From 9d79e987ae4153a2613e47808a691c155f6c5b4d Mon Sep 17 00:00:00 2001 From: Sergio Soutinho Date: Tue, 8 Aug 2023 14:36:01 +0100 Subject: [PATCH] lab done --- your-code/continuous.ipynb | 434 ++++++++++++++++++++++++++++++++++--- your-code/discrete.ipynb | 179 +++++++++++++-- 2 files changed, 558 insertions(+), 55 deletions(-) diff --git a/your-code/continuous.ipynb b/your-code/continuous.ipynb index 32218e9..449b7dd 100644 --- a/your-code/continuous.ipynb +++ b/your-code/continuous.ipynb @@ -23,6 +23,27 @@ "Use either or both of the lbraries in this exercise." ] }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import math\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from scipy.stats import bernoulli\n", + "from scipy.stats import binom\n", + "from scipy.stats import geom\n", + "from scipy.stats import poisson\n", + "\n", + "from scipy.stats import uniform\n", + "from scipy.stats import expon\n", + "from scipy.stats import norm" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -35,9 +56,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2.37955963 2.66148515 2.48000774 2.2702791 2.23618001 2.88458506\n", + " 2.53329671 2.33159991 2.64259503 2.42580003]\n" + ] + } + ], "source": [ "from scipy.stats import uniform\n", "x = uniform.rvs(size=10)\n", @@ -73,11 +103,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(array([103., 99., 96., 97., 102., 85., 101., 105., 102., 110.]),\n", + " array([10.00008552, 14.99960692, 19.99912832, 24.99864972, 29.99817112,\n", + " 34.99769251, 39.99721391, 44.99673531, 49.99625671, 54.99577811,\n", + " 59.99529951]),\n", + " )" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "# 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\n", + "\n", + "random_numbers(10,15,100)\n", + "random_numbers(10,60,1000)\n", + "\n", + "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))" ] }, { @@ -89,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -114,11 +180,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "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)" ] }, { @@ -130,11 +198,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 9., 30., 87., 138., 223., 229., 163., 85., 28., 8.]),\n", + " array([-136.43625756, -107.04458443, -77.6529113 , -48.26123817,\n", + " -18.86956504, 10.52210809, 39.91378122, 69.30545435,\n", + " 98.69712748, 128.08880061, 157.48047374]),\n", + " )" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Hj+rw4cPKysqSJBUUFGjYsGFqaGiw27S0tGjv3r0qLCw0VSYA9Bun7AEgxjo6OvTJJ5/Yr5ubm7V7926lp6crPT1dPp9Pt956q7KysnTgwAE9+OCDyszM1C233CJJcrlcmjt3rhYuXKiMjAylp6eroqJC+fn59l33ADCQEEgBIMbeeecd3XDDDfbrU9d1zp49W6tWrdKePXv07LPP6ssvv1RWVpZuuOEGbdiwQampqfZ7li9frsTERM2cOVOdnZ2aMGGCamtrlZCQEPP9AYDzFfYpe54wAgDnp6ioSJZl9Vpqa2uVnJys1157Ta2trerq6tLBgwdVW1vb6+aj4cOHq7q6WkePHtWJEyf08ssvc4MSgAEr7EDKE0YAAAAQSWGfsucJIwAAAIikqNxlH+knjPB0EQAAgMEr4oE0Gk8Y4ekiAAAAg1fE77KPxhNGeLoIAADA4BX1ifEj8YQRni4CAAAweEU9kPKEEQAAAJxN2KfsecIIAAAAIinsQMoTRgAAABBJYQfSU08Y6ctrr732rZ9x6gkj1dXV4X49AAADysWLNpsuAYh7Ub+GFAAAADgbAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAqETTBSC+Xbxos+kSAADAIMcIKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCgCKQAAAIxKNF0AgPBdvGhzTL7nwGNTYvI9AIChjRFSAAAAGEUgBYAYe/PNNzVt2jR5vV45HA698MILIdsty5LP55PX61VycrKKioq0b9++kDbBYFALFixQZmamUlJSNH36dB05ciSGewEAkUMgBYAYO378uK688krV1NSccfvSpUu1bNky1dTUaNeuXfJ4PJo0aZKOHTtmtykrK9OmTZtUX1+vHTt2qKOjQ1OnTlVPT0+sdgMAIoZrSAEgxkpKSlRSUnLGbZZlacWKFVqyZIlmzJghSaqrq5Pb7db69et17733KhAIaPXq1Vq7dq0mTpwoSVq3bp2ys7O1detWTZ48OWb7AgCRwAgpAMSR5uZm+f1+FRcX2+ucTqfGjx+vnTt3SpKamprU3d0d0sbr9SovL89ucybBYFDt7e0hCwDEAwIpAMQRv98vSXK73SHr3W63vc3v9yspKUkjR47ss82ZVFVVyeVy2Ut2dnaEqweA/iGQAkAccjgcIa8ty+q17nTf1mbx4sUKBAL2cvjw4YjUCgDni0AKAHHE4/FIUq+RztbWVnvU1OPxqKurS21tbX22OROn06m0tLSQBQDiQdiBlOlKACB6cnJy5PF41NDQYK/r6upSY2OjCgsLJUkFBQUaNmxYSJuWlhbt3bvXbgMAA0nYgZTpSgDg/HR0dGj37t3avXu3pG9uZNq9e7cOHTokh8OhsrIyVVZWatOmTdq7d6/mzJmjESNGaNasWZIkl8uluXPnauHChXr99df13nvv6c4771R+fr591z0ADCRhT/vEdCUAcH7eeecd3XDDDfbr8vJySdLs2bNVW1ur+++/X52dnZo3b57a2to0btw4bdmyRampqfZ7li9frsTERM2cOVOdnZ2aMGGCamtrlZCQEPP9AYDzFdFrSKM1XQlTlQAYTIqKimRZVq+ltrZW0jc3NPl8PrW0tOirr75SY2Oj8vLyQj5j+PDhqq6u1tGjR3XixAm9/PLL3DUPYMCKaCCN1nQlTFUCAAAweEXlLvtIT1fCVCUAAACDV0QDabSmK2GqEgAAgMErooGU6UoAAAAQrrDvsu/o6NAnn3xivz41XUl6erpGjx5tT1eSm5ur3NxcVVZW9jldSUZGhtLT01VRUcF0JQAAAENU2IGU6UoAAAAQSQ7LsizTRYSrvb1dLpdLgUCA60mj7OJFm02XAIMOPDbFdAnGDIV+ZijsYzygH41fQ7mPi4Vw+hieZQ8AAACjCKQAAAAwikAKAAAAo8K+qQkAgMGC6zuB+MAIKQAAAIxihBQAAAxJsRgh507+c8MIKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAAAAjCKQAgAAwCgCKQAAAIwikAIAAMAoAikAAACMIpACAADAKAIpAMQZn88nh8MRsng8Hnu7ZVny+Xzyer1KTk5WUVGR9u3bZ7BiADg/BFIAiENXXHGFWlpa7GXPnj32tqVLl2rZsmWqqanRrl275PF4NGnSJB07dsxgxQDQfwRSAIhDiYmJ8ng89nLhhRdK+mZ0dMWKFVqyZIlmzJihvLw81dXV6cSJE1q/fr3hqgGgfyIeSDnVBADnb//+/fJ6vcrJydHtt9+u//3vf5Kk5uZm+f1+FRcX222dTqfGjx+vnTt3nvUzg8Gg2tvbQxYAiAdRGSHlVBMA9N+4ceP07LPP6rXXXtPf/vY3+f1+FRYW6ujRo/L7/ZIkt9sd8h63221v60tVVZVcLpe9ZGdnR20fACAcUQmknGoCgP4rKSnRrbfeqvz8fE2cOFGbN2+WJNXV1dltHA5HyHssy+q17nSLFy9WIBCwl8OHD0e+eADoh6gE0micagKAoSolJUX5+fnav3+/fQnU6aOhra2tvUZNT+d0OpWWlhayAEA8iHggjcapJq57AjCUBYNBffjhh8rKylJOTo48Ho8aGhrs7V1dXWpsbFRhYaHBKgGg/xIj/YElJSX2f+fn5+uaa67R97//fdXV1enqq6+WFP6ppqqqKj388MORLhUA4lJFRYWmTZum0aNHq7W1VX/+85/V3t6u2bNny+FwqKysTJWVlcrNzVVubq4qKys1YsQIzZo1y3TpANAvUZ/2KRKnmrjuCcBQcuTIEd1xxx267LLLNGPGDCUlJentt9/WmDFjJEn333+/ysrKNG/ePI0dO1affvqptmzZotTUVMOVA0D/RHyE9HSnTjVdd911IaeafvzjH0v6v1NNjz/+eJ+f4XQ65XQ6o10qAMSF+vr6s253OBzy+Xzy+XyxKQgAoizigZRTTQAAAAhHxAPpqVNNn3/+uS688EJdffXVvU41dXZ2at68eWpra9O4ceM41QQAADCERTyQcqoJGDwuXrQ5Jt9z4LEpMfkeAEB8ivo1pIiOWAUFAACAaIv6XfYAAADA2RBIAQAAYBSBFAAAAEYRSAEAAGAUgRQAAABGEUgBAABgFIEUAAAARhFIAQAAYBSBFAAAAEYRSAEAAGAUgRQAAABGEUgBAABgFIEUAAAARhFIAQAAYBSBFAAAAEYlmi4AAIDTXbxos+kSAMQQI6QAAAAwihFSAACAKInVaP+Bx6bE5HuihRFSAAAAGEUgBQAAgFEEUgAAABhFIAUAAIBRBFIAAAAYRSAFAACAUQRSAAAAGEUgBQAAgFEEUgAAABhFIAUAAIBRBFIAAAAYRSAFAACAUQRSAAAAGJVouoDB6OJFm02XAAAAMGAwQgoAAACjCKQAAAAwilP2AIyLxWUuBx6bEvXvAAD0DyOkAAAAMIpACgAAAKMIpAAAADCKQAoAAACjCKQAAAAwikAKAAAAowikAAAAMIp5SAEA54xHIwOIBgIpAADAABerPxaj9ZARTtkDAADAKAIpAAAAjCKQAgAAwCgCKQAAAIwikAIAAMAoo4F05cqVysnJ0fDhw1VQUKC33nrLZDkAMODQjwIYDIxN+7RhwwaVlZVp5cqV+tnPfqa//vWvKikp0QcffKDRo0dH5TuZPw/AYGKiHwWAaDA2Qrps2TLNnTtXv/71r/XDH/5QK1asUHZ2tlatWmWqJAAYUOhHAQwWRkZIu7q61NTUpEWLFoWsLy4u1s6dO3u1DwaDCgaD9utAICBJam9vD+t7TwZP9KNaAINBuP3FqfaWZUWjnPMWbj8qRaYvpR8FhrZw+otw+lEjgfTzzz9XT0+P3G53yHq32y2/39+rfVVVlR5++OFe67Ozs6NWI4DBxbWif+87duyYXC5XRGuJhHD7UYm+FMD5609fei79qNFHhzocjpDXlmX1WidJixcvVnl5uf365MmT+uKLL5SRkXHG9kNVe3u7srOzdfjwYaWlpZkuZ8DiOEbGQD+OlmXp2LFj8nq9pks5q3PtR6WB35cO9H9T8YxjGz1D+diG048aCaSZmZlKSEjo9Vd8a2trr7/2JcnpdMrpdIas++53vxvNEge0tLS0IfePPho4jpExkI9jPI6MnhJuPyoNnr50IP+bincc2+gZqsf2XPtRIzc1JSUlqaCgQA0NDSHrGxoaVFhYaKIkABhQ6EcBDCbGTtmXl5frrrvu0tixY3XNNdfomWee0aFDh3TfffeZKgkABhT6UQCDhbFAetttt+no0aN65JFH1NLSory8PL3yyisaM2aMqZIGPKfTqYceeqjXKTmEh+MYGRzH6Btq/Sj/pqKHYxs9HNtz47DidU4TAAAADAk8yx4AAABGEUgBAABgFIEUAAAARhFIAQAAYBSBdBD4+uuv9Yc//EE5OTlKTk7WJZdcokceeUQnT540XVpce/PNNzVt2jR5vV45HA698MILIdsty5LP55PX61V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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": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "vehicles = pd.read_csv(\"vehicles.csv\")\n", + "vehicles.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "plt.hist(vehicles[\"Fuel Barrels/Year\"])\n", + "plt.show()" ] }, { @@ -174,11 +461,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "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()" ] }, { @@ -190,11 +490,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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G0dHRys7ODve0AADAEBTZ3xM6Ozv13nvvOa+bm5vl8/mUmJgoj8ejf/7nf9abb76p3/72t+rt7XV2XRITExUVFSXLsjR//nyVlJQoKSlJiYmJKi0tVVZWlqZMmSJJGjNmjKZPn66ioiJt2rRJkrRgwQLl5+dr9OjRkqS8vDxlZGTI6/Vq/fr1OnnypEpLS1VUVMRODQAAkPQZgs4bb7yhO+64w3m9fPlySdK8efO0evVqvfjii5Kkm266KeS8V199VZMmTZIkbdy4UZGRkZo9e7a6uro0efJkbd68WREREU791q1btWzZMufbWQUFBSHP7omIiND27du1aNEi3XrrrYqJiVFhYaEeffTR/k4JAAAY6nM9R2eo4zk6ZuA5OgDw5XJZPUcHAABgsBB0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICx+h10Xn/9dc2cOVMej0cul0vbtm0L6bdtW6tXr5bH41FMTIwmTZqk/fv3h9QEg0EtXbpUycnJio2NVUFBgY4ePRpS4/f75fV6ZVmWLMuS1+vVqVOnQmqOHDmimTNnKjY2VsnJyVq2bJm6u7v7OyUAAGCofgedM2fOaOzYsaqoqLhg/7p167RhwwZVVFRo7969crvdmjp1qk6fPu3UFBcXq6qqSpWVlaqvr1dnZ6fy8/PV29vr1BQWFsrn86m6ulrV1dXy+Xzyer1Of29vr2bMmKEzZ86ovr5elZWVev7551VSUtLfKQEAAEO5bNu2P/PJLpeqqqo0a9YsSZ/s5ng8HhUXF2vFihWSPtm9SU1N1dq1a7Vw4UIFAgGNGDFCW7Zs0Zw5cyRJx44dU1pamnbs2KFp06bpwIEDysjIUENDg3JyciRJDQ0Nys3N1cGDBzV69Gi99NJLys/PV0tLizwejySpsrJS9957r9rb2xUfH/+p4+/o6JBlWQoEApdUj8vTdSu3D/YQ+u39NTMGewgAMGT15/M7rPfoNDc3q62tTXl5eU5bdHS0Jk6cqJ07d0qSGhsb1dPTE1Lj8XiUmZnp1OzatUuWZTkhR5LGjx8vy7JCajIzM52QI0nTpk1TMBhUY2PjBccXDAbV0dERcgAAAHOFNei0tbVJklJTU0PaU1NTnb62tjZFRUUpISHhojUpKSl9rp+SkhJSc/77JCQkKCoqyqk5X3l5uXPPj2VZSktL+wyzBAAAQ8WAfOvK5XKFvLZtu0/b+c6vuVD9Z6n5S6tWrVIgEHCOlpaWi44JAAAMbWENOm63W5L67Ki0t7c7uy9ut1vd3d3y+/0XrTl+/Hif6584cSKk5vz38fv96unp6bPTc050dLTi4+NDDgAAYK6wBp1Ro0bJ7XartrbWaevu7lZdXZ0mTJggScrOztawYcNCalpbW9XU1OTU5ObmKhAIaM+ePU7N7t27FQgEQmqamprU2trq1NTU1Cg6OlrZ2dnhnBYAABiiIvt7Qmdnp9577z3ndXNzs3w+nxITEzVy5EgVFxerrKxM6enpSk9PV1lZmYYPH67CwkJJkmVZmj9/vkpKSpSUlKTExESVlpYqKytLU6ZMkSSNGTNG06dPV1FRkTZt2iRJWrBggfLz8zV69GhJUl5enjIyMuT1erV+/XqdPHlSpaWlKioqYqcGAABI+gxB54033tAdd9zhvF6+fLkkad68edq8ebMeeOABdXV1adGiRfL7/crJyVFNTY3i4uKcczZu3KjIyEjNnj1bXV1dmjx5sjZv3qyIiAinZuvWrVq2bJnz7ayCgoKQZ/dERERo+/btWrRokW699VbFxMSosLBQjz76aP9XAQAAGOlzPUdnqOM5OmbgOToA8OUyaM/RAQAAuJwQdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFiRgz0AXF6uW7l9sIcAAEDYsKMDAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADBW2IPOxx9/rP/8z//UqFGjFBMTo+uvv14PP/ywzp4969TYtq3Vq1fL4/EoJiZGkyZN0v79+0OuEwwGtXTpUiUnJys2NlYFBQU6evRoSI3f75fX65VlWbIsS16vV6dOnQr3lAAAwBAV9qCzdu1aPfnkk6qoqNCBAwe0bt06rV+/Xj/+8Y+dmnXr1mnDhg2qqKjQ3r175Xa7NXXqVJ0+fdqpKS4uVlVVlSorK1VfX6/Ozk7l5+ert7fXqSksLJTP51N1dbWqq6vl8/nk9XrDPSUAADBEuWzbtsN5wfz8fKWmpuqZZ55x2v7pn/5Jw4cP15YtW2Tbtjwej4qLi7VixQpJn+zepKamau3atVq4cKECgYBGjBihLVu2aM6cOZKkY8eOKS0tTTt27NC0adN04MABZWRkqKGhQTk5OZKkhoYG5ebm6uDBgxo9evSnjrWjo0OWZSkQCCg+Pj6cyzBk8WTkL8b7a2YM9hAAYMjqz+d32Hd0brvtNv3Xf/2X3nnnHUnSH//4R9XX1+sf/uEfJEnNzc1qa2tTXl6ec050dLQmTpyonTt3SpIaGxvV09MTUuPxeJSZmenU7Nq1S5ZlOSFHksaPHy/Lspya8wWDQXV0dIQcAADAXGH/XVcrVqxQIBDQ17/+dUVERKi3t1ePPPKI/vVf/1WS1NbWJklKTU0NOS81NVWHDx92aqKiopSQkNCn5tz5bW1tSklJ6fP+KSkpTs35ysvL9f3vf//zTRAAAAwZYQ86v/rVr/Tss8/queee0w033CCfz6fi4mJ5PB7NmzfPqXO5XCHn2bbdp+1859dcqP5i11m1apWWL1/uvO7o6FBaWtolzQsIp6H4I0J+3AZgKAp70Ln//vu1cuVK3X333ZKkrKwsHT58WOXl5Zo3b57cbrekT3Zkrr76aue89vZ2Z5fH7Xaru7tbfr8/ZFenvb1dEyZMcGqOHz/e5/1PnDjRZ7fonOjoaEVHR4dnogAA4LIX9nt0PvzwQ11xRehlIyIinK+Xjxo1Sm63W7W1tU5/d3e36urqnBCTnZ2tYcOGhdS0traqqanJqcnNzVUgENCePXucmt27dysQCDg1AADgyy3sOzozZ87UI488opEjR+qGG27QH/7wB23YsEHf+ta3JH3y46bi4mKVlZUpPT1d6enpKisr0/Dhw1VYWChJsixL8+fPV0lJiZKSkpSYmKjS0lJlZWVpypQpkqQxY8Zo+vTpKioq0qZNmyRJCxYsUH5+/iV94woAAJgv7EHnxz/+sR588EEtWrRI7e3t8ng8Wrhwob73ve85NQ888IC6urq0aNEi+f1+5eTkqKamRnFxcU7Nxo0bFRkZqdmzZ6urq0uTJ0/W5s2bFRER4dRs3bpVy5Ytc76dVVBQoIqKinBPCQAADFFhf47OUMJzdPoaijfJ4ovBzcgALheD+hwdAACAywVBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYa0CCzgcffKB77rlHSUlJGj58uG666SY1NjY6/bZta/Xq1fJ4PIqJidGkSZO0f//+kGsEg0EtXbpUycnJio2NVUFBgY4ePRpS4/f75fV6ZVmWLMuS1+vVqVOnBmJKAABgCAp70PH7/br11ls1bNgwvfTSS3r77bf12GOP6aqrrnJq1q1bpw0bNqiiokJ79+6V2+3W1KlTdfr0aaemuLhYVVVVqqysVH19vTo7O5Wfn6/e3l6nprCwUD6fT9XV1aqurpbP55PX6w33lAAAwBDlsm3bDucFV65cqf/+7//W73//+wv227Ytj8ej4uJirVixQtInuzepqalau3atFi5cqEAgoBEjRmjLli2aM2eOJOnYsWNKS0vTjh07NG3aNB04cEAZGRlqaGhQTk6OJKmhoUG5ubk6ePCgRo8e/alj7ejokGVZCgQCio+PD9MKDG3Xrdw+2EPAZer9NTMGewgAIKl/n99h39F58cUXdfPNN+tf/uVflJKSonHjxunpp592+pubm9XW1qa8vDynLTo6WhMnTtTOnTslSY2Njerp6Qmp8Xg8yszMdGp27doly7KckCNJ48ePl2VZTs35gsGgOjo6Qg4AAGCusAedP/3pT3riiSeUnp6ul19+Wd/+9re1bNky/eIXv5AktbW1SZJSU1NDzktNTXX62traFBUVpYSEhIvWpKSk9Hn/lJQUp+Z85eXlzv08lmUpLS3t800WAABc1sIedM6ePau//du/VVlZmcaNG6eFCxeqqKhITzzxREidy+UKeW3bdp+2851fc6H6i11n1apVCgQCztHS0nKp0wIAAENQ2IPO1VdfrYyMjJC2MWPG6MiRI5Ikt9stSX12Xdrb251dHrfbre7ubvn9/ovWHD9+vM/7nzhxos9u0TnR0dGKj48POQAAgLnCHnRuvfVWHTp0KKTtnXfe0bXXXitJGjVqlNxut2pra53+7u5u1dXVacKECZKk7OxsDRs2LKSmtbVVTU1NTk1ubq4CgYD27Nnj1OzevVuBQMCpAQAAX26R4b7gd77zHU2YMEFlZWWaPXu29uzZo6eeekpPPfWUpE9+3FRcXKyysjKlp6crPT1dZWVlGj58uAoLCyVJlmVp/vz5KikpUVJSkhITE1VaWqqsrCxNmTJF0ie7RNOnT1dRUZE2bdokSVqwYIHy8/Mv6RtXAADAfGEPOrfccouqqqq0atUqPfzwwxo1apQef/xxzZ0716l54IEH1NXVpUWLFsnv9ysnJ0c1NTWKi4tzajZu3KjIyEjNnj1bXV1dmjx5sjZv3qyIiAinZuvWrVq2bJnz7ayCggJVVFSEe0oAAGCICvtzdIYSnqPTF8/RwV/Dc3QAXC4G9Tk6AAAAlwuCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAw1oAHnfLycrlcLhUXFztttm1r9erV8ng8iomJ0aRJk7R///6Q84LBoJYuXark5GTFxsaqoKBAR48eDanx+/3yer2yLEuWZcnr9erUqVMDPSUAADBEDGjQ2bt3r5566indeOONIe3r1q3Thg0bVFFRob1798rtdmvq1Kk6ffq0U1NcXKyqqipVVlaqvr5enZ2dys/PV29vr1NTWFgon8+n6upqVVdXy+fzyev1DuSUAADAEDJgQaezs1Nz587V008/rYSEBKfdtm09/vjj+u53v6u77rpLmZmZ+vnPf64PP/xQzz33nCQpEAjomWee0WOPPaYpU6Zo3LhxevbZZ7Vv3z698sorkqQDBw6ourpaP/3pT5Wbm6vc3Fw9/fTT+u1vf6tDhw4N1LQAAMAQMmBBZ/HixZoxY4amTJkS0t7c3Ky2tjbl5eU5bdHR0Zo4caJ27twpSWpsbFRPT09IjcfjUWZmplOza9cuWZalnJwcp2b8+PGyLMupAQAAX26RA3HRyspKvfnmm9q7d2+fvra2NklSampqSHtqaqoOHz7s1ERFRYXsBJ2rOXd+W1ubUlJS+lw/JSXFqTlfMBhUMBh0Xnd0dPRjVgAAYKgJ+45OS0uL7rvvPj377LO68sor/2qdy+UKeW3bdp+2851fc6H6i12nvLzcuXHZsiylpaVd9P0AAMDQFvag09jYqPb2dmVnZysyMlKRkZGqq6vTj370I0VGRjo7OefvurS3tzt9brdb3d3d8vv9F605fvx4n/c/ceJEn92ic1atWqVAIOAcLS0tn3u+AADg8hX2oDN58mTt27dPPp/POW6++WbNnTtXPp9P119/vdxut2pra51zuru7VVdXpwkTJkiSsrOzNWzYsJCa1tZWNTU1OTW5ubkKBALas2ePU7N7924FAgGn5nzR0dGKj48POQAAgLnCfo9OXFycMjMzQ9piY2OVlJTktBcXF6usrEzp6elKT09XWVmZhg8frsLCQkmSZVmaP3++SkpKlJSUpMTERJWWliorK8u5uXnMmDGaPn26ioqKtGnTJknSggULlJ+fr9GjR4d7WgAAYAgakJuRP80DDzygrq4uLVq0SH6/Xzk5OaqpqVFcXJxTs3HjRkVGRmr27Nnq6urS5MmTtXnzZkVERDg1W7du1bJly5xvZxUUFKiiouILnw8AALg8uWzbtgd7EIOlo6NDlmUpEAjwY6z/57qV2wd7CLhMvb9mxmAPAQAk9e/zm991BQAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWJGDPQAAQ8N1K7cP9hD67f01MwZ7CAAGGTs6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMFfagU15erltuuUVxcXFKSUnRrFmzdOjQoZAa27a1evVqeTwexcTEaNKkSdq/f39ITTAY1NKlS5WcnKzY2FgVFBTo6NGjITV+v19er1eWZcmyLHm9Xp06dSrcUwIAAENU2INOXV2dFi9erIaGBtXW1urjjz9WXl6ezpw549SsW7dOGzZsUEVFhfbu3Su3262pU6fq9OnTTk1xcbGqqqpUWVmp+vp6dXZ2Kj8/X729vU5NYWGhfD6fqqurVV1dLZ/PJ6/XG+4pAQCAIcpl27Y9kG9w4sQJpaSkqK6uTrfffrts25bH41FxcbFWrFgh6ZPdm9TUVK1du1YLFy5UIBDQiBEjtGXLFs2ZM0eSdOzYMaWlpWnHjh2aNm2aDhw4oIyMDDU0NCgnJ0eS1NDQoNzcXB08eFCjR4/+1LF1dHTIsiwFAgHFx8cP3CIMIUPxWSnAX8NzdAAz9efze8Dv0QkEApKkxMRESVJzc7Pa2tqUl5fn1ERHR2vixInauXOnJKmxsVE9PT0hNR6PR5mZmU7Nrl27ZFmWE3Ikafz48bIsy6k5XzAYVEdHR8gBAADMNaBBx7ZtLV++XLfddpsyMzMlSW1tbZKk1NTUkNrU1FSnr62tTVFRUUpISLhoTUpKSp/3TElJcWrOV15e7tzPY1mW0tLSPt8EAQDAZW1Ag86SJUv01ltv6Ze//GWfPpfLFfLatu0+bec7v+ZC9Re7zqpVqxQIBJyjpaXlUqYBAACGqAELOkuXLtWLL76oV199Vddcc43T7na7JanPrkt7e7uzy+N2u9Xd3S2/33/RmuPHj/d53xMnTvTZLTonOjpa8fHxIQcAADBX2IOObdtasmSJXnjhBf3ud7/TqFGjQvpHjRolt9ut2tpap627u1t1dXWaMGGCJCk7O1vDhg0LqWltbVVTU5NTk5ubq0AgoD179jg1u3fvViAQcGoAAMCXW9h/e/nixYv13HPP6de//rXi4uKcnRvLshQTEyOXy6Xi4mKVlZUpPT1d6enpKisr0/Dhw1VYWOjUzp8/XyUlJUpKSlJiYqJKS0uVlZWlKVOmSJLGjBmj6dOnq6ioSJs2bZIkLViwQPn5+Zf0jSsAAGC+sAedJ554QpI0adKkkPaf/exnuvfeeyVJDzzwgLq6urRo0SL5/X7l5OSopqZGcXFxTv3GjRsVGRmp2bNnq6urS5MnT9bmzZsVERHh1GzdulXLli1zvp1VUFCgioqKcE8JAAAMUQP+HJ3LGc/R6Yvn6MAkPEcHMNNl9RwdAACAwULQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGCtysAdgsutWbh/sIQBfakPx/8H318wY7CEARmFHBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYa8g/GfknP/mJ1q9fr9bWVt1www16/PHH9Y1vfGOwhwUAnwlPcwbCa0jv6PzqV79ScXGxvvvd7+oPf/iDvvGNb+jOO+/UkSNHBntoAADgMjCkg86GDRs0f/58/fu//7vGjBmjxx9/XGlpaXriiScGe2gAAOAyMGR/dNXd3a3GxkatXLkypD0vL087d+684DnBYFDBYNB5HQgEJEkdHR0DMsazwQ8H5LoAcDkZ+Z3/M9hD6Lem708b7CHgczj3uW3b9qfWDtmg87//+7/q7e1VampqSHtqaqra2toueE55ebm+//3v92lPS0sbkDECAC5P1uODPQKEw+nTp2VZ1kVrhmzQOcflcoW8tm27T9s5q1at0vLly53XZ8+e1cmTJ5WUlPRXzxkKOjo6lJaWppaWFsXHxw/2cIzHen+xWO8vFuv9xWK9PxvbtnX69Gl5PJ5PrR2yQSc5OVkRERF9dm/a29v77PKcEx0drejo6JC2q666aqCG+IWLj4/nf5QvEOv9xWK9v1is9xeL9e6/T9vJOWfI3owcFRWl7Oxs1dbWhrTX1tZqwoQJgzQqAABwORmyOzqStHz5cnm9Xt18883Kzc3VU089pSNHjujb3/72YA8NAABcBoZ00JkzZ47+/Oc/6+GHH1Zra6syMzO1Y8cOXXvttYM9tC9UdHS0HnrooT4/lsPAYL2/WKz3F4v1/mKx3gPPZV/Kd7MAAACGoCF7jw4AAMCnIegAAABjEXQAAICxCDoAAMBYBJ0h5PXXX9fMmTPl8Xjkcrm0bdu2kH7btrV69Wp5PB7FxMRo0qRJ2r9//+AMdogrLy/XLbfcori4OKWkpGjWrFk6dOhQSA3rHT5PPPGEbrzxRuehabm5uXrppZecftZ6YJWXl8vlcqm4uNhpY83Da/Xq1XK5XCGH2+12+lnvgUPQGULOnDmjsWPHqqKi4oL969at04YNG1RRUaG9e/fK7XZr6tSpOn369Bc80qGvrq5OixcvVkNDg2pra/Xxxx8rLy9PZ86ccWpY7/C55pprtGbNGr3xxht644039Pd///f65je/6fxFz1oPnL179+qpp57SjTfeGNLOmoffDTfcoNbWVufYt2+f08d6DyAbQ5Iku6qqynl99uxZ2+1222vWrHHaPvroI9uyLPvJJ58chBGapb293ZZk19XV2bbNen8REhIS7J/+9Kes9QA6ffq0nZ6ebtfW1toTJ06077vvPtu2+fM9EB566CF77NixF+xjvQcWOzqGaG5uVltbm/Ly8py26OhoTZw4UTt37hzEkZkhEAhIkhITEyWx3gOpt7dXlZWVOnPmjHJzc1nrAbR48WLNmDFDU6ZMCWlnzQfGu+++K4/Ho1GjRunuu+/Wn/70J0ms90Ab0k9Gxv937pebnv8LTVNTU3X48OHBGJIxbNvW8uXLddtttykzM1MS6z0Q9u3bp9zcXH300Uf6yle+oqqqKmVkZDh/0bPW4VVZWak333xTe/fu7dPHn+/wy8nJ0S9+8Qt97Wtf0/Hjx/WDH/xAEyZM0P79+1nvAUbQMYzL5Qp5bdt2nzb0z5IlS/TWW2+pvr6+Tx/rHT6jR4+Wz+fTqVOn9Pzzz2vevHmqq6tz+lnr8GlpadF9992nmpoaXXnllX+1jjUPnzvvvNP576ysLOXm5upv/uZv9POf/1zjx4+XxHoPFH50ZYhzd++f+5fBOe3t7X3+lYBLt3TpUr344ot69dVXdc011zjtrHf4RUVF6atf/apuvvlmlZeXa+zYsfrhD3/IWg+AxsZGtbe3Kzs7W5GRkYqMjFRdXZ1+9KMfKTIy0llX1nzgxMbGKisrS++++y5/xgcYQccQo0aNktvtVm1trdPW3d2turo6TZgwYRBHNjTZtq0lS5bohRde0O9+9zuNGjUqpJ/1Hni2bSsYDLLWA2Dy5Mnat2+ffD6fc9x8882aO3eufD6frr/+etZ8gAWDQR04cEBXX301f8YHGD+6GkI6Ozv13nvvOa+bm5vl8/mUmJiokSNHqri4WGVlZUpPT1d6errKyso0fPhwFRYWDuKoh6bFixfrueee069//WvFxcU5/9KyLEsxMTHOM0dY7/D4j//4D915551KS0vT6dOnVVlZqddee03V1dWs9QCIi4tz7jc7JzY2VklJSU47ax5epaWlmjlzpkaOHKn29nb94Ac/UEdHh+bNm8ef8YE2eF/4Qn+9+uqrtqQ+x7x582zb/uQrig899JDtdrvt6Oho+/bbb7f37ds3uIMeoi60zpLsn/3sZ04N6x0+3/rWt+xrr73WjoqKskeMGGFPnjzZrqmpcfpZ64H3l18vt23WPNzmzJljX3311fawYcNsj8dj33XXXfb+/fudftZ74Lhs27YHKWMBAAAMKO7RAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBY/xfVSgdKsAhJ7gAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "# your code here\n", + "plt.hist(vehicles[\"Combined MPG\"])\n", + "plt.show()" ] }, { @@ -206,7 +519,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -237,11 +550,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "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", + "y = np.random.exponential(scale=1, size= 1000)\n" ] }, { @@ -253,11 +569,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(array([565., 238., 116., 52., 10., 12., 3., 0., 1., 3.]),\n", + " array([3.16324521e-04, 8.09825840e-01, 1.61933536e+00, 2.42884487e+00,\n", + " 3.23835439e+00, 4.04786390e+00, 4.85737342e+00, 5.66688293e+00,\n", + " 6.47639245e+00, 7.28590196e+00, 8.09541148e+00]),\n", + " )" + ] + }, + "execution_count": 28, + "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)" ] }, { @@ -273,12 +617,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7534030360583935\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", + "exp_dist = expon(scale = lambda_inv)\n", + "x = exp_dist.cdf(14)\n", + "print(x)" ] }, { @@ -290,11 +646,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.2231301601484298" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your answer here\n" + "# your answer here\n", + "1-exp_dist.cdf(15)" ] } ], @@ -314,7 +682,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.10.9" } }, "nbformat": 4, diff --git a/your-code/discrete.ipynb b/your-code/discrete.ipynb index 310a3c6..fe1022a 100644 --- a/your-code/discrete.ipynb +++ b/your-code/discrete.ipynb @@ -17,6 +17,27 @@ "- Round the final answer to three decimal places." ] }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import math\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from scipy.stats import bernoulli\n", + "from scipy.stats import binom\n", + "from scipy.stats import geom\n", + "from scipy.stats import poisson\n", + "\n", + "from scipy.stats import uniform\n", + "from scipy.stats import expon\n", + "from scipy.stats import norm" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -33,9 +54,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability apple: 0.6\n", + "Probability orange: 0.4\n" + ] + } + ], "source": [ "\"\"\"\n", "Calculate:\n", @@ -43,7 +73,14 @@ "q = probability that the fruit is an orange\n", "\"\"\"\n", "\n", - "# your code here\n" + "p = 60\n", + "q = 40\n", + "n_total = 100\n", + "p_apple = p / n_total\n", + "p_orange = q / n_total\n", + "\n", + "print(\"Probability apple:\", p_apple)\n", + "print(\"Probability orange:\", p_orange)\n" ] }, { @@ -61,11 +98,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.07775999999999998\n" + ] + }, + { + "data": { + "text/plain": [ + "8.349416423424006e-08" + ] + }, + "execution_count": 3, + "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" ] }, { @@ -83,11 +143,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0012944935222876583\n" + ] + } + ], "source": [ - "# your code here\n" + "# your code here\n", + "p = 0.6\n", + "n = 20\n", + "\n", + "binomial_dist = binom(n,p)\n", + "print(binomial_dist.pmf(5))" ] }, { @@ -101,11 +174,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.00031703112116863004\n" + ] + } + ], "source": [ - "# your code here\n" + "# your code here\n", + "print(binomial_dist.cdf(4))\n" ] }, { @@ -119,12 +201,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'PDF')" + ] + }, + "execution_count": 8, + "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\")" ] }, { @@ -151,11 +259,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "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))" ] }, { @@ -167,12 +288,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "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(0,10)\n", + "plt.plot(x, poisson_dist.pmf(x),\"o\")\n", + "plt.show()" ] } ], @@ -192,7 +327,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.10.9" } }, "nbformat": 4,