfdod3YvqfhnX+2VjVa9jm7o1HAtKxkfvfozXxy+WSe2naWDT/czsGKy+/dKacI7v4XOPs2vPaX1sLtalR0eKW6oSNVzXzr5WPsOXuJm6dl8A/3zg/fMfL9nXoNXv4zKLwV7vq+jrIJI/FuJ9/+8DxumZnJ158/zIcf384DS/L4/z4wi7RE3+R3N/0RXDxi3TEbEw+3flP/DkdBE70aVNXlDr635SQvHKgiLdHNP9w7nweW5IXnlAYDHXkent8IE+bCR/5TpyAOU7fOnsAfvpzGD35/mp/tOMfmw7VsWlvIQysmkxjrgtv/Dno6YNu/gMMF6/5Sk/0I6RQI6j1O1rby5NZyXjpYhUOEz9xcwKZbCkmJi5Bkufcn8MqXIX85PPgMxKfaHZHyw6mLrfz95uO8dbKe8QkxfHb1VD6xfDLjYp3wuz+17mgu+hTc+V3tUL+OG02BoIle0ePx8ocTdfzPngu8dbKe+BgnH1uSx8Y1U5mUGiFT+Ha3wf9+FQ78Emash/t/Bu4IKDGp9zhwoYkfvHH63X+HH75pEp9Ylsfc4/9mja/PWwYf/TkkT7Q71LCjiV5dw+s1HKi4zObDNbxYWkXDlW6ykmP5o2WTeWjFZMYnhuFCIddTtR9e+Bw0nIbVX4Zb/gKcWpWMZEeqmvnFzvO8eLCKzh4vc7JT+FL2EW479Tc43AnwgX+ABR/VUk4/mugVAM3tPewsb2Tr6Xp+f+wida1dxDiFW2dN4KNLclkzPROXM4IGYrXWwhvfhtL/ssZe3/skTF1rd1QqgJrbe3j+QCUvllZTWnGZ6VLJvyX8hNmekzRPWk3iXX+HK0fvdAZN9GOSMYbKpg4OVFym9MJl9p2/xOGqZrwGEtxO1s7IZP28iayblRU59fc+Dadh9xNQ+t/g7YXlf2K15OPG2R2ZCqKKS+28fKiGt47XMKvqN3zZ+Qwp0kFJ3ArOzPg/TJq3mnk5qZH122gAaaKPYh6vobalk7P1bZQ3XOFM3RWO17ZyoqaFlk7rZpO4GAcLclJZUZjOqmkZLMpLxe2KoJY7WDfQHH8Jjv4Wzr0DzljrdvnVfw7phXZHp0KsuaOHXUfLiSn5D5ZcfIZk00aZdxLPedZwMHElCZPmMH1iMtMyk5iamcjk9ETGJ8RExqixERp1oheR9cAPACfwlDHmHwe8L7737wTagT82xuz359jBaKKHXo+X5o4emtp7aLzSRWNbN/WtXVxs6eRiSxc1zR1UXe6g+nIHPZ6rf4eJbiezslOYNTGZWdkp3JSXysyJycREUknG64HGMqg9DJV74dw2a0w1QMYMK8Ev/jQkhfEkaip0ulppL32O3pJfkFJv5Y1ayWKbZzb7PdM46J1KmckhJjaB3PHxZI+LY+I4a5uZHEtGUiwZSW7SEt2kJrhJiXNF5BfCqBK9iDiBU8D7sRYB3ws8aIw51m+fO4EvYCX6ZcAPjDHL/Dl2MHYmemMMXmO1lL3G4PEaPMbg9V597PEaej2GXq+h1+Olx2Po9VrbHo+XXo+h2+Ohq8dLt8dLV4+Xrl4PnT1eOns8dPR4aO/20NHt4Up3L+1dvVzp6qW10/pp6eihtWvwW79dDiErOZYJ4+LIHZ9ATmo8eWnxFGQkUpiZRFZybHj9IzUGPN3WeOjeTmt0TFcrdLVAR5M1bfCVOrhSC81V0HQWms6Dx7fmqCse8pfBlNUw807Imq0dcOr6LldA2etw+veYCzuRjksAGITL7myqHROp8qZxrmc8Fd2JXDIpXCaRVpPAFeJpN7F0OeKIjY0nLj6B5PhYEmOdJMXGkBTrJN7tIsHtJMHtJC6m78dBrMuJ2+Ug1uXA7XLgdjqIcTpwOQW3b+tyOHA55N3HTodYPyI4HLz72kh/275RovdnaMJSoMwYU+472TPABqB/st4A/NxY3xq7RCRVRLKBKX4cGzCr/+kPdHR7MAYMVtK2ttc+9hrwvvtav+chqGTFOIU4l5OEWCeJbhcJsU6SYl3kpSWQHOciJS6G8QluxsW7GJ/oJj0xlvQkNwVvPkJs0ykErA94yfdTfoOL+f2BBtnPmKuv9z0ebGu8VivceMF4rLq5pxe8PVaS90d8GqTkWItQzFgPWXNg4nyrBe8amzVXNQKpeVD8MBQ/jBgDTeeg+gDScJrxDScZ33SeuS1HobsWYm7wf8MA7dDb7sSDi16cvh8HHuPAg+DBgTFi5RDE93P18bWnFHqBnkHe69PqSKHor3eN8g/hWv4k+hygot/zSqxW+1D75Ph5LAAishHYCJCfn+9HWNd638wserzWH7EICOLbgoi85zWnQ6w/bsH6RvW97xDrW9Yh4PB921rPfVuHEOP7Ju77Zo7p27qsxzFO6xvd7ft2j3VZ3/ixMQ7iY5wjL6NkFYJrJK1ZP48ZtKUs/V7ve9xv63D6tg4QB4jTuhPV4bK2Trf1ExMPrjhwJ1kLcscmW8k9PhUSszSZq8ATgbQC62cgTy90XLLWDu5osmY37Wq1ljTs6bB+PN24ejtxeXuJ9fRaDRhfQ8Z4vXi9HjweD16vF4/X69sa6z0DXuPt14Ds//PeRmZfW8xrINEdnMXq/Un0g/3vH/hVeL19/DnWetGYJ4EnwSrd+BHXNf5mw7yRHBY5bvtruyNQKjo4XZCUZf2MgGB1Oob5tH7v8ifRVwJ5/Z7nAtV+7uP241illFJB5E8NYS8wXUQKRMQNPAC8NGCfl4CHxLIcaDbG1Ph5rFJKqSAaskVvjOkVkUeBLVi/qfzUGHNURDb53n8C2Iw14qYMa3jlp290bFA+iVJKqUHpDVNKKRUFbjS8MoLuolFKKTUSmuiVUirKaaJXSqkop4leKaWiXFh2xopIPXB+kLcygIYQhxNq+hmjg37G6BBJn3GyMWbQmf7CMtFfj4iUXK9XOVroZ4wO+hmjQ7R8Ri3dKKVUlNNEr5RSUS7SEv2TdgcQAvoZo4N+xugQFZ8xomr0Simlhi/SWvRKKaWGSRO9UkpFuYhM9CLyBRE5KSJHReSf7I4nWETkKyJiRCTD7lgCTUS+KyInROSQiLwgIql2xxQIIrLe92+zTES+bnc8gSYieSLypogc9/3/+6LdMQWLiDhF5ICIvGx3LKMVcYleRNZhrTu7wBgzF/iezSEFhYjkYS2qfsHuWILkdWCeMWYB1gLyf2FzPKMmIk7gMeAOYA7woIjMsTeqgOsFvmyMmQ0sBx6Jws/Y54vAcbuDCISIS/TAnwD/aIzpAjDG1NkcT7D8C/BVrrP0YqQzxrxmjOn1Pd2FtfpYpFsKlBljyo0x3cAzWI2SqGGMqTHG7Pc9bsVKhDn2RhV4IpIL3AU8ZXcsgRCJiX4GsFpEdovI2yKyxO6AAk1EPgRUGWMO2h1LiDwM/K/dQQRADlDR73klUZgE+4jIFOAmYLfNoQTDv2I1tLw2xxEQ/qwZG3Ii8ntg4iBv/SVWzOOxfm1cAjwrIlNNhI0THeIz/l/g9tBGFHg3+ozGmBd9+/wlVjngv0IZW5DIIK9F1L9Lf4lIEvAc8CVjTIvd8QSSiNwN1Blj9onILTaHExBhmeiNMbdd7z0R+RPgeV9i3yMiXqyJh+pDFV8gXO8zish8oAA4KCJglTT2i8hSY0xtCEMctRv9PQKIyKeAu4FbI+2L+joqgbx+z3OBaptiCRoRicFK8v9ljHne7niCYBXwIRG5E4gDUkTkl8aYT9gc14hF3A1TvrVqJxljviEiM4A3gPwoSRTXEJFzQLExJlJm0POLiKwHvg+sNcZE1Jf09YiIC6tj+VagCtgLfDya1kkWq/XxNHDJGPMlm8MJOl+L/ivGmLttDmVUIrFG/1Ngqogcwers+lS0Jvko90MgGXhdREpF5Am7AxotX+fyo8AWrE7KZ6MpyfusAj4JvM/391bqa/mqMBZxLXqllFLDE4kteqWUUsOgiV4ppaKcJnqllIpymuiVUirKaaJXSqkop4leKaWinCZ6pZSKcv8/jgEncPPjHNwAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "from scipy.stats import t\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "rv = t(df=10, loc=0, scale=1)\n", "x = np.linspace(rv.ppf(0.0001),5, 100)\n", "y = rv.pdf(x) \n", "\n", "rv2=t(df=10000,loc=0,scale=1)\n", "xx = np.linspace(rv2.ppf(0.0001), 5, 100)\n", "yy = rv2.pdf(x) \n", "#plt.xlim(-5,5)\n", "plt.plot(x,y)\n", "plt.plot(xx,yy)\n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: wooldridge in c:\\users\\v.campasano\\anaconda3\\lib\\site-packages (0.4.2)\n", "Requirement already satisfied: pandas in c:\\users\\v.campasano\\anaconda3\\lib\\site-packages (from wooldridge) (1.1.3)\n", "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\v.campasano\\anaconda3\\lib\\site-packages (from pandas->wooldridge) (2.8.1)\n", "Requirement already satisfied: pytz>=2017.2 in c:\\users\\v.campasano\\anaconda3\\lib\\site-packages (from pandas->wooldridge) (2020.1)\n", "Requirement already satisfied: numpy>=1.15.4 in c:\\users\\v.campasano\\anaconda3\\lib\\site-packages (from pandas->wooldridge) (1.19.2)\n", "Requirement already satisfied: six>=1.5 in c:\\users\\v.campasano\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7.3->pandas->wooldridge) (1.15.0)\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "pip install wooldridge" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "import wooldridge as woo" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "ceosal1=woo.dataWoo('ceosal1')\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "ceosal1\n", "x=ceosal1['roe']\n", "y=ceosal1['salary']" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[7.25649898e+01, 1.34253840e+03],\n", " [1.34253840e+03, 1.88333164e+06]])" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "covmat_xy=np.cov(x,y)\n", "covmat_xy" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1342.5383979609712" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cov_xy=covmat_xy[1,0]\n", "cov_xy" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "72.56498977473412" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "var_x=covmat_xy[0,0]\n", "var_x" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "18.501186345214922" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b1=cov_xy/var_x\n", "b1" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "963.1913364725579" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b0=np.mean(y)-b1*np.mean(x)\n", "b0" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "import statsmodels.formula.api as smf" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x1c67cdb27c0>" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reg=smf.ols(formula='salary ~ roe', data=ceosal1)\n", "results=reg.fit()\n", "results" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Intercept 963.191336\n", "roe 18.501186\n", "dtype: float64" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b=results.params\n", "b" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot('roe','salary', data=ceosal1,color='grey',marker='o',linestyle='')\n", "plt.plot(ceosal1['roe'], results.fittedvalues, color='black', linestyle='-')\n", "plt.ylabel('salary')\n", "plt.xlabel('roe')\n", "plt.savefig('Example-2-3-3.pdf')" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Intercept -0.904852\n", "educ 0.541359\n", "dtype: float64" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wage1=woo.dataWoo('wage1')\n", "reg=smf.ols(formula='wage~educ',data=wage1)\n", "results=reg.fit()\n", "b=results.params\n", "b" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Intercept 26.812214\n", "shareA 0.463827\n", "dtype: float64" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vote1=woo.dataWoo('vote1')\n", "reg=smf.ols(formula='voteA~shareA',data=vote1)\n", "results=reg.fit()\n", "b=results.params\n", "b\n", "\n" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'shareA')" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot('shareA','voteA',data=vote1,color='grey',marker='o',linestyle='')\n", "plt.plot(vote1['shareA'],results.fittedvalues, color='black',linestyle='-')\n", "plt.ylabel('voteA')\n", "plt.xlabel('shareA')" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "reg=smf.ols(formula='salary ~ roe', data=ceosal1)\n", "results=reg.fit()\n", "b=results.params\n" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "salary_hat=b[0]+b[1]*ceosal1['roe']\n", "u_hat=ceosal1['salary']-salary_hat" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "salary_hat2=results.fittedvalues\n", "u_hat2=results.resid" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>roe</th>\n", " <th>salary</th>\n", " <th>salary_hat</th>\n", " <th>u_hat</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>14.1</td>\n", " <td>1095</td>\n", " <td>1224.058071</td>\n", " <td>-129.058071</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>10.9</td>\n", " <td>1001</td>\n", " <td>1164.854261</td>\n", " <td>-163.854261</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>23.5</td>\n", " <td>1122</td>\n", " <td>1397.969216</td>\n", " <td>-275.969216</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>5.9</td>\n", " <td>578</td>\n", " <td>1072.348338</td>\n", " <td>-494.348338</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>13.8</td>\n", " <td>1368</td>\n", " <td>1218.507712</td>\n", " <td>149.492288</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>204</th>\n", " <td>9.0</td>\n", " <td>930</td>\n", " <td>1129.702014</td>\n", " <td>-199.702014</td>\n", " </tr>\n", " <tr>\n", " <th>205</th>\n", " <td>15.5</td>\n", " <td>525</td>\n", " <td>1249.959725</td>\n", " <td>-724.959725</td>\n", " </tr>\n", " <tr>\n", " <th>206</th>\n", " <td>12.1</td>\n", " <td>658</td>\n", " <td>1187.055698</td>\n", " <td>-529.055698</td>\n", " </tr>\n", " <tr>\n", " <th>207</th>\n", " <td>13.7</td>\n", " <td>555</td>\n", " <td>1216.657586</td>\n", " <td>-661.657586</td>\n", " </tr>\n", " <tr>\n", " <th>208</th>\n", " <td>14.4</td>\n", " <td>626</td>\n", " <td>1229.608413</td>\n", " <td>-603.608413</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>209 rows � 4 columns</p>\n", "</div>" ], "text/plain": [ " roe salary salary_hat u_hat\n", "0 14.1 1095 1224.058071 -129.058071\n", "1 10.9 1001 1164.854261 -163.854261\n", "2 23.5 1122 1397.969216 -275.969216\n", "3 5.9 578 1072.348338 -494.348338\n", "4 13.8 1368 1218.507712 149.492288\n", ".. ... ... ... ...\n", "204 9.0 930 1129.702014 -199.702014\n", "205 15.5 525 1249.959725 -724.959725\n", "206 12.1 658 1187.055698 -529.055698\n", "207 13.7 555 1216.657586 -661.657586\n", "208 14.4 626 1229.608413 -603.608413\n", "\n", "[209 rows x 4 columns]" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "table=pd.DataFrame({'roe':ceosal1['roe'],\n", " 'salary':ceosal1['salary'],\n", " 'salary_hat':salary_hat2,\n", " 'u_hat': u_hat2})\n", "table" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-2.6109895744677366e-13" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(u_hat2)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1.85849309e+06, -4.99097132e-14],\n", " [-4.99097132e-14, 7.25649898e+01]])" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "covmat=np.cov(u_hat2,ceosal1['roe'])\n", "covmat" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(salary_hat2)-np.mean(ceosal1['salary'])\n" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "24838.553076282253" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.var(salary_hat2,ddof=1)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1883331.6442767757" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.var(ceosal1['salary'],ddof=1)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.013188624081034111" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.var(salary_hat2,ddof=1)/np.var(ceosal1['salary'],ddof=1)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.013188624081034111" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "SST=np.var(ceosal1['salary'],ddof=1)\n", "SSE=np.var(salary_hat2 ,ddof=1)\n", "SSR=np.var(u_hat2,ddof=1)\n", "R2=SSE/SST\n", "R2\n" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "lnroe=np.log(ceosal1['roe'])\n", "lnsal=np.log(ceosal1['salary'])\n" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Intercept 6.846650\n", "sales 0.000015\n", "dtype: float64" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reg=smf.ols(formula='np.log(salary) ~ sales',data=ceosal1)\n", "results=reg.fit()\n", "b=results.params\n", "b" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Intercept 6.846650\n", "sales 0.000015\n", "dtype: float64" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "import numpy as np \n", "import wooldridge as woo\n", "import statsmodels.formula.api as smf\n", "meap93=woo.dataWoo('meap93')\n", "#Data set -- math performance score of schools on share of students elibible for fed. funded lunch\n" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "reg=smf.ols(formula='math10~lnchprg', data=meap93)\n", "results=reg.fit()" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "n=results.nobs" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "408.0" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9.565938459482764" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#SER=standard error of the regression\n", "u_hat_var=np.var(results.resid,ddof=1) # sample variance\n", "SER=np.sqrt(u_hat_var)*np.sqrt((n-1)/(n-2)) ## reverse the n-1 from calcing the variance, n-2 since 2 parameters\n", "SER" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "#SE of b0 and b1\n", "lunchprg_mean_sq=np.mean(meap93['lnchprg']**2) # Getting sum of x**2s\n", "lunchprg_var=np.var(meap93['lnchprg'],ddof=1) # Getting sample variance of the xs\n", "b1_se=SER/(np.sqrt(lunchprg_var)*np.sqrt(n-1))\n", "\n", "###b1_se=SER/sqrt(SSTx) . NOTE SSTx is variance of x without the n-1 adjustment\n", "\n", "b0_se=SER/(np.sqrt(lunchprg_var)*np.sqrt(n-1))*np.sqrt(lunchprg_mean_sq)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.034839334258369645" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b1_se" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9975823856755016" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b0_se" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"simpletable\">\n", "<caption>OLS Regression Results</caption>\n", "<tr>\n", " <th>Dep. Variable:</th> <td>math10</td> <th> R-squared: </th> <td> 0.171</td>\n", "</tr>\n", "<tr>\n", " <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.169</td>\n", "</tr>\n", "<tr>\n", " <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 83.77</td>\n", "</tr>\n", "<tr>\n", " <th>Date:</th> <td>Thu, 04 Feb 2021</td> <th> Prob (F-statistic):</th> <td>2.75e-18</td>\n", "</tr>\n", "<tr>\n", " <th>Time:</th> <td>11:21:43</td> <th> Log-Likelihood: </th> <td> -1499.3</td>\n", "</tr>\n", "<tr>\n", " <th>No. Observations:</th> <td> 408</td> <th> AIC: </th> <td> 3003.</td>\n", "</tr>\n", "<tr>\n", " <th>Df Residuals:</th> <td> 406</td> <th> BIC: </th> <td> 3011.</td>\n", "</tr>\n", "<tr>\n", " <th>Df Model:</th> <td> 1</td> <th> </th> <td> </td> \n", "</tr>\n", "<tr>\n", " <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n", "</tr>\n", "<tr>\n", " <th>Intercept</th> <td> 32.1427</td> <td> 0.998</td> <td> 32.221</td> <td> 0.000</td> <td> 30.182</td> <td> 34.104</td>\n", "</tr>\n", "<tr>\n", " <th>lnchprg</th> <td> -0.3189</td> <td> 0.035</td> <td> -9.152</td> <td> 0.000</td> <td> -0.387</td> <td> -0.250</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<tr>\n", " <th>Omnibus:</th> <td>61.162</td> <th> Durbin-Watson: </th> <td> 1.908</td>\n", "</tr>\n", "<tr>\n", " <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td> 105.062</td>\n", "</tr>\n", "<tr>\n", " <th>Skew:</th> <td> 0.886</td> <th> Prob(JB): </th> <td>1.53e-23</td>\n", "</tr>\n", "<tr>\n", " <th>Kurtosis:</th> <td> 4.743</td> <th> Cond. No. </th> <td> 60.4</td>\n", "</tr>\n", "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." ], "text/plain": [ "<class 'statsmodels.iolib.summary.Summary'>\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: math10 R-squared: 0.171\n", "Model: OLS Adj. R-squared: 0.169\n", "Method: Least Squares F-statistic: 83.77\n", "Date: Thu, 04 Feb 2021 Prob (F-statistic): 2.75e-18\n", "Time: 11:21:43 Log-Likelihood: -1499.3\n", "No. Observations: 408 AIC: 3003.\n", "Df Residuals: 406 BIC: 3011.\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "Intercept 32.1427 0.998 32.221 0.000 30.182 34.104\n", "lnchprg -0.3189 0.035 -9.152 0.000 -0.387 -0.250\n", "==============================================================================\n", "Omnibus: 61.162 Durbin-Watson: 1.908\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 105.062\n", "Skew: 0.886 Prob(JB): 1.53e-23\n", "Kurtosis: 4.743 Cond. No. 60.4\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results.summary()" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "a=woo.dataWoo('MEAP93')" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>lnchprg</th>\n", " <th>enroll</th>\n", " <th>staff</th>\n", " <th>expend</th>\n", " <th>salary</th>\n", " <th>benefits</th>\n", " <th>droprate</th>\n", " <th>gradrate</th>\n", " <th>math10</th>\n", " <th>sci11</th>\n", " <th>totcomp</th>\n", " <th>ltotcomp</th>\n", " <th>lexpend</th>\n", " <th>lenroll</th>\n", " <th>lstaff</th>\n", " <th>bensal</th>\n", " <th>lsalary</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1.400000</td>\n", " <td>1862</td>\n", " <td>112.599998</td>\n", " <td>5765</td>\n", " <td>37498.0</td>\n", " <td>7420</td>\n", " <td>2.900000</td>\n", " <td>89.199997</td>\n", " <td>56.400002</td>\n", " <td>67.900002</td>\n", " <td>44918.0</td>\n", " <td>10.712594</td>\n", " <td>8.659560</td>\n", " <td>7.529407</td>\n", " <td>4.723842</td>\n", " <td>0.197877</td>\n", " <td>10.532043</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2.300000</td>\n", " <td>11355</td>\n", " <td>101.199997</td>\n", " <td>6601</td>\n", " <td>48722.0</td>\n", " <td>10370</td>\n", " <td>1.300000</td>\n", " <td>91.400002</td>\n", " <td>42.700001</td>\n", " <td>65.300003</td>\n", " <td>59092.0</td>\n", " <td>10.986851</td>\n", " <td>8.794976</td>\n", " <td>9.337414</td>\n", " <td>4.617099</td>\n", " <td>0.212840</td>\n", " <td>10.793886</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2.700000</td>\n", " <td>7685</td>\n", " <td>114.000000</td>\n", " <td>6834</td>\n", " <td>44541.0</td>\n", " <td>7313</td>\n", " <td>3.500000</td>\n", " <td>91.400002</td>\n", " <td>43.799999</td>\n", " <td>54.299999</td>\n", " <td>51854.0</td>\n", " <td>10.856188</td>\n", " <td>8.829665</td>\n", " <td>8.947025</td>\n", " <td>4.736198</td>\n", " <td>0.164186</td>\n", " <td>10.704165</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3.400000</td>\n", " <td>1148</td>\n", " <td>85.400002</td>\n", " <td>3586</td>\n", " <td>31566.0</td>\n", " <td>5989</td>\n", " <td>3.600000</td>\n", " <td>86.599998</td>\n", " <td>25.299999</td>\n", " <td>60.000000</td>\n", " <td>37555.0</td>\n", " <td>10.533562</td>\n", " <td>8.184793</td>\n", " <td>7.045776</td>\n", " <td>4.447346</td>\n", " <td>0.189729</td>\n", " <td>10.359836</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>3.400000</td>\n", " <td>1572</td>\n", " <td>96.099998</td>\n", " <td>3847</td>\n", " <td>29781.0</td>\n", " <td>5545</td>\n", " <td>0.000000</td>\n", " <td>100.000000</td>\n", " <td>15.300000</td>\n", " <td>65.800003</td>\n", " <td>35326.0</td>\n", " <td>10.472375</td>\n", " <td>8.255049</td>\n", " <td>7.360104</td>\n", " <td>4.565389</td>\n", " <td>0.186193</td>\n", " <td>10.301626</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>403</th>\n", " <td>60.900002</td>\n", " <td>1689</td>\n", " <td>78.400002</td>\n", " <td>5380</td>\n", " <td>41856.0</td>\n", " <td>9468</td>\n", " <td>35.799999</td>\n", " <td>23.500000</td>\n", " <td>7.000000</td>\n", " <td>11.200000</td>\n", " <td>51324.0</td>\n", " <td>10.845914</td>\n", " <td>8.590444</td>\n", " <td>7.431892</td>\n", " <td>4.361824</td>\n", " <td>0.226204</td>\n", " <td>10.641991</td>\n", " </tr>\n", " <tr>\n", " <th>404</th>\n", " <td>61.099998</td>\n", " <td>2631</td>\n", " <td>100.699997</td>\n", " <td>5088</td>\n", " <td>35020.0</td>\n", " <td>7655</td>\n", " <td>17.500000</td>\n", " <td>61.099998</td>\n", " <td>8.100000</td>\n", " <td>22.700001</td>\n", " <td>42675.0</td>\n", " <td>10.661368</td>\n", " <td>8.534640</td>\n", " <td>7.875119</td>\n", " <td>4.612146</td>\n", " <td>0.218589</td>\n", " <td>10.463675</td>\n", " </tr>\n", " <tr>\n", " <th>405</th>\n", " <td>73.800003</td>\n", " <td>4024</td>\n", " <td>85.199997</td>\n", " <td>3942</td>\n", " <td>32678.0</td>\n", " <td>6220</td>\n", " <td>8.700000</td>\n", " <td>83.300003</td>\n", " <td>11.500000</td>\n", " <td>26.799999</td>\n", " <td>38898.0</td>\n", " <td>10.568698</td>\n", " <td>8.279444</td>\n", " <td>8.300032</td>\n", " <td>4.445002</td>\n", " <td>0.190342</td>\n", " <td>10.394458</td>\n", " </tr>\n", " <tr>\n", " <th>406</th>\n", " <td>74.400002</td>\n", " <td>850</td>\n", " <td>122.400002</td>\n", " <td>5029</td>\n", " <td>29210.0</td>\n", " <td>6513</td>\n", " <td>1.600000</td>\n", " <td>91.900002</td>\n", " <td>9.500000</td>\n", " <td>9.700000</td>\n", " <td>35723.0</td>\n", " <td>10.483550</td>\n", " <td>8.522977</td>\n", " <td>6.745236</td>\n", " <td>4.807294</td>\n", " <td>0.222972</td>\n", " <td>10.282267</td>\n", " </tr>\n", " <tr>\n", " <th>407</th>\n", " <td>79.500000</td>\n", " <td>7585</td>\n", " <td>93.300003</td>\n", " <td>4529</td>\n", " <td>33645.0</td>\n", " <td>7682</td>\n", " <td>22.000000</td>\n", " <td>40.599998</td>\n", " <td>2.700000</td>\n", " <td>7.200000</td>\n", " <td>41327.0</td>\n", " <td>10.629272</td>\n", " <td>8.418257</td>\n", " <td>8.933928</td>\n", " <td>4.535820</td>\n", " <td>0.228325</td>\n", " <td>10.423619</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>408 rows � 17 columns</p>\n", "</div>" ], "text/plain": [ " lnchprg enroll staff expend salary benefits droprate \\\n", "0 1.400000 1862 112.599998 5765 37498.0 7420 2.900000 \n", "1 2.300000 11355 101.199997 6601 48722.0 10370 1.300000 \n", "2 2.700000 7685 114.000000 6834 44541.0 7313 3.500000 \n", "3 3.400000 1148 85.400002 3586 31566.0 5989 3.600000 \n", "4 3.400000 1572 96.099998 3847 29781.0 5545 0.000000 \n", ".. ... ... ... ... ... ... ... \n", "403 60.900002 1689 78.400002 5380 41856.0 9468 35.799999 \n", "404 61.099998 2631 100.699997 5088 35020.0 7655 17.500000 \n", "405 73.800003 4024 85.199997 3942 32678.0 6220 8.700000 \n", "406 74.400002 850 122.400002 5029 29210.0 6513 1.600000 \n", "407 79.500000 7585 93.300003 4529 33645.0 7682 22.000000 \n", "\n", " gradrate math10 sci11 totcomp ltotcomp lexpend lenroll \\\n", "0 89.199997 56.400002 67.900002 44918.0 10.712594 8.659560 7.529407 \n", "1 91.400002 42.700001 65.300003 59092.0 10.986851 8.794976 9.337414 \n", "2 91.400002 43.799999 54.299999 51854.0 10.856188 8.829665 8.947025 \n", "3 86.599998 25.299999 60.000000 37555.0 10.533562 8.184793 7.045776 \n", "4 100.000000 15.300000 65.800003 35326.0 10.472375 8.255049 7.360104 \n", ".. ... ... ... ... ... ... ... \n", "403 23.500000 7.000000 11.200000 51324.0 10.845914 8.590444 7.431892 \n", "404 61.099998 8.100000 22.700001 42675.0 10.661368 8.534640 7.875119 \n", "405 83.300003 11.500000 26.799999 38898.0 10.568698 8.279444 8.300032 \n", "406 91.900002 9.500000 9.700000 35723.0 10.483550 8.522977 6.745236 \n", "407 40.599998 2.700000 7.200000 41327.0 10.629272 8.418257 8.933928 \n", "\n", " lstaff bensal lsalary \n", "0 4.723842 0.197877 10.532043 \n", "1 4.617099 0.212840 10.793886 \n", "2 4.736198 0.164186 10.704165 \n", "3 4.447346 0.189729 10.359836 \n", "4 4.565389 0.186193 10.301626 \n", ".. ... ... ... \n", "403 4.361824 0.226204 10.641991 \n", "404 4.612146 0.218589 10.463675 \n", "405 4.445002 0.190342 10.394458 \n", "406 4.807294 0.222972 10.282267 \n", "407 4.535820 0.228325 10.423619 \n", "\n", "[408 rows x 17 columns]" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }