From 2cb05bf942234b5a3bb3bbcfd3075e9652ffbfb2 Mon Sep 17 00:00:00 2001 From: HenrikSoeder Date: Tue, 21 Nov 2023 21:26:21 +0000 Subject: [PATCH] Lab Done --- your-code/continuous.ipynb | 454 +++++++++++++++++++++++++++++++++---- your-code/discrete.ipynb | 213 ++++++++++++++--- 2 files changed, 601 insertions(+), 66 deletions(-) diff --git a/your-code/continuous.ipynb b/your-code/continuous.ipynb index 32218e9..5fad7f9 100644 --- a/your-code/continuous.ipynb +++ b/your-code/continuous.ipynb @@ -35,11 +35,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 97, "metadata": {}, "outputs": [], "source": [ "from scipy.stats import uniform\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "x = uniform.rvs(size=10)\n", "a = 2\n", "b = 3\n", @@ -73,27 +84,64 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ - "# your code here\n" + "def random_number(bottom, ceiling, count):\n", + " \n", + " x = uniform.rvs(size=count) # count\n", + " a = bottom\n", + " b = ceiling\n", + " randoms = a + (b-a)*x\n", + " \n", + " plt.hist(randoms,bins=10)\n", + " plt.ylim((0,130))\n", + " plt.show()" ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": 56, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "How are the two distributions different?" + "random_number(bottom = 10, ceiling = 15, count = 100)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 60, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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WKisrNX78eM2fP18/+clPVFdXFzfP4/HEPTbGDBg7oby8XJFIxDk6OzvdXjYAALCI64GSm5urSy+9NG7skksu0YEDByRJgUBAkgbcLenq6hpwV+UEr9errKysuAMAAJy5XA+UKVOm6N13340b2717ty688EJJUn5+vgKBgJqampzzfX19am5uVlFRkdvLAQAAKcj13+L52c9+pqKiIlVWVurmm2/WG2+8oTVr1mjNmjWSPnlqp6ysTJWVlSooKFBBQYEqKyuVkZGhuXPnur0cAACQglwPlCuvvFKbNm1SeXm5Hn74YeXn56umpka33367M2fZsmXq7e3VwoUL1d3drUmTJqmxsVGZmZluLwcAAKQgjzHGJHsRgxWNRuXz+RSJRBLyepQxK553/ZoAAKSSfatmuX7Nwfz85r14AACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUSHiihUEgej0dlZWXOmDFGFRUVCgaDSk9PV0lJidrb2xO9FAAAkCISGiitra1as2aNLr/88rjxqqoqVVdXq7a2Vq2trQoEApoxY4Z6enoSuRwAAJAiEhYohw8f1u233661a9dq5MiRzrgxRjU1NVq5cqXmzJmjwsJC1dfX6+jRo2poaEjUcgAAQApJWKAsWrRIs2bN0vTp0+PGOzo6FA6HVVpa6ox5vV4VFxerpaUlUcsBAAApJC0RF92wYYO2b9+u1tbWAefC4bAkye/3x437/X7t37//lNeLxWKKxWLO42g06uJqAQCAbVy/g9LZ2al7771X69ev1/Dhwz91nsfjiXtsjBkwdkIoFJLP53OOvLw8V9cMAADs4nqgtLW1qaurSxMmTFBaWprS0tLU3Nysxx9/XGlpac6dkxN3Uk7o6uoacFflhPLyckUiEefo7Ox0e9kAAMAirj/FM23aNO3atStu7Ec/+pEuvvhiLV++XBdddJECgYCampo0fvx4SVJfX5+am5v16KOPnvKaXq9XXq/X7aUCAABLuR4omZmZKiwsjBsbMWKEcnJynPGysjJVVlaqoKBABQUFqqysVEZGhubOnev2cgAAQApKyItkP8+yZcvU29urhQsXqru7W5MmTVJjY6MyMzOTsRwAAGAZjzHGJHsRgxWNRuXz+RSJRJSVleX69ceseN71awIAkEr2rZrl+jUH8/Ob9+IBAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdVwPlFAopCuvvFKZmZkaNWqUbrjhBr377rtxc4wxqqioUDAYVHp6ukpKStTe3u72UgAAQIpyPVCam5u1aNEivf7662pqatLHH3+s0tJSHTlyxJlTVVWl6upq1dbWqrW1VYFAQDNmzFBPT4/bywEAACkoze0L/v3vf497vG7dOo0aNUptbW266qqrZIxRTU2NVq5cqTlz5kiS6uvr5ff71dDQoPnz57u9JAAAkGIS/hqUSCQiScrOzpYkdXR0KBwOq7S01Jnj9XpVXFyslpaWU14jFospGo3GHQAA4MyV0EAxxmjp0qWaOnWqCgsLJUnhcFiS5Pf74+b6/X7n3MlCoZB8Pp9z5OXlJXLZAAAgyRIaKIsXL9abb76pP/3pTwPOeTyeuMfGmAFjJ5SXlysSiThHZ2dnQtYLAADs4PprUE5YsmSJNm/erC1btmj06NHOeCAQkPTJnZTc3FxnvKura8BdlRO8Xq+8Xm+ilgoAACzj+h0UY4wWL16sjRs36pVXXlF+fn7c+fz8fAUCATU1NTljfX19am5uVlFRkdvLAQAAKcj1OyiLFi1SQ0OD/vKXvygzM9N5XYnP51N6ero8Ho/KyspUWVmpgoICFRQUqLKyUhkZGZo7d67bywEAACnI9UCpq6uTJJWUlMSNr1u3Tj/84Q8lScuWLVNvb68WLlyo7u5uTZo0SY2NjcrMzHR7OQAAIAW5HijGmM+d4/F4VFFRoYqKCrc/PQAAOAPwXjwAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwTlID5YknnlB+fr6GDx+uCRMm6J///GcylwMAACyRtEB59tlnVVZWppUrV2rHjh367ne/q5kzZ+rAgQPJWhIAALBE0gKlurpad999t3784x/rkksuUU1NjfLy8lRXV5esJQEAAEukJeOT9vX1qa2tTStWrIgbLy0tVUtLy4D5sVhMsVjMeRyJRCRJ0Wg0Ievrjx1NyHUBAEgVifgZe+KaxpjPnZuUQPnPf/6j48ePy+/3x437/X6Fw+EB80OhkH71q18NGM/Ly0vYGgEAOJv5ahJ37Z6eHvl8vs+ck5RAOcHj8cQ9NsYMGJOk8vJyLV261Hnc39+v//73v8rJyTnl/K8iGo0qLy9PnZ2dysrKcvXa+H/Y59ODfT592OvTg30+PRK1z8YY9fT0KBgMfu7cpATKeeedpyFDhgy4W9LV1TXgrookeb1eeb3euLFzzz03kUtUVlYW3/ynAft8erDPpw97fXqwz6dHIvb58+6cnJCUF8kOGzZMEyZMUFNTU9x4U1OTioqKkrEkAABgkaQ9xbN06VL94Ac/0MSJEzV58mStWbNGBw4c0IIFC5K1JAAAYImkBcott9yiDz/8UA8//LAOHTqkwsJCvfDCC7rwwguTtSRJnzyd9NBDDw14SgnuYp9PD/b59GGvTw/2+fSwYZ895ov8rg8AAMBpxHvxAAAA6xAoAADAOgQKAACwDoECAACsc1YGypYtWzR79mwFg0F5PB4999xzceeNMaqoqFAwGFR6erpKSkrU3t6enMWmsFAopCuvvFKZmZkaNWqUbrjhBr377rtxc9hrd9TV1enyyy93/qjS5MmT9eKLLzrn2efECIVC8ng8Kisrc8bY66+uoqJCHo8n7ggEAs559tg9H3zwge644w7l5OQoIyNDV1xxhdra2pzzydzrszJQjhw5onHjxqm2tvaU56uqqlRdXa3a2lq1trYqEAhoxowZ6unpOc0rTW3Nzc1atGiRXn/9dTU1Nenjjz9WaWmpjhw54sxhr90xevRorVq1Stu2bdO2bdv0ve99T9dff73zDwn77L7W1latWbNGl19+edw4e+2Oyy67TIcOHXKOXbt2OefYY3d0d3drypQpGjp0qF588UW9/fbbeuyxx+L+UntS99qc5SSZTZs2OY/7+/tNIBAwq1atcsY++ugj4/P5zJNPPpmEFZ45urq6jCTT3NxsjGGvE23kyJHm97//PfucAD09PaagoMA0NTWZ4uJic++99xpj+J52y0MPPWTGjRt3ynPssXuWL19upk6d+qnnk73XZ+UdlM/S0dGhcDis0tJSZ8zr9aq4uFgtLS1JXFnqi0QikqTs7GxJ7HWiHD9+XBs2bNCRI0c0efJk9jkBFi1apFmzZmn69Olx4+y1e/bs2aNgMKj8/Hzdeuut2rt3ryT22E2bN2/WxIkTddNNN2nUqFEaP3681q5d65xP9l4TKCc58QaGJ79pod/vH/DmhvjijDFaunSppk6dqsLCQknstdt27dqlr33ta/J6vVqwYIE2bdqkSy+9lH122YYNG7R9+3aFQqEB59hrd0yaNElPP/20XnrpJa1du1bhcFhFRUX68MMP2WMX7d27V3V1dSooKNBLL72kBQsW6J577tHTTz8tKfnfz0n7U/e283g8cY+NMQPG8MUtXrxYb775prZu3TrgHHvtjm9+85vauXOn/ve//+nPf/6z5s2bp+bmZuc8+/zVdXZ26t5771VjY6OGDx/+qfPY669m5syZzn+PHTtWkydP1te//nXV19frO9/5jiT22A39/f2aOHGiKisrJUnjx49Xe3u76urqdOeddzrzkrXX3EE5yYlXip9ch11dXQMqEl/MkiVLtHnzZr366qsaPXq0M85eu2vYsGH6xje+oYkTJyoUCmncuHH63e9+xz67qK2tTV1dXZowYYLS0tKUlpam5uZmPf7440pLS3P2k71214gRIzR27Fjt2bOH72cX5ebm6tJLL40bu+SSS3TgwAFJyf83mkA5SX5+vgKBgJqampyxvr4+NTc3q6ioKIkrSz3GGC1evFgbN27UK6+8ovz8/Ljz7HViGWMUi8XYZxdNmzZNu3bt0s6dO51j4sSJuv3227Vz505ddNFF7HUCxGIxvfPOO8rNzeX72UVTpkwZ8Kcfdu/e7bxpb9L3OuEvw7VQT0+P2bFjh9mxY4eRZKqrq82OHTvM/v37jTHGrFq1yvh8PrNx40aza9cuc9ttt5nc3FwTjUaTvPLU8tOf/tT4fD7z2muvmUOHDjnH0aNHnTnstTvKy8vNli1bTEdHh3nzzTfNz3/+c3POOeeYxsZGYwz7nEj//2/xGMNeu+G+++4zr732mtm7d695/fXXzbXXXmsyMzPNvn37jDHssVveeOMNk5aWZh555BGzZ88e88c//tFkZGSY9evXO3OSuddnZaC8+uqrRtKAY968ecaYT3616qGHHjKBQMB4vV5z1VVXmV27diV30SnoVHssyaxbt86Zw16746677jIXXnihGTZsmDn//PPNtGnTnDgxhn1OpJMDhb3+6m655RaTm5trhg4daoLBoJkzZ45pb293zrPH7vnrX/9qCgsLjdfrNRdffLFZs2ZN3Plk7rXHGGMSf58GAADgi+M1KAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOv8H1T15GXyAPX4AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your answer here:\n" + "random_number(bottom=10, ceiling= 60, count= 1000)" ] }, { @@ -114,11 +162,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 95, "metadata": {}, "outputs": [], "source": [ - "# your code here\n" + "def random_norm_numbers(mean, std, count):\n", + " data = np.random.normal(mean, std, count)\n", + " \n", + " plt.hist(data, bins=50)\n", + " \n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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", 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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
\n", + "
" + ], + "text/plain": [ + " Make Model Year Engine Displacement Cylinders \\\n", + "0 AM General DJ Po Vehicle 2WD 1984 2.5 4.0 \n", + "1 AM General FJ8c Post Office 1984 4.2 6.0 \n", + "2 AM General Post Office DJ5 2WD 1985 2.5 4.0 \n", + "\n", + " Transmission Drivetrain Vehicle Class Fuel Type \\\n", + "0 Automatic 3-spd 2-Wheel Drive Special Purpose Vehicle 2WD Regular \n", + "1 Automatic 3-spd 2-Wheel Drive Special Purpose Vehicle 2WD Regular \n", + "2 Automatic 3-spd Rear-Wheel Drive Special Purpose Vehicle 2WD Regular \n", + "\n", + " Fuel Barrels/Year City MPG Highway MPG Combined MPG \\\n", + "0 19.388824 18 17 17 \n", + "1 25.354615 13 13 13 \n", + "2 20.600625 16 17 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 " + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('vehicles.csv')\n", + "df.head(3)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -158,11 +378,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 103, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "df[\"Fuel Barrels/Year\"].hist()\n", + "plt.show()" ] }, { @@ -174,11 +408,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 104, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "df[\"CO2 Emission Grams/Mile\"].hist()\n", + "plt.show()" ] }, { @@ -190,11 +436,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 105, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "df[\"Combined MPG\"].hist()\n", + "plt.show()" ] }, { @@ -206,11 +464,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 106, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "''" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# you answer here:\n" + "\"\"\"\n", + "it seems like all of the variables are normally distributed\n", + "i know because of the histrogram's shape\n", + "\n", + "\"\"\"\n" ] }, { @@ -237,11 +510,83 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 117, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "non-default argument follows default argument (1337851570.py, line 1)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m Cell \u001b[0;32mIn[117], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m def random_num_exp(mean = 10,count):\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m non-default argument follows default argument\n" + ] + } + ], "source": [ - "# your code here\n" + "\n", + "def random_num_exp(mean = 10,count):\n", + " \n", + " \n", + " number = np.random.exponential(mean,size=count)\n", + " \n", + " plt.hist(number,bins=100)\n", + " plt.show()\n", + "\n", + " \n", + " \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "None" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(random_num_exp(mean=1,count=1000))\n", + "display(random_num_exp(mean=100,count=1000))\n" ] }, { @@ -257,7 +602,8 @@ "metadata": {}, "outputs": [], "source": [ - "# your answer here:\n" + "# the first plot is more equal distributed\n", + "\n" ] }, { @@ -273,12 +619,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 121, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.7534030360583935" + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your answer here\n", - "# Hint: This is same as saying P(x<15)\n" + "from scipy.stats import expon\n", + "\n", + "lambda_inv=10\n", + "expon_dist = expon(scale=lambda_inv)\n", + "expon_dist.cdf(14)\n" ] }, { @@ -290,17 +650,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 122, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.24659696394160646" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your answer here\n" + "1-expon_dist.cdf(14)\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -314,7 +692,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.11.4" } }, "nbformat": 4, diff --git a/your-code/discrete.ipynb b/your-code/discrete.ipynb index 310a3c6..11762de 100644 --- a/your-code/discrete.ipynb +++ b/your-code/discrete.ipynb @@ -33,39 +33,64 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the probability that the fruit is apple: 0.6\n", + "the probability that the fruit is orange 0.4\n" + ] + } + ], "source": [ "\"\"\"\n", "Calculate:\n", "p = probability that the fruit is an apple \n", "q = probability that the fruit is an orange\n", "\"\"\"\n", + "total = 100\n", + "p = 60/100\n", + "q = 1-p\n", "\n", - "# your code here\n" + "print(\"the probability that the fruit is apple: \",p)\n", + "print(\"the probability that the fruit is orange\", q)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now we take a random sample of 20 fruits from the basket. After each fruit is taken, a new fruit of the same type is replaced in the basket. Therefore, every time we are taking 1 fruit from 100 fruits. \n", + "Now we take a random sample of 20 fruits from the basket. After each fruit is taken, a new fruit of the same type is replaced in the basket. Therefore, every time we are taking 1 fruit from 100 fruits.\n", "\n", - "1. **What is the probability that the first 5 fruits are all apples?**\n", + "### What is the probability that the first 5 fruits are all apples?\n", "\n", - "1. **What is the probability that the first 5 fruits are all apples and the next 15 fruits are all oranges?**\n", + "### What is the probability that the first 5 fruits are all apples and the next 15 fruits are all oranges?\n", "\n", - "You can include the `p` and `q` probabilities you previous calculated in your solution." + "You can include the p and q probabilities you previous calculated in your solution." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the chance of every fruit beeing an apple: 0.07775999999999998\n", + "What is the probability that the first 5 fruits are all apples and the next 15 fruits are all oranges: 8.349416423424006e-08\n" + ] + } + ], "source": [ - "# your code here\n" + "print(\"the chance of every fruit beeing an apple:\",p**5)\n", + "print(\"What is the probability that the first 5 fruits are all apples and the next 15 fruits are all oranges:\", (p**5)*(q**15))\n" ] }, { @@ -83,11 +108,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.0012944935222876587" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here\n" + "from scipy.stats import binom\n", + "import numpy as np\n", + "\n", + "p = 0.6\n", + "n = 20\n", + "k = 5\n", + "\n", + "binom.pmf(5, 20, 0.6)\n" ] }, { @@ -101,11 +144,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.00031703112116863004" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here\n" + "#p = 0.6\n", + "#n = 20\n", + "#k = <5\n", + "\n", + "binomial_dist = binom.cdf(4, 20, 0.6)\n", + "binomial_dist" ] }, { @@ -119,12 +178,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "# your code here\n", - "# Please label the axes and give a title to the plot\n" + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "x = np.arange(1,21)\n", + "\n", + "plt.plot(x, binom.pmf(x, 20, 0.6), \"o\", color = \"blue\", markersize = \"8\")\n", + "\n", + "plt.xlabel(\"number of hits\")\n", + "plt.title(\"PDF\")\n", + "plt.show()" ] }, { @@ -151,11 +239,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ - "# your code here\n" + "import math" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0537750255819468" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expected = 2.3\n", + "k = 5\n", + "\n", + "p_5 = (math.exp(-expected) * expected**k) / math.factorial(k)\n", + "p_5" ] }, { @@ -167,18 +281,61 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.053775025581946814\n" + ] + } + ], "source": [ - "# your code here\n", - "# Please label the axes and give a title to the plot \n" + "from scipy.stats import poisson\n", + "mu = 2.3\n", + "poisson_dist = poisson(mu)\n", + "\n", + "print(poisson_dist.pmf(5))\n" ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X = np.arange(0, 11)\n", + "plt.plot(X, poisson_dist.pmf(X), \"o\",color = \"blue\", markersize = \"8\")\n", + "plt.title('Poisson Distribution')\n", + "plt.ylabel('Probability')\n", + "plt.xlabel('Number of Tries')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -192,7 +349,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.11.4" } }, "nbformat": 4,