From 88f8ccc8d09a5df089b92835b3c53800b30374b8 Mon Sep 17 00:00:00 2001 From: Alexandre Garanhao Date: Thu, 18 May 2023 15:15:04 +0100 Subject: [PATCH] lab done --- your-code/continuous.ipynb | 451 ++++++++++++++++++++++++++++++++++--- your-code/discrete.ipynb | 182 +++++++++++++-- 2 files changed, 583 insertions(+), 50 deletions(-) diff --git a/your-code/continuous.ipynb b/your-code/continuous.ipynb index 32218e9..4935c2b 100644 --- a/your-code/continuous.ipynb +++ b/your-code/continuous.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -12,6 +13,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -24,6 +26,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -35,9 +38,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np\n", + "from scipy.stats import expon" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2.86886157 2.91236738 2.59281256 2.95216648 2.67606563 2.40541115\n", + " 2.64380207 2.11462511 2.62913974 2.25794417]\n" + ] + } + ], "source": [ "from scipy.stats import uniform\n", "x = uniform.rvs(size=10)\n", @@ -48,6 +72,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -71,16 +96,67 @@ "![uniform distribution](ud.png)" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n", + "def random_numbers(a,b,c):\n", + " x = uniform.rvs(size=c)\n", + " randoms = a + (b-a)*x\n", + " return randoms" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# your code here\n" + "random_numbers(10,15,100)\n", + "random_numbers(10,60,1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([109., 100., 101., 90., 112., 109., 85., 90., 99., 105.]),\n", + " array([10.08085174, 15.062486 , 20.04412026, 25.02575453, 30.00738879,\n", + " 34.98902306, 39.97065732, 44.95229158, 49.93392585, 54.91556011,\n", + " 59.89719438]),\n", + " )" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 2, figsize=(8, 4))\n", + "axs[0].hist(random_numbers(10,15,100))\n", + "axs[1].hist(random_numbers(10,60,1000))\n" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -97,6 +173,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -114,14 +191,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "# your code here\n" + "# your code here\n", + "numbers = np.random.normal(loc=10, scale=1, size=1000)\n", + "numbers2 = np.random.normal(loc=10, scale=50, size=1000)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -130,14 +210,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 3., 14., 59., 134., 205., 281., 164., 97., 35., 8.]),\n", + " array([-161.5382397 , -129.52347583, -97.50871196, -65.49394808,\n", + " -33.47918421, -1.46442033, 30.55034354, 62.56510741,\n", + " 94.57987129, 126.59463516, 158.60939904]),\n", + " )" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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MakeModelYearEngine DisplacementCylindersTransmissionDrivetrainVehicle ClassFuel TypeFuel Barrels/YearCity MPGHighway MPGCombined MPGCO2 Emission Grams/MileFuel Cost/Year
0AM GeneralDJ Po Vehicle 2WD19842.54.0Automatic 3-spd2-Wheel DriveSpecial Purpose Vehicle 2WDRegular19.388824181717522.7647061950
1AM GeneralFJ8c Post Office19844.26.0Automatic 3-spd2-Wheel DriveSpecial Purpose Vehicle 2WDRegular25.354615131313683.6153852550
2AM GeneralPost Office DJ5 2WD19852.54.0Automatic 3-spdRear-Wheel DriveSpecial Purpose Vehicle 2WDRegular20.600625161716555.4375002100
3AM GeneralPost Office DJ8 2WD19854.26.0Automatic 3-spdRear-Wheel DriveSpecial Purpose Vehicle 2WDRegular25.354615131313683.6153852550
4ASC IncorporatedGNX19873.86.0Automatic 4-spdRear-Wheel DriveMidsize CarsPremium20.600625142116555.4375002550
\n", + "
" + ], + "text/plain": [ + " Make Model Year Engine Displacement \n", + "0 AM General DJ Po Vehicle 2WD 1984 2.5 \\\n", + "1 AM General FJ8c Post Office 1984 4.2 \n", + "2 AM General Post Office DJ5 2WD 1985 2.5 \n", + "3 AM General Post Office DJ8 2WD 1985 4.2 \n", + "4 ASC Incorporated GNX 1987 3.8 \n", + "\n", + " Cylinders Transmission Drivetrain Vehicle Class \n", + "0 4.0 Automatic 3-spd 2-Wheel Drive Special Purpose Vehicle 2WD \\\n", + "1 6.0 Automatic 3-spd 2-Wheel Drive Special Purpose Vehicle 2WD \n", + "2 4.0 Automatic 3-spd Rear-Wheel Drive Special Purpose Vehicle 2WD \n", + "3 6.0 Automatic 3-spd Rear-Wheel Drive Special Purpose Vehicle 2WD \n", + "4 6.0 Automatic 4-spd Rear-Wheel Drive Midsize Cars \n", + "\n", + " Fuel Type Fuel Barrels/Year City MPG Highway MPG Combined MPG \n", + "0 Regular 19.388824 18 17 17 \\\n", + "1 Regular 25.354615 13 13 13 \n", + "2 Regular 20.600625 16 17 16 \n", + "3 Regular 25.354615 13 13 13 \n", + "4 Premium 20.600625 14 21 16 \n", + "\n", + " CO2 Emission Grams/Mile Fuel Cost/Year \n", + "0 522.764706 1950 \n", + "1 683.615385 2550 \n", + "2 555.437500 2100 \n", + "3 683.615385 2550 \n", + "4 555.437500 2550 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vehicles = pd.read_csv(\"vehicles.csv\")\n", + "vehicles.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "# your code here\n", + "plt.hist(vehicles[\"Fuel Barrels/Year\"])\n", + "plt.show()" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -174,14 +476,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "# your code here\n", + "plt.hist(vehicles[\"CO2 Emission Grams/Mile\"])\n", + "plt.show()\n" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -190,14 +506,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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gFStWaO7cuXYfs2fPVmlpqZYvX66DBw9qyZIl2rNnj3JycoIwLQAAoKvr8E/Id+7cqbFjx16wf8aMGVqyZImSkpIuetw777yjH/7wh5L+thhgTk6ONm/erPDwcE2dOlUrV67UNddcY9fv3btX2dnZ2r17t/r27atHH31UCxcuDDjnxo0b9cQTT+jo0aO68cYbVVhYqIkTJ17yvfAT8q6Pn5ADwNXnUt+/v9E6OV0dIafrI+QAwNXnilknBwAAIBQIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASB0OORUVFZo8ebLcbrfCwsJUXFwcMG5ZlvLz89WvXz/FxMQoNTVVhw4dCqg5deqUMjMz5XA4FBsbq6ysLDU1NQXU7N27V6NHj1Z0dLQSExNVWFh4QS8bN27UoEGDFB0drSFDhuitt97q6O0AAABDdTjkNDc3a+jQoVq1atVFxwsLC7Vy5UoVFRWpqqpKPXv2VFpams6cOWPXZGZmqra2VmVlZSopKVFFRYVmzZplj/v9fo0fP14DBgxQdXW1li1bpiVLlmjNmjV2za5du3TvvfcqKytLH374oTIyMpSRkaH9+/d39JYAAICBwizLsi774LAwbdq0SRkZGZL+9hTH7XZr3rx5euyxxyRJPp9PCQkJWrt2raZPn64DBw4oOTlZu3fv1siRIyVJpaWlmjhxoj755BO53W6tXr1ajz/+uLxeryIjIyVJixYtUnFxsQ4ePChJmjZtmpqbm1VSUmL3M2rUKA0bNkxFRUWX1L/f75fT6ZTP55PD4bjcaUAIXb9oS6hbuCxHl6aHugUA6LIu9f07qN/JOXLkiLxer1JTU+19TqdTKSkpqqyslCRVVlYqNjbWDjiSlJqaqvDwcFVVVdk1Y8aMsQOOJKWlpamurk6nT5+2a758nfaa9utcTEtLi/x+f8AGAADMFNSQ4/V6JUkJCQkB+xMSEuwxr9er+Pj4gPGIiAjFxcUF1FzsHF++xlfVtI9fTEFBgZxOp70lJiZ29BYBAEAXcVX9uiovL08+n8/eTpw4EeqWAABAJwlqyHG5XJKk+vr6gP319fX2mMvlUkNDQ8D4uXPndOrUqYCai53jy9f4qpr28YuJioqSw+EI2AAAgJmCGnKSkpLkcrm0fft2e5/f71dVVZU8Ho8kyePxqLGxUdXV1XbNjh071NbWppSUFLumoqJCra2tdk1ZWZkGDhyo3r172zVfvk57Tft1AADA1a3DIaepqUk1NTWqqamR9LcvG9fU1Oj48eMKCwtTbm6unnnmGb355pvat2+fHnjgAbndbvsXWIMHD9aECRM0c+ZMvf/++3r33XeVk5Oj6dOny+12S5Luu+8+RUZGKisrS7W1tdqwYYNWrFihuXPn2n3Mnj1bpaWlWr58uQ4ePKglS5Zoz549ysnJ+eazAgAAuryIjh6wZ88ejR071n7dHjxmzJihtWvXasGCBWpubtasWbPU2NioO++8U6WlpYqOjraPWbdunXJycjRu3DiFh4dr6tSpWrlypT3udDq1bds2ZWdna8SIEerbt6/y8/MD1tL5/ve/r/Xr1+uJJ57Qv/7rv+rGG29UcXGxbr755suaCAAAYJZvtE5OV8c6OV0f6+QAwNUnJOvkAAAAXCkIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYKSIUDeAK8f1i7aEugUAAIKGJzkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMFPeScP39eixcvVlJSkmJiYvTd735Xv/jFL2RZll1jWZby8/PVr18/xcTEKDU1VYcOHQo4z6lTp5SZmSmHw6HY2FhlZWWpqakpoGbv3r0aPXq0oqOjlZiYqMLCwmDfDgAA6KKCHnKeffZZrV69Wi+99JIOHDigZ599VoWFhXrxxRftmsLCQq1cuVJFRUWqqqpSz549lZaWpjNnztg1mZmZqq2tVVlZmUpKSlRRUaFZs2bZ436/X+PHj9eAAQNUXV2tZcuWacmSJVqzZk2wbwkAAHRBYdaXH7EEwaRJk5SQkKBXXnnF3jd16lTFxMTot7/9rSzLktvt1rx58/TYY49Jknw+nxISErR27VpNnz5dBw4cUHJysnbv3q2RI0dKkkpLSzVx4kR98skncrvdWr16tR5//HF5vV5FRkZKkhYtWqTi4mIdPHjwknr1+/1yOp3y+XxyOBzBnIYuiRWPvz1Hl6aHugUA6LIu9f076E9yvv/972v79u36+OOPJUn//d//rT/96U+66667JElHjhyR1+tVamqqfYzT6VRKSooqKyslSZWVlYqNjbUDjiSlpqYqPDxcVVVVds2YMWPsgCNJaWlpqqur0+nTpy/aW0tLi/x+f8AGAADMFPR/u2rRokXy+/0aNGiQunXrpvPnz+uXv/ylMjMzJUler1eSlJCQEHBcQkKCPeb1ehUfHx/YaESE4uLiAmqSkpIuOEf7WO/evS/oraCgQE899VQQ7hIAAFzpgh5y3njjDa1bt07r16/XTTfdpJqaGuXm5srtdmvGjBnBvlyH5OXlae7cufZrv9+vxMTEEHaEq1VX/GiQj9gAdDVBDznz58/XokWLNH36dEnSkCFDdOzYMRUUFGjGjBlyuVySpPr6evXr188+rr6+XsOGDZMkuVwuNTQ0BJz33LlzOnXqlH28y+VSfX19QE376/aa/ysqKkpRUVHf/CYBAMAVL+jfyfnrX/+q8PDA03br1k1tbW2SpKSkJLlcLm3fvt0e9/v9qqqqksfjkSR5PB41NjaqurrartmxY4fa2tqUkpJi11RUVKi1tdWuKSsr08CBAy/6URUAALi6BD3kTJ48Wb/85S+1ZcsWHT16VJs2bdJzzz2nu+++W5IUFham3NxcPfPMM3rzzTe1b98+PfDAA3K73crIyJAkDR48WBMmTNDMmTP1/vvv691331VOTo6mT58ut9stSbrvvvsUGRmprKws1dbWasOGDVqxYkXAx1EAAODqFfSPq1588UUtXrxYjzzyiBoaGuR2u/Uv//Ivys/Pt2sWLFig5uZmzZo1S42NjbrzzjtVWlqq6Ohou2bdunXKycnRuHHjFB4erqlTp2rlypX2uNPp1LZt25Sdna0RI0aob9++ys/PD1hLBwAAXL2Cvk5OV8I6OYG64pdh8e3hi8cArhQhWycHAADgSkDIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABG6pSQ85e//EU/+clP1KdPH8XExGjIkCHas2ePPW5ZlvLz89WvXz/FxMQoNTVVhw4dCjjHqVOnlJmZKYfDodjYWGVlZampqSmgZu/evRo9erSio6OVmJiowsLCzrgdAADQBQU95Jw+fVp33HGHunfvrrffflsfffSRli9frt69e9s1hYWFWrlypYqKilRVVaWePXsqLS1NZ86csWsyMzNVW1ursrIylZSUqKKiQrNmzbLH/X6/xo8frwEDBqi6ulrLli3TkiVLtGbNmmDfEgAA6ILCLMuygnnCRYsW6d1339Uf//jHi45bliW326158+bpsccekyT5fD4lJCRo7dq1mj59ug4cOKDk5GTt3r1bI0eOlCSVlpZq4sSJ+uSTT+R2u7V69Wo9/vjj8nq9ioyMtK9dXFysgwcPXlKvfr9fTqdTPp9PDocjCHfftV2/aEuoW8AV7OjS9FC3AACSLv39O+hPct58802NHDlS//RP/6T4+HgNHz5cv/nNb+zxI0eOyOv1KjU11d7ndDqVkpKiyspKSVJlZaViY2PtgCNJqampCg8PV1VVlV0zZswYO+BIUlpamurq6nT69OmL9tbS0iK/3x+wAQAAMwU95PzP//yPVq9erRtvvFFbt27Vz372M/385z/Xa6+9Jknyer2SpISEhIDjEhIS7DGv16v4+PiA8YiICMXFxQXUXOwcX77G/1VQUCCn02lviYmJ3/BuAQDAlSroIaetrU233nqrfvWrX2n48OGaNWuWZs6cqaKiomBfqsPy8vLk8/ns7cSJE6FuCQAAdJKgh5x+/fopOTk5YN/gwYN1/PhxSZLL5ZIk1dfXB9TU19fbYy6XSw0NDQHj586d06lTpwJqLnaOL1/j/4qKipLD4QjYAACAmYIecu644w7V1dUF7Pv44481YMAASVJSUpJcLpe2b99uj/v9flVVVcnj8UiSPB6PGhsbVV1dbdfs2LFDbW1tSklJsWsqKirU2tpq15SVlWngwIEBv+QCAABXp6CHnDlz5ui9997Tr371Kx0+fFjr16/XmjVrlJ2dLUkKCwtTbm6unnnmGb355pvat2+fHnjgAbndbmVkZEj625OfCRMmaObMmXr//ff17rvvKicnR9OnT5fb7ZYk3XfffYqMjFRWVpZqa2u1YcMGrVixQnPnzg32LQEAgC4oItgnvO2227Rp0ybl5eXp6aefVlJSkl544QVlZmbaNQsWLFBzc7NmzZqlxsZG3XnnnSotLVV0dLRds27dOuXk5GjcuHEKDw/X1KlTtXLlSnvc6XRq27Ztys7O1ogRI9S3b1/l5+cHrKUDAACuXkFfJ6crYZ2cQKyTg6/DOjkArhQhWycHAADgSkDIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABG6vSQs3TpUoWFhSk3N9fed+bMGWVnZ6tPnz665pprNHXqVNXX1wccd/z4caWnp6tHjx6Kj4/X/Pnzde7cuYCanTt36tZbb1VUVJRuuOEGrV27trNvBwAAdBGdGnJ2796tX//617rlllsC9s+ZM0ebN2/Wxo0bVV5erpMnT2rKlCn2+Pnz55Wenq6zZ89q165deu2117R27Vrl5+fbNUeOHFF6errGjh2rmpoa5ebm6qGHHtLWrVs785YAAEAX0Wkhp6mpSZmZmfrNb36j3r172/t9Pp9eeeUVPffcc/rRj36kESNG6NVXX9WuXbv03nvvSZK2bdumjz76SL/97W81bNgw3XXXXfrFL36hVatW6ezZs5KkoqIiJSUlafny5Ro8eLBycnJ0zz336Pnnn++sWwIAAF1Ip4Wc7OxspaenKzU1NWB/dXW1WltbA/YPGjRI/fv3V2VlpSSpsrJSQ4YMUUJCgl2TlpYmv9+v2tpau+b/njstLc0+BwAAuLpFdMZJX3/9dX3wwQfavXv3BWNer1eRkZGKjY0N2J+QkCCv12vXfDngtI+3j31djd/v1xdffKGYmJgLrt3S0qKWlhb7td/v7/jNAQCALiHoT3JOnDih2bNna926dYqOjg726b+RgoICOZ1Oe0tMTAx1SwAAoJMEPeRUV1eroaFBt956qyIiIhQREaHy8nKtXLlSERERSkhI0NmzZ9XY2BhwXH19vVwulyTJ5XJd8Gur9td/r8bhcFz0KY4k5eXlyefz2duJEyeCccsAAOAKFPSQM27cOO3bt081NTX2NnLkSGVmZtr/3b17d23fvt0+pq6uTsePH5fH45EkeTwe7du3Tw0NDXZNWVmZHA6HkpOT7Zovn6O9pv0cFxMVFSWHwxGwAQAAMwX9Ozm9evXSzTffHLCvZ8+e6tOnj70/KytLc+fOVVxcnBwOhx599FF5PB6NGjVKkjR+/HglJyfr/vvvV2Fhobxer5544gllZ2crKipKkvTwww/rpZde0oIFC/Tggw9qx44deuONN7Rly5Zg3xIAAOiCOuWLx3/P888/r/DwcE2dOlUtLS1KS0vTv/3bv9nj3bp1U0lJiX72s5/J4/GoZ8+emjFjhp5++mm7JikpSVu2bNGcOXO0YsUKXXfddXr55ZeVlpYWilsCAABXmDDLsqxQNxEqfr9fTqdTPp+Pj64kXb+Ip2D4akeXpoe6BQCQdOnv3/zbVQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAIxFyAACAkQg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjBQR6gYAdA3XL9oS6hY67OjS9FC3ACCEeJIDAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIQQ85BQUFuu2229SrVy/Fx8crIyNDdXV1ATVnzpxRdna2+vTpo2uuuUZTp05VfX19QM3x48eVnp6uHj16KD4+XvPnz9e5c+cCanbu3Klbb71VUVFRuuGGG7R27dpg3w4AAOiigh5yysvLlZ2drffee09lZWVqbW3V+PHj1dzcbNfMmTNHmzdv1saNG1VeXq6TJ09qypQp9vj58+eVnp6us2fPateuXXrttde0du1a5efn2zVHjhxRenq6xo4dq5qaGuXm5uqhhx7S1q1bg31LAACgCwqzLMvqzAt89tlnio+PV3l5ucaMGSOfz6drr71W69ev1z333CNJOnjwoAYPHqzKykqNGjVKb7/9tiZNmqSTJ08qISFBklRUVKSFCxfqs88+U2RkpBYuXKgtW7Zo//799rWmT5+uxsZGlZaWXlJvfr9fTqdTPp9PDocj+DffxXTFdVCAr8M6OYCZLvX9u9O/k+Pz+SRJcXFxkqTq6mq1trYqNTXVrhk0aJD69++vyspKSVJlZaWGDBliBxxJSktLk9/vV21trV3z5XO017Sf42JaWlrk9/sDNgAAYKZODTltbW3Kzc3VHXfcoZtvvlmS5PV6FRkZqdjY2IDahIQEeb1eu+bLAad9vH3s62r8fr+++OKLi/ZTUFAgp9Npb4mJid/4HgEAwJWpU0NOdna29u/fr9dff70zL3PJ8vLy5PP57O3EiROhbgkAAHSSTvu3q3JyclRSUqKKigpdd9119n6Xy6WzZ8+qsbEx4GlOfX29XC6XXfP+++8HnK/911dfrvm/v8iqr6+Xw+FQTEzMRXuKiopSVFTUN743AABw5Qv6kxzLspSTk6NNmzZpx44dSkpKChgfMWKEunfvru3bt9v76urqdPz4cXk8HkmSx+PRvn371NDQYNeUlZXJ4XAoOTnZrvnyOdpr2s8BAACubkF/kpOdna3169frD3/4g3r16mV/h8bpdComJkZOp1NZWVmaO3eu4uLi5HA49Oijj8rj8WjUqFGSpPHjxys5OVn333+/CgsL5fV69cQTTyg7O9t+EvPwww/rpZde0oIFC/Tggw9qx44deuONN7RlC78QAgAAnfAkZ/Xq1fL5fPrhD3+ofv362duGDRvsmueff16TJk3S1KlTNWbMGLlcLv3+97+3x7t166aSkhJ169ZNHo9HP/nJT/TAAw/o6aeftmuSkpK0ZcsWlZWVaejQoVq+fLlefvllpaWlBfuWAABAF9Tp6+RcyVgnJxDr5MA0rJMDmOmKWScHAAAgFAg5AADASIQcAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACMRcgAAgJEIOQAAwEiEHAAAYCRCDgAAMBIhBwAAGImQAwAAjETIAQAARiLkAAAAI0WEugFTXb9oS6hbAK56XfH/w6NL00PdAmAMnuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIxEyAEAAEYi5AAAACN1+RWPV61apWXLlsnr9Wro0KF68cUXdfvtt4e6LQC4LKzSDARPl36Ss2HDBs2dO1dPPvmkPvjgAw0dOlRpaWlqaGgIdWsAACDEunTIee655zRz5kz99Kc/VXJysoqKitSjRw/9+7//e6hbAwAAIdZlP646e/asqqurlZeXZ+8LDw9XamqqKisrL3pMS0uLWlpa7Nc+n0+S5Pf7g95fW8tfg35OALgS9Z+zMdQtdNj+p9JC3QK+gfb3bcuyvrauy4ac//3f/9X58+eVkJAQsD8hIUEHDx686DEFBQV66qmnLtifmJjYKT0CAK5MzhdC3QGC4fPPP5fT6fzK8S4bci5HXl6e5s6da79ua2vTqVOn1KdPH4WFhYWws2/G7/crMTFRJ06ckMPhCHU7xmO+v13M97eL+f52Md+Xx7Isff7553K73V9b12VDTt++fdWtWzfV19cH7K+vr5fL5broMVFRUYqKigrYFxsb21ktfuscDgf/k3yLmO9vF/P97WK+v13Md8d93ROcdl32i8eRkZEaMWKEtm/fbu9ra2vT9u3b5fF4QtgZAAC4EnTZJzmSNHfuXM2YMUMjR47U7bffrhdeeEHNzc366U9/GurWAABAiHXpkDNt2jR99tlnys/Pl9fr1bBhw1RaWnrBl5FNFxUVpSeffPKCj+LQOZjvbxfz/e1ivr9dzHfnCrP+3u+vAAAAuqAu+50cAACAr0PIAQAARiLkAAAAIxFyAACAkQg5XUhFRYUmT54st9utsLAwFRcXB4xblqX8/Hz169dPMTExSk1N1aFDh0LTbBdXUFCg2267Tb169VJ8fLwyMjJUV1cXUHPmzBllZ2erT58+uuaaazR16tQLFqfEpVm9erVuueUWe0E0j8ejt99+2x5nrjvX0qVLFRYWptzcXHsfcx48S5YsUVhYWMA2aNAge5y57jyEnC6kublZQ4cO1apVqy46XlhYqJUrV6qoqEhVVVXq2bOn0tLSdObMmW+5066vvLxc2dnZeu+991RWVqbW1laNHz9ezc3Nds2cOXO0efNmbdy4UeXl5Tp58qSmTJkSwq67ruuuu05Lly5VdXW19uzZox/96Ef68Y9/rNraWknMdWfavXu3fv3rX+uWW24J2M+cB9dNN92kTz/91N7+9Kc/2WPMdSey0CVJsjZt2mS/bmtrs1wul7Vs2TJ7X2NjoxUVFWX953/+Zwg6NEtDQ4MlySovL7cs629z2717d2vjxo12zYEDByxJVmVlZajaNErv3r2tl19+mbnuRJ9//rl14403WmVlZdYPfvADa/bs2ZZl8fcdbE8++aQ1dOjQi44x152LJzmGOHLkiLxer1JTU+19TqdTKSkpqqysDGFnZvD5fJKkuLg4SVJ1dbVaW1sD5nvQoEHq378/8/0NnT9/Xq+//rqam5vl8XiY606UnZ2t9PT0gLmV+PvuDIcOHZLb7dZ3vvMdZWZm6vjx45KY687WpVc8xv/n9Xol6YLVnhMSEuwxXJ62tjbl5ubqjjvu0M033yzpb/MdGRl5wT/wynxfvn379snj8ejMmTO65pprtGnTJiUnJ6umpoa57gSvv/66PvjgA+3evfuCMf6+gyslJUVr167VwIED9emnn+qpp57S6NGjtX//fua6kxFygL8jOztb+/fvD/gMHcE3cOBA1dTUyOfz6Xe/+51mzJih8vLyULdlpBMnTmj27NkqKytTdHR0qNsx3l133WX/9y233KKUlBQNGDBAb7zxhmJiYkLYmfn4uMoQLpdLki74Rn59fb09ho7LyclRSUmJ3nnnHV133XX2fpfLpbNnz6qxsTGgnvm+fJGRkbrhhhs0YsQIFRQUaOjQoVqxYgVz3Qmqq6vV0NCgW2+9VREREYqIiFB5eblWrlypiIgIJSQkMOedKDY2Vt/73vd0+PBh/r47GSHHEElJSXK5XNq+fbu9z+/3q6qqSh6PJ4SddU2WZSknJ0ebNm3Sjh07lJSUFDA+YsQIde/ePWC+6+rqdPz4ceY7SNra2tTS0sJcd4Jx48Zp3759qqmpsbeRI0cqMzPT/m/mvPM0NTXpz3/+s/r168ffdyfj46oupKmpSYcPH7ZfHzlyRDU1NYqLi1P//v2Vm5urZ555RjfeeKOSkpK0ePFiud1uZWRkhK7pLio7O1vr16/XH/7wB/Xq1cv+bNzpdComJkZOp1NZWVmaO3eu4uLi5HA49Oijj8rj8WjUqFEh7r7rycvL01133aX+/fvr888/1/r167Vz505t3bqVue4EvXr1sr9f1q5nz57q06ePvZ85D57HHntMkydP1oABA3Ty5Ek9+eST6tatm+69917+vjtbqH/ehUv3zjvvWJIu2GbMmGFZ1t9+Rr548WIrISHBioqKssaNG2fV1dWFtuku6mLzLMl69dVX7ZovvvjCeuSRR6zevXtbPXr0sO6++27r008/DV3TXdiDDz5oDRgwwIqMjLSuvfZaa9y4cda2bdvscea68335J+SWxZwH07Rp06x+/fpZkZGR1j/8wz9Y06ZNsw4fPmyPM9edJ8yyLCtE+QoAAKDT8J0cAABgJEIOAAAwEiEHAAAYiZADAACMRMgBAABGIuQAAAAjEXIAAICRCDkAAMBIhBwAAGAkQg4AADASIQcAABiJkAMAAIz0/wDvwUCuc2l0WQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "# your code here\n", + "plt.hist(vehicles[\"Combined MPG\"])\n", + "plt.show()" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -210,10 +540,12 @@ "metadata": {}, "outputs": [], "source": [ - "# you answer here:\n" + "# you answer here:\n", + "# plot 1 seems to be the normally distributed, the peak seems to be at the middle and the other plots are more skewed" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -221,6 +553,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -237,14 +570,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ - "# your code here\n" + "# your code here\n", + "x = np.random.exponential(scale=10, size= 100)\n", + "z =np.random.exponential(scale=1, size= 1000)\n", + " = np.random.exponential(scale=1, size= 1000)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -253,14 +590,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(array([547., 262., 103., 45., 23., 11., 2., 5., 1., 1.]),\n", + " array([2.91597296e-04, 7.90264170e-01, 1.58023674e+00, 2.37020932e+00,\n", + " 3.16018189e+00, 3.95015446e+00, 4.74012703e+00, 5.53009961e+00,\n", + " 6.32007218e+00, 7.11004475e+00, 7.90001733e+00]),\n", + " )" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your answer here:\n" + "# your answer here:\n", + "fig, axs = plt.subplots(1, 2, figsize=(10, 4))\n", + "axs[0].hist(z)\n", + "axs[1].hist(y)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -273,15 +639,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7534030360583934\n" + ] + } + ], "source": [ "# your answer here\n", - "# Hint: This is same as saying P(x<15)\n" + "# Hint: This is same as saying P(x<15)\n", + "lambda_inv = 10/1\n", + "\n", + "exp_dist = expon(scale = lambda_inv)\n", + "\n", + "x = exp_dist.cdf(14)\n", + "print(x)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -290,11 +671,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.2231301601484298" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your answer here\n" + "# your answer here\n", + "1-exp_dist.cdf(15)" ] } ], @@ -314,7 +707,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.11.2" } }, "nbformat": 4, diff --git a/your-code/discrete.ipynb b/your-code/discrete.ipynb index 310a3c6..f034b45 100644 --- a/your-code/discrete.ipynb +++ b/your-code/discrete.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -18,6 +19,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -33,9 +35,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 63, "metadata": {}, "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import math\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from scipy.stats import bernoulli \n", + "from scipy.stats import binom \n", + "from scipy.stats import poisson " + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability apple: 0.6\n", + "Probability orange: 0.4\n" + ] + } + ], "source": [ "\"\"\"\n", "Calculate:\n", @@ -43,10 +70,19 @@ "q = probability that the fruit is an orange\n", "\"\"\"\n", "\n", - "# your code here\n" + "# your code here\n", + "n_apples = 60\n", + "n_oranges = 40\n", + "n_total = 100\n", + "p_apple = n_apples / n_total\n", + "p_orange = n_oranges / n_total\n", + "\n", + "print(\"Probability apple:\", p_apple)\n", + "print(\"Probability orange:\", p_orange)\n" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -61,14 +97,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.07775999999999998\n" + ] + }, + { + "data": { + "text/plain": [ + "8.349416423424006e-08" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here\n" + "# your code here\n", + "probability_all_apples = p_apple ** 5\n", + "probability_apples_oranges = p_apple ** 5 * p_orange ** 15\n", + "\n", + "print(probability_all_apples)\n", + "probability_apples_oranges" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -83,14 +143,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0012944935222876579\n" + ] + } + ], "source": [ - "# your code here\n" + "# your code here\n", + "p = 0.6\n", + "n = 20\n", + "\n", + "binomial_dist = binom(n,p)\n", + "\n", + "print(binomial_dist.pmf(5))" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -101,14 +176,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.00031703112116863037\n" + ] + } + ], "source": [ - "# your code here\n" + "# your code here\n", + "print(binomial_dist.cdf(4))" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -119,15 +204,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'PDF')" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# your code here\n", - "# Please label the axes and give a title to the plot\n" + "# Please label the axes and give a title to the plot\n", + "x = np.arange(0,20)\n", + "plt.plot(x, binomial_dist.pmf(x),\"o\")\n", + "plt.ylabel(\"probability\")\n", + "plt.xlabel(\"Apples\")\n", + "plt.title(\"PDF\")" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -151,14 +263,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 64, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.053775025581946814\n" + ] + } + ], "source": [ - "# your code here\n" + "# your code here\n", + "mu = 2.3\n", + "\n", + "poisson_dist = poisson(mu)\n", + "\n", + "print(poisson_dist.pmf(5))" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -167,12 +293,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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uVigUij9OnDgxwxMDAICZdEsyiwsLC2Wz2TQwMJCwfWBg4Kon1CbrM5/5jP7gD/5AH3300YTfz83Nver5MAAAILMkdYQlJydHS5YsUSAQiG+LRqMKBAKqra2dtqEuXLig3/72t3K5XNO2TwAAkL6SOsIiSV6vV+vWrVNVVZWqq6u1c+dODQ8Pa/369ZKktWvX6vbbb1dra6ukyyfq9vb2xv/95MmTOnbsmG677TbdddddkqSnn35aDQ0N+tznPqdTp07J5/PJZrNp9erV0/U6AQBAGks6WFauXKnTp0+rpaVFwWBQlZWV8vv98RNxjx8/Lqv1yoGbU6dO6d57741/vWPHDu3YsUMPPfSQOjo6JEkff/yxVq9erbNnz+qzn/2sli1bpiNHjuizn/3sDb48AACQCSyxWCyW6iFuVDgclsPhUCgUUn5+fqrHAQAA1yGZ399GXCUEAABwNQQLAAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAOMRLAAAwHgECwAAMB7BAgAAjEewAAAA4xEsAADAeAQLAAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAOMRLAAAwHgECwAAMB7BAgAAjEewAAAA4xEsAADAeAQLAAAwHsECAACMR7AAAADjESwAAMB4BAsAADDeLakeAEB6ikRj6uo7p8GhERXl2VVdViCb1ZLqsQBkKIIFQNL8Pf3afKhX/aGR+DaXwy5fQ7nqK1wpnAxApuIjIQBJ8ff0q3Ffd0KsSFIwNKLGfd3y9/SnaDIAmYxgAXDdItGYNh/qVWyC741t23yoV5HoRCsAYOoIFgDXravv3LgjK58Wk9QfGlFX37mZGwpAViBYAFy3waHJY2Uq6wDgehEsAK5bUZ59WtcBwPUiWABct+qyArkcdk128bJFl68Wqi4rmMmxAGQBggXAdbNZLfI1lEvSuGgZ+9rXUM79WABMO4IFQFLqK1xqX7NYTkfixz5Oh13taxZzHxYANwU3jgOQtPoKlx4pd3KnWwAzhmABMCU2q0W18+akegwAWYKPhAAAgPEIFgAAYDyCBQAAGI9gAQAAxiNYAACA8QgWAABgPIIFAAAYj2ABAADGI1gAAIDxuNPtVUSiMW49DgCAAaZ0hGXXrl0qLS2V3W5XTU2Nurq6Jl37q1/9So8//rhKS0tlsVi0c+fOG97nTPD39GvZtre0es8RbTxwTKv3HNGybW/J39Of0rkAAMhGSQfLwYMH5fV65fP51N3drUWLFqmurk6Dg4MTrr948aLmzp2rrVu3yul0Tss+bzZ/T78a93WrPzSSsD0YGlHjvm6iBQCAGWaJxWKxZJ5QU1OjpUuXqq2tTZIUjUbldru1YcMGNTU1XfW5paWl2rRpkzZt2jRt+5SkcDgsh8OhUCik/Pz8ZF7OOJFoTMu2vTUuVsZYJDkddh1+9gt8PAQAwA1I5vd3UkdYRkdHdfToUXk8nis7sFrl8XjU2dk5pWGnss9Lly4pHA4nPKZLV9+5SWNFkmKS+kMj6uo7N20/EwAAXF1SwXLmzBlFIhEVFxcnbC8uLlYwGJzSAFPZZ2trqxwOR/zhdrun9LMnMjg0eaxMZR0AALhxaXlZc3Nzs0KhUPxx4sSJadt3UZ59WtcBAIAbl9RlzYWFhbLZbBoYGEjYPjAwMOkJtTdjn7m5ucrNzZ3Sz7uW6rICuRx2BUMjmujknrFzWKrLCm7KzwcAAOMldYQlJydHS5YsUSAQiG+LRqMKBAKqra2d0gA3Y583wma1yNdQLulynHza2Ne+hnJOuAUAYAYl/ZGQ1+vVnj17tHfvXn3wwQdqbGzU8PCw1q9fL0lau3atmpub4+tHR0d17NgxHTt2TKOjozp58qSOHTumjz766Lr3OdPqK1xqX7NYTkfixz5Oh13taxarvsKVkrkAAMhWSd/pduXKlTp9+rRaWloUDAZVWVkpv98fP2n2+PHjslqvdNCpU6d07733xr/esWOHduzYoYceekgdHR3Xtc9UqK9w6ZFyJ3e6BQDAAEnfh8VE03kfFgAAMDNu2n1YAAAAUoFgAQAAxiNYAACA8QgWAABgPIIFAAAYj2ABAADGI1gAAIDxCBYAAGA8ggUAABiPYAEAAMYjWAAAgPEIFgAAYDyCBQAAGI9gAQAAxiNYAACA8QgWAABgPIIFAAAYj2ABAADGI1gAAIDxCBYAAGA8ggUAABiPYAEAAMYjWAAAgPEIFgAAYDyCBQAAGI9gAQAAxiNYAACA8QgWAABgPIIFAAAYj2ABAADGI1gAAIDxCBYAAGA8ggUAABiPYAEAAMYjWAAAgPEIFgAAYDyCBQAAGI9gAQAAxiNYAACA8QgWAABgPIIFAAAYj2ABAADGI1gAAIDxCBYAAGA8ggUAABiPYAEAAMYjWAAAgPFuSfUAAJBqkWhMXX3nNDg0oqI8u6rLCmSzWlI9FoBPIVgAZDV/T782H+pVf2gkvs3lsMvXUK76ClcKJwPwaXwkBCBr+Xv61bivOyFWJCkYGlHjvm75e/pTNBmA/49gAZCVItGYNh/qVWyC741t23yoV5HoRCsAzDSCBUBW6uo7N+7IyqfFJPWHRtTVd27mhgIwqSkFy65du1RaWiq73a6amhp1dXVddf1PfvITzZ8/X3a7XQsWLNAbb7yR8P2vfOUrslgsCY/6+vqpjAYA12VwaPJYmco6ADdX0sFy8OBBeb1e+Xw+dXd3a9GiRaqrq9Pg4OCE63/xi19o9erV+upXv6r3339fK1as0IoVK9TT05Owrr6+Xv39/fHHq6++OrVXBADXoSjPPq3rANxcllgsltQHtDU1NVq6dKna2tokSdFoVG63Wxs2bFBTU9O49StXrtTw8LBef/31+Lb77rtPlZWV2r17t6TLR1jOnz+v1157bUovIhwOy+FwKBQKKT8/f0r7AJBdItGYlm17S8HQyITnsVgkOR12HX72C1ziDNwkyfz+TuoIy+joqI4ePSqPx3NlB1arPB6POjs7J3xOZ2dnwnpJqqurG7e+o6NDRUVF+vznP6/GxkadPXs2mdEAICk2q0W+hnJJl+Pk08a+9jWUEyuAIZIKljNnzigSiai4uDhhe3FxsYLB4ITPCQaD11xfX1+vH/3oRwoEAtq2bZvefvttPfroo4pEIhPu89KlSwqHwwkPAEhWfYVL7WsWy+lI/NjH6bCrfc1i7sMCGMSIG8etWrUq/u8LFizQwoULNW/ePHV0dOjhhx8et761tVWbN2+eyREBZKj6CpceKXdyp1vAcEkdYSksLJTNZtPAwEDC9oGBATmdzgmf43Q6k1ovSXPnzlVhYaE++uijCb/f3NysUCgUf5w4cSKZlwEACWxWi2rnzdFjlberdt4cYgUwUFLBkpOToyVLligQCMS3RaNRBQIB1dbWTvic2trahPWS9Oabb066XpI+/vhjnT17Vi7XxIdjc3NzlZ+fn/AAAACZK+nLmr1er/bs2aO9e/fqgw8+UGNjo4aHh7V+/XpJ0tq1a9Xc3Bxfv3HjRvn9fr300kv69a9/rRdeeEHvvfeennrqKUnShQsX9Mwzz+jIkSP63e9+p0AgoMcee0x33XWX6urqpullAgCAdJb0OSwrV67U6dOn1dLSomAwqMrKSvn9/viJtcePH5fVeqWD7r//fu3fv1/f+ta39Nxzz+nuu+/Wa6+9poqKCkmSzWbTL3/5S+3du1fnz59XSUmJli9fri1btig3N3eaXiYAAEhnSd+HxUTchwUAgPRz0+7DAgAAkAoECwAAMB7BAgAAjEewAAAA4xEsAADAeAQLAAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAOMRLAAAwHgECwAAMB7BAgAAjEewAAAA4xEsAADAeAQLAAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIxHsAAAAOMRLAAAwHgECwAAMN4tqR4AADA9ItGYuvrOaXBoREV5dlWXFchmtaR6LGBaECwAkAH8Pf3afKhX/aGR+DaXwy5fQ7nqK1wpnAyYHnwkBABpzt/Tr8Z93QmxIknB0Iga93XL39OfosmA6UOwAEAai0Rj2nyoV7EJvje2bfOhXkWiE60A0gfBAgBprKvv3LgjK58Wk9QfGlFX37mZGwq4CQgWAEhjg0OTx8pU1gGmIlgAII0V5dmndR1gKoIFANJYdVmBXA67Jrt42aLLVwtVlxXM5FjAtCNYACCN2awW+RrKJWlctIx97Wso534sSHsECwCkufoKl9rXLJbTkfixj9NhV/uaxdyHBRmBG8cBQAaor3DpkXInd7pFxiJYACBD2KwW1c6bk+oxgJuCj4QAAIDxCBYAAGA8ggUAABiPYAEAAMYjWAAAgPEIFgAAYDwuawYAGCUSjXE/GYxDsAAAjOHv6dfmQ73qD13569Iuh12+hnLu2Jvl+EgIAGAEf0+/Gvd1J8SKJAVDI2rc1y1/T3+KJoMJCBYAQMpFojFtPtSr2ATfG9u2+VCvItGJViAbECwAgJTr6js37sjKp8Uk9YdG1NV3buaGglEIFgBAyg0OTR4rU1mHzMNJtwCAlCvKs0/rOhNwtdP0IlgAAClXXVYgl8OuYGhkwvNYLJKcjsu/9NNBJl3tZEp4ESwAgJSzWS3yNZSrcV+3LFJCtIz9avQ1lKfFEYqxq53+f3iNXe3UvmZx2kSLSeE1pXNYdu3apdLSUtntdtXU1Kirq+uq63/yk59o/vz5stvtWrBggd54442E78diMbW0tMjlcmnWrFnyeDz68MMPpzIaACBN1Ve41L5msZyOxI99nA572vySz6SrnUy7zDzpYDl48KC8Xq98Pp+6u7u1aNEi1dXVaXBwcML1v/jFL7R69Wp99atf1fvvv68VK1ZoxYoV6unpia/Zvn27vve972n37t165513dOutt6qurk4jI5xcBQDZpL7CpcPPfkGvfu0+/dWqSr36tft0+NkvpEWsSJlztZOJ4WWJxWJJ/bSamhotXbpUbW1tkqRoNCq3260NGzaoqalp3PqVK1dqeHhYr7/+enzbfffdp8rKSu3evVuxWEwlJSX6xje+oaefflqSFAqFVFxcrFdeeUWrVq265kzhcFgOh0OhUEj5+fnJvBwAAKbNT4+d1MYDx6657q9WVeqxyttv/kBT1Pnbs1q958g11736tftUO2/OlH9OMr+/kzrCMjo6qqNHj8rj8VzZgdUqj8ejzs7OCZ/T2dmZsF6S6urq4uv7+voUDAYT1jgcDtXU1Ey6z0uXLikcDic8AABItUy52snEy8yTCpYzZ84oEomouLg4YXtxcbGCweCEzwkGg1ddP/bPZPbZ2toqh8MRf7jd7mReBgAAN8XY1U6TnRps0eWTVk2/2snE8ErLG8c1NzcrFArFHydOnEj1SAAAxK92kjQuWtLpaicTwyupYCksLJTNZtPAwEDC9oGBATmdzgmf43Q6r7p+7J/J7DM3N1f5+fkJDwAATJAJVzuZGF5JBUtOTo6WLFmiQCAQ3xaNRhUIBFRbWzvhc2praxPWS9Kbb74ZX19WVian05mwJhwO65133pl0nwAAmCzdr3aSzAuvpG8c5/V6tW7dOlVVVam6ulo7d+7U8PCw1q9fL0lau3atbr/9drW2tkqSNm7cqIceekgvvfSSvvjFL+rAgQN677339PLLL0uSLBaLNm3apBdffFF33323ysrK9Pzzz6ukpEQrVqyYvlcKAMAMslktN3QFjQnqK1x6pNyZnne6XblypU6fPq2WlhYFg0FVVlbK7/fHT5o9fvy4rNYrB27uv/9+7d+/X9/61rf03HPP6e6779Zrr72mioqK+JpvfvObGh4e1te//nWdP39ey5Ytk9/vl91u9lnUAABkOlPCK+n7sJiI+7AAAJB+btp9WAAAAFKBYAEAAMYjWAAAgPEIFgAAYDyCBQAAGI9gAQAAxiNYAACA8QgWAABgvKTvdGuisXvfhcPhFE8CAACu19jv7eu5h21GBMvQ0JAkye12p3gSAACQrKGhITkcjquuyYhb80ejUZ06dUp5eXmyWKb3DzKFw2G53W6dOHGC2/4bgPfDLLwf5uE9MQvvx9XFYjENDQ2ppKQk4e8QTiQjjrBYrVbdcccdN/Vn5Ofn8x82g/B+mIX3wzy8J2bh/ZjctY6sjOGkWwAAYDyCBQAAGI9guYbc3Fz5fD7l5uamehSI98M0vB/m4T0xC+/H9MmIk24BAEBm4wgLAAAwHsECAACMR7AAAADjESwAAMB4BMs17Nq1S6WlpbLb7aqpqVFXV1eqR8pKra2tWrp0qfLy8lRUVKQVK1boP//zP1M9Fv7P1q1bZbFYtGnTplSPkrVOnjypNWvWaM6cOZo1a5YWLFig9957L9VjZaVIJKLnn39eZWVlmjVrlubNm6ctW7Zc19/LweQIlqs4ePCgvF6vfD6furu7tWjRItXV1WlwcDDVo2Wdt99+W08++aSOHDmiN998U5988omWL1+u4eHhVI+W9d599139zd/8jRYuXJjqUbLW//zP/+iBBx7Q7/3e7+mf/umf1Nvbq5deekmzZ89O9WhZadu2bWpvb1dbW5s++OADbdu2Tdu3b9f3v//9VI+W1ris+Spqamq0dOlStbW1Sbr8N4vcbrc2bNigpqamFE+X3U6fPq2ioiK9/fbb+qM/+qNUj5O1Lly4oMWLF+uv//qv9eKLL6qyslI7d+5M9VhZp6mpSf/2b/+mf/3Xf031KJD0x3/8xyouLtYPfvCD+LbHH39cs2bN0r59+1I4WXrjCMskRkdHdfToUXk8nvg2q9Uqj8ejzs7OFE4GSQqFQpKkgoKCFE+S3Z588kl98YtfTPjvCWbeP/7jP6qqqkpf/vKXVVRUpHvvvVd79uxJ9VhZ6/7771cgENBvfvMbSdK///u/6/Dhw3r00UdTPFl6y4g/fngznDlzRpFIRMXFxQnbi4uL9etf/zpFU0G6fKRr06ZNeuCBB1RRUZHqcbLWgQMH1N3drXfffTfVo2S9//qv/1J7e7u8Xq+ee+45vfvuu/qzP/sz5eTkaN26dakeL+s0NTUpHA5r/vz5stlsikQi+va3v60nnngi1aOlNYIFaefJJ59UT0+PDh8+nOpRstaJEye0ceNGvfnmm7Lb7akeJ+tFo1FVVVXpO9/5jiTp3nvvVU9Pj3bv3k2wpMCPf/xj/d3f/Z3279+ve+65R8eOHdOmTZtUUlLC+3EDCJZJFBYWymazaWBgIGH7wMCAnE5niqbCU089pddff10///nPdccdd6R6nKx19OhRDQ4OavHixfFtkUhEP//5z9XW1qZLly7JZrOlcMLs4nK5VF5enrDtD//wD/X3f//3KZoouz3zzDNqamrSqlWrJEkLFizQf//3f6u1tZVguQGcwzKJnJwcLVmyRIFAIL4tGo0qEAiotrY2hZNlp1gspqeeekr/8A//oLfeektlZWWpHimrPfzww/qP//gPHTt2LP6oqqrSE088oWPHjhErM+yBBx4Yd5n/b37zG33uc59L0UTZ7eLFi7JaE3+92mw2RaPRFE2UGTjCchVer1fr1q1TVVWVqqurtXPnTg0PD2v9+vWpHi3rPPnkk9q/f79++tOfKi8vT8FgUJLkcDg0a9asFE+XffLy8sadP3Trrbdqzpw5nFeUAn/+53+u+++/X9/5znf0J3/yJ+rq6tLLL7+sl19+OdWjZaWGhgZ9+9vf1p133ql77rlH77//vr773e/qT//0T1M9WnqL4aq+//3vx+68885YTk5OrLq6OnbkyJFUj5SVJE34+Nu//dtUj4b/89BDD8U2btyY6jGy1qFDh2IVFRWx3Nzc2Pz582Mvv/xyqkfKWuFwOLZx48bYnXfeGbPb7bG5c+fG/uIv/iJ26dKlVI+W1rgPCwAAMB7nsAAAAOMRLAAAwHgECwAAMB7BAgAAjEewAAAA4xEsAADAeAQLAAAwHsECAACMR7AAAADjESwAAMB4BAsAADAewQIAAIz3vzOp8Kk2lVdMAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# your code here\n", - "# Please label the axes and give a title to the plot \n" + "# Please label the axes and give a title to the plot \n", + "x = np.arange(0,10)\n", + "plt.plot(x, poisson_dist.pmf(x),\"o\")\n", + "plt.show()" ] } ], @@ -192,7 +332,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.11.2" } }, "nbformat": 4,