From 92feae28e45ca5aab3a03194345f6b679edf7f6f Mon Sep 17 00:00:00 2001 From: AnaCarvalho84 <131803922+AnaCarvalho84@users.noreply.github.com> Date: Fri, 11 Aug 2023 22:29:11 +0100 Subject: [PATCH] lab done --- your-code/continuous.ipynb | 474 ++++++++++++++++++++++++++++++++++--- your-code/discrete.ipynb | 170 +++++++++++-- 2 files changed, 589 insertions(+), 55 deletions(-) diff --git a/your-code/continuous.ipynb b/your-code/continuous.ipynb index 32218e9..9b2531d 100644 --- a/your-code/continuous.ipynb +++ b/your-code/continuous.ipynb @@ -35,11 +35,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2.88052818 2.35183287 2.47060075 2.94832323 2.63836492 2.04765872\n", + " 2.64411319 2.44797221 2.43678353 2.77880428]\n" + ] + } + ], "source": [ "from scipy.stats import uniform\n", + "from numpy import random\n", + "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\n", + "\n", "x = uniform.rvs(size=10)\n", "a = 2\n", "b = 3\n", @@ -73,11 +97,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "# your code here\n" + "#1.\n", + "def generate_uniform_randoms(bottom, ceiling, count):\n", + " x = np.random.uniform(size=count)\n", + " randoms = bottom + (ceiling - bottom) * x\n", + " return randoms\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#2. Parameter sets\n", + "params_set_1 = (10, 15, 100)\n", + "params_set_2 = (10, 60, 1000)\n", + "\n", + "\n", + "randoms_set_1 = generate_uniform_randoms(*params_set_1)\n", + "randoms_set_2 = generate_uniform_randoms(*params_set_2)\n", + "\n", + "#3. plot uniform distributions\n", + "\n", + "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10,5), sharey=True)\n", + "\n", + "ax[0].hist(randoms_set_1, bins=10)\n", + "ax[1].hist(randoms_set_2, bins=10)\n", + "plt.show()" ] }, { @@ -88,12 +150,27 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# your answer here:\n" + "***My Insights***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- The two distributions are different due to variations in the parameters used to generate the random numbers.\n", + "\n", + "- Range of Values: In Distribution 1, the range of values is limited between 10 and 15, which means all generated random numbers will fall within this range. In Distribution 2, the range is wider, spanning from 10 to 60.\n", + "\n", + "- Number of Samples: Distribution 1 generates 100 random numbers, while Distribution 2 generates 1000. This means Distribution 2 will have a larger sample size to draw from, potentially leading to a smoother and more accurate representation of the uniform distribution.\n", + "\n", + "- Distribution Shape: Distribution 1's histogram will likely show a more concentrated distribution of values between 10 and 15, since that's the specified range. Distribution 2's histogram will show a spread of values across the wider range from 10 to 60.\n", + "\n", + "- Frequency: Due to the larger sample size in Distribution 2, the histogram bars may be taller and more evenly distributed, reflecting the larger number of random numbers generated within the wider range.\n", + "\n", + "The main differences between the two distributions are the range of values and the number of random numbers generated, which will influence the shape and spread of the histograms when visualized." ] }, { @@ -114,11 +191,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "def generate_normal_randoms(mean, std_dev, count):\n", + " return np.random.normal(mean, std_dev, count)\n", + "\n", + "mean_1 = 10\n", + "std_dev_1 = 1\n", + "count = 1000\n", + "\n", + "mean_2 = 10\n", + "std_dev_2 = 50\n", + "\n", + "randoms_1 = generate_normal_randoms(mean_1, std_dev_1, count)\n", + "randoms_2 = generate_normal_randoms(mean_2, std_dev_2, count)\n", + "\n", + "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(8, 4), sharey=True)\n", + "ax[0].hist(randoms_1, bins=30, label=f\"Mean = {mean_1}, Std Dev = {std_dev_1}\")\n", + "ax[1].hist(randoms_2, bins=30, label=f\"Mean = {mean_2}, Std Dev = {std_dev_2}\")\n", + "\n", + "ax[0].set_title(\"Normal Distribution 1\")\n", + "ax[1].set_title(\"Normal Distribution 2\")\n", + "\n", + "ax[0].set_xticks([8, 10, 12]) \n", + "ax[1].set_xticks([-100, 0, 100])\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" ] }, { @@ -130,11 +242,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ - "# your answer here:\n" + "# your answer here:\n", + "#The first distribution will appear narrower and more centered around the mean, while the second distribution will be wider and potentially show signs of skewness." ] }, { @@ -158,11 +271,157 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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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": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here\n" + "vehicles = pd.read_csv(\"vehicles.csv\")\n", + "vehicles.head(3)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(vehicles['Fuel Barrels/Year'])\n", + "plt.show()" ] }, { @@ -174,11 +433,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "plt.hist(vehicles['CO2 Emission Grams/Mile'])\n", + "plt.show()\n" ] }, { @@ -190,11 +461,60 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "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" + "plt.hist(vehicles['Combined MPG'])\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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U/+abbxqSjPj4+HxjLl++bOYR+Tlx4oTh7+9vNG7c2MjKyrrp99asWTPffOX1118343Lyj6lTp9p8/v777zckGatWrTLbMjMzjTvuuMPo1q2b2Xb48GFDkrFgwQKzrWLFikZMTEy+fdu1a5chyfjss89uegx/zunGjBljSDK2b99uE/f8888bFovFzJNy+hQWFmZcvXrVjNuxY4chyVi+fPkNv9fd3d1o1qxZrvasrCwjMzPTfP35PNwoj7ve1atXjQsXLhheXl7GP/7xD7M9J/8dMmSITXzXrl0NSca0adNs2u+//37jgQceMN+/+OKLRuXKlW/6/dOnTzd8fHzMnNowruVaXl5e5rEEBAQYmZmZxtmzZw03Nzdj4cKFxpkzZ3Llkxs3bjQkGRs3bsw1FjmOHTtmODs75zqu8+fPGwEBAUb37t1v2mcAAEpSQfO9gwcPGpKMwYMH27Rv377dkGS8/PLLZlvLli0NScauXbvMtrNnzxpOTk6Gh4eHcfLkSbM9MTHRkGS8++67ZlvO7+xLL71k811Lly41JBlLliyx+a6WLVua7wuSI9WrV89o2LChTb5gGIYRGRlpVKtWzcyDnnrqKcPDw8NITk42Y65evWrUq1fPkGQcPnz4hmOXczxnzpwxFixYYLi5uRlnz541rl69alSrVs2IjY01DMMwvLy8bPLCnGOZMmWKkZmZaVy5csXYvXu38eCDDxqSjHXr1hmGYRjt27c3JJlzVjly5qByXn8eDwA3x1KfAIrko48+0s6dO21eBb3j74svvtCjjz6qwMBAXb161Xw9/vjjkqRNmzYVqm/du3c3+/Tdd9/p3Xff1a5du9S+fXulp6ebcadPn9Zzzz2noKAgOTs7y8XFRTVr1pQkHTx4MNd+87vS/95771WdOnXsdmxNmjTRl19+qTFjxujbb7/V5cuXc8VcvHhRX331VZ59cnJy0vjx4yVJL7/8srn0xRdffKHKlSurU6dONn26//77FRAQYLMU1G+//aaoqCgFBATIyclJLi4uatmyZYHHBgAAR3D9XXg5/vjjD3Xo0EGGYWjlypW5lmfMz0MPPZQrj9q5c6fN89tyREZG2ryvX7++LBaLmVNI11ZduPvuu3MtUXm9Jk2aaOHChZo4caK2bduWa6mnu+++Wz4+Pho9erTef//9XHek5eebb77RPffcYy55mqNPnz4yDCPXagEdO3a0WX793nvvlaSb9j8/3bp1k4uLi/kaOnRorpi8cpULFy6Yd0o6OzvL2dlZFStW1MWLF/PMd/I6FznHc337n4+lSZMmOnfunJ5++ml9/vnnuZaVz/Hpp5+qS5cu+ebUffv21alTp/Tll19q6dKlcnV11ZNPPpln7K346quvdPXqVfXq1csmN3R3d1fLli1vaZlQAADKso0bN0pSriXKmzRpovr16+vrr7+2aa9WrZoaNWpkvvf19ZWfn5/uv/9+mzv7cnKAvHKXZ555xuZ99+7d5ezsbPblRm6WI/3yyy/6z3/+Y37Hn3+/O3TooKSkJHNZ8I0bN6p169by9/c39+fk5JTnEvc38+STT8rV1VVLly7Vv/71LyUnJ+ca0+uNHj1aLi4ucnd3V6NGjXTs2DHNmTNHHTp0uOHn/vGPf9jkdX9ewh3AzbHUJ4AiqV+/fqGWWfqzU6dOae3atfkudZDfpMjN3HHHHTZ9e/jhh3XHHXfo6aef1sKFCzVo0CBlZ2crIiJCv//+u1599VWFhYXJy8tL2dnZatasWZ7FtmrVquX5fXm1F+XY3n33XVWvXl0rV67UlClT5O7urnbt2umtt94yl3Jat26dMjMz810SIafY5+rqatOnc+fO2bTl1acLFy7o4Ycflru7uyZOnKg6derI09PTXKf++rHx9PTM9zk7AACUlKpVq8rT01OHDx++pfgaNWpI0g3jc7bltZRUSkqK2rZtq5MnT+qbb75RrVq1brmvVqv1lvMoX19fm/eurq7y9PSUu7t7rva0tLQb7mvlypWaOHGiPvjgA7366quqWLGinnjiCcXFxSkgIEBWq1WbNm3SG2+8oZdfflkpKSmqVq2aBgwYoFdeeSXfvObs2bMKDg7O1Z4zQXb9cuXXPycxJ2/JK//6sxo1auQ5wTZ16lS98sorkqQHH3wwz8/mla9FRUXp66+/1quvvqoHH3xQlSpVksViUYcOHfLsS17nIr/2K1eumO+jo6N19epVzZs3T3/5y1+UnZ2tBx98UBMnTlTbtm0lXVta7N///rdGjRqV7/HXrFlTrVu31ocffqgjR46oR48e8vT01KVLl/L9zI2cOnVKUv5jdquFbAAASkpB872cHCSvPCAwMDBXXnH9b7p07Xc9vxzgz7/3OQICAmzeOzs7q0qVKrnyobzcLEfK+e0eOXKkRo4cmec+cuZ2zp49m6svefXvVnh5eempp57Shx9+qJo1a5pLkN/IsGHD1LNnT1WoUEGVK1dWSEiIzcV0Obn40aNHbS6mj4qK0kMPPSRJGjRokM0F/ABujsIfgGLj5uaW5w/z9UlO1apVde+99+qNN97Icz/Xr5NeFDlXSX3//feSpH379un777/XwoUL1bt3bzMuZ83xvOR3tX9e7UU5Ni8vL02YMEETJkwwr+oeM2aMOnXqpP/85z+Srl0R/thjj8nHxyff/eTVpypVquT7AGpvb29J167a//333/Xtt9+ad/lJ0rlz5/L8XH7jAgBASXJyclLr1q315Zdf6sSJE6pevfoN4x999FE5Ozvrs88+03PPPZdnzGeffSZJZnEmR0pKitq0aaPDhw/r66+/NvOMsq5q1aqaPn26pk+frmPHjmnNmjUaM2aMTp8+beYHYWFhWrFihQzD0A8//KCFCxfqtddek4eHh8aMGZPnfqtUqaKkpKRc7b///rv5vfbQtm1bvffee9q1a5dN4fSuu+666Wevz1dSU1P1xRdfaPz48TbHlZ6erj/++MMu/f2zvn37qm/fvrp48aK+++47jR8/XpGRkfrpp59Us2ZNrV69Wl5eXrn+rF2vX79+6tmzp7KzszV79uwi9SnnvHzyySc3nbwDAKAsKGi+l1NIS0pKyhX7+++/2y1H+bPk5GTdeeed5vurV6/q7NmzuYp6hZHT37Fjx+b7XOe6detKunbsycnJefavMPr166cPPvhAP/zwg5YuXXrT+OrVq9/wQre2bdtq7ty5WrNmjU0R08/PT35+fpKuzVNR+AMKhkv3ABSb4OBg/fDDDzZt33zzjS5cuGDTFhkZqX379umuu+5S48aNc73sWfhLTEyUJDN5yJn8ybl6KsecOXPs8n32OjZ/f3/16dNHTz/9tA4dOqRLly7pypUr+te//lXg5TUjIyN19uxZZWVl5dmnnOSwuMcGAIDiMnbsWBmGoQEDBigjIyPX9szMTK1du1bStaud+/Xrp6+++korV67MFfvTTz9pypQpatCggbp27Wq25xT9fvvtN61fv14NGzYstuMpTjVq1NCLL76otm3bas+ePbm2WywW3XfffXrnnXdUuXLlPGNytG7dWgcOHMgV89FHH8lisejRRx+1S59feukleXp66oUXXtD58+eLtC+LxSLDMHLlOx988IGysrKKtO8b8fLy0uOPP65x48YpIyND+/fvl3Ttoq7IyMhc/bneE088oSeeeEL9+vVTs2bNitSXdu3aydnZWb/++mueuWFRV/cAAKA4FCTfe+yxxyRJS5YssYnZuXOnDh48qNatW9u9f9cXxT7++GNdvXpVrVq1KvK+69atq9q1a+v777/P97c756LuRx99VF9//bV5l6AkZWVl5Zn33orw8HD169fPzEWK6oknntA999yjSZMmmRe5Ayg67vgDUGyio6P16quv6u9//7tatmypAwcOaObMmbJarTZxr732mhISEtS8eXMNHTpUdevW1ZUrV3TkyBH961//0vvvv3/Tq7fycurUKW3btk3StWUXEhMTNXHiRFWuXFl9+/aVJNWrV0933XWXxowZI8Mw5Ovrq7Vr1yohIaHoA1DEY2vatKkiIyN17733ysfHRwcPHtTixYsVHh4uT09PffbZZ7p06ZLNJOSt6NGjh5YuXaoOHTpo2LBhatKkiVxcXHTixAlt3LhRXbp00RNPPKHmzZvLx8dHzz33nMaPHy8XFxctXbrUvFsSAICyKjw8XLNnz9bgwYPVqFEjPf/882rQoIEyMzO1d+9ezZ07V6GhoerUqZMkadq0aTp06JB69uyp7777Tp06dZKbm5u2bdumt99+W97e3vr000/NZ61cvnxZ7dq10969ezV9+nRdvXrVzDmka8uN38rdZ+fOnbP5XA43N7diKySmpqbq0UcfVVRUlOrVqydvb2/t3LlT8fHx5hXjX3zxhWbNmqWuXbuqVq1aMgxDq1at0rlz5254J9pLL72kjz76SB07dtRrr72mmjVrat26dZo1a5aef/75XM9CLqy77rpLy5cv19NPP62wsDA9//zzeuCBB+Tm5qbTp09r/fr1knRLS5BXqlRJjzzyiN566y1VrVpVwcHB2rRpk+bPn6/KlSvbpb85BgwYIA8PD7Vo0ULVqlVTcnKyJk+eLKvVqgcffFBnz57Vpk2btGLFipvuy93dXZ988old+hUcHKzXXntN48aN02+//ab27dvLx8dHp06d0o4dO8xVKAAAKEsKku/VrVtXAwcO1IwZM1ShQgU9/vjjOnLkiF599VUFBQXppZdesnv/Vq1aJWdnZ7Vt21b79+/Xq6++qvvuu0/du3e3y/7nzJmjxx9/XO3atVOfPn1055136o8//tDBgwe1Z88e/fOf/5QkvfLKK1qzZo0ee+wx/f3vf5enp6fee+89Xbx4sdDfPX/+fLscg3Tt7s3PPvtM7dq1U5MmTTRgwAC1atVKPj4+OnfunLZv367vv//efJ4igFtD4Q9Asfnb3/6mtLQ0LVy4UG+//baaNGmijz/+WF26dLGJq1atmnbt2qXXX39db731lk6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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 3, figsize=(18, 4))\n", + "axs[0].hist(vehicles['Fuel Barrels/Year'], bins=20)\n", + "axs[0].set_title('Fuel Barrels/Year')\n", + "axs[0].set_xlabel('Fuel Barrels/Year')\n", + "axs[0].set_ylabel('Frequency')\n", + "\n", + "axs[1].hist(vehicles['CO2 Emission Grams/Mile'], bins=20)\n", + "axs[1].set_title('CO2 Emission Grams/Mile')\n", + "axs[1].set_xlabel('CO2 Emission Grams/Mile')\n", + "axs[1].set_ylabel('Frequency')\n", + "\n", + "axs[2].hist(vehicles['Combined MPG'], bins=20)\n", + "axs[2].set_title('Combined MPG')\n", + "axs[2].set_xlabel('Combined MPG')\n", + "axs[2].set_ylabel('Frequency')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { @@ -206,11 +526,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fuel Barrels/Year - Skewness: 0.6382445784906976\n", + "CO2 Emission Grams/Mile - Skewness: 0.7416608937445333\n", + "Combined MPG - Skewness: 1.0677281511016457\n" + ] + } + ], "source": [ - "# you answer here:\n" + "# you answer here:\n", + "from scipy.stats import skew\n", + "for variable in ['Fuel Barrels/Year', 'CO2 Emission Grams/Mile', 'Combined MPG']:\n", + " data = vehicles[variable]\n", + " skewness = skew(data)\n", + " print(f'{variable} - Skewness: {skewness}')\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "***My answer***\n", + "- Skewness measures the lack of symmetry relative to the mean of a distribution.\n", + "- If the skewness is close to 0, this suggests that the distribution is symmetric (bell-shaped) and closer to a normal distribution. \n", + "- Among the three variables, 'Fuel Barrels/Year' has the skewness coefficient closest to 0 (0.638). This suggests that its distribution is relatively closer to a symmetric bell-shaped normal distribution compared to the other two variables." ] }, { @@ -237,11 +583,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "def generate_exponential_numbers(mean, size):\n", + " scale = 1 / mean\n", + " exponential_numbers = np.random.exponential(scale=scale, size=size)\n", + " return exponential_numbers\n", + "\n", + "mean_1 = 1\n", + "mean_100 = 100\n", + "size = 1000\n", + "\n", + "exponential_numbers_mean_1 = generate_exponential_numbers(mean_1, size)\n", + "exponential_numbers_mean_100 = generate_exponential_numbers(mean_100, size)\n", + "\n", + "#Plots\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(exponential_numbers_mean_1, bins=100)\n", + "plt.title('Exponential Distribution (Mean 1)')\n", + "plt.xlabel('Value')\n", + "plt.ylabel('Frequency')\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(exponential_numbers_mean_100, bins=100)\n", + "plt.title('Exponential Distribution (Mean 100)')\n", + "plt.xlabel('Value')\n", + "plt.ylabel('Frequency')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" ] }, { @@ -252,12 +635,14 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# your answer here:\n" + "***My answer***\n", + "- When the mean of the exponential distribution is larger, it results in a distribution with a broader range of values. \n", + "- This means that the data points are more spread out, and the distribution has a longer tail on the right side. \n", + "- Conversely, when the mean is smaller, the distribution is more concentrated around lower values, and the data points are less spread out.\n", + "- When comparing the two exponential distributions with means of 1 and 100, you can observe that the distribution with a mean of 100 has more spread-out values" ] }, { @@ -273,12 +658,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "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", + "from scipy.stats import expon" ] }, { @@ -290,11 +676,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability of spending more than fifteen minutes: 0.2231301601484298\n" + ] + } + ], "source": [ - "# your answer here\n" + "lambda_inv = 10\n", + "exp_d = expon(scale=lambda_inv) \n", + "exp_d.cdf(15)\n", + "probability_more_than_15 = 1 - exp_d.cdf(15) \n", + "print(\"Probability of spending more than fifteen minutes:\", probability_more_than_15)" ] } ], @@ -314,7 +712,7 @@ "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..c5662c6 100644 --- a/your-code/discrete.ipynb +++ b/your-code/discrete.ipynb @@ -33,9 +33,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], + "source": [ + "#libraries\n", + "import pandas as pd\n", + "import numpy as np\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", + "from scipy.stats import uniform\n", + "from scipy.stats import expon\n", + "from scipy.stats import norm\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability that the fruit is an apple: 0.6\n", + "Probability that the fruit is an orange: 0.4\n" + ] + } + ], "source": [ "\"\"\"\n", "Calculate:\n", @@ -43,7 +72,18 @@ "q = probability that the fruit is an orange\n", "\"\"\"\n", "\n", - "# your code here\n" + "total_fruits = 100\n", + "apples = 60\n", + "oranges = 40\n", + "\n", + "p = apples / total_fruits\n", + "q = oranges / total_fruits\n", + "\n", + "p_rounded = round(p, 3)\n", + "q_rounded = round(q, 3)\n", + "\n", + "print(\"Probability that the fruit is an apple:\", p_rounded)\n", + "print(\"Probability that the fruit is an orange:\", q_rounded)\n" ] }, { @@ -61,11 +101,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability that the first 5 fruits are all apples: 0.078\n" + ] + } + ], "source": [ - "# your code here\n" + "total_fruits = 100\n", + "apples = 60\n", + "oranges = 40\n", + "\n", + "p = 0.6\n", + "n = 5 # all first fruits are all apples\n", + "\n", + "binom_dist = binom(n, p)\n", + "probability_all_apples = round(binom_dist.pmf(5), 3) #pmf - finding the probability that exactly 5 apples are picked out of 5 trials\n", + "\n", + "print(\"Probability that the first 5 fruits are all apples:\", probability_all_apples)" ] }, { @@ -83,11 +141,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0012944935222876583\n" + ] + } + ], "source": [ - "# your code here\n" + "p = 0.6\n", + "n = 20\n", + "\n", + "binomial_apple = binom(n,p)\n", + "print(binomial_apple.pmf(5))" ] }, { @@ -103,9 +173,17 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.00031703112116863004\n" + ] + } + ], "source": [ - "# your code here\n" + "print(binomial_apple.cdf(4))\n" ] }, { @@ -121,10 +199,36 @@ "cell_type": "code", "execution_count": null, "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", + "p = 0.6\n", + "n = 20\n", + "\n", + "binomial_apple = binom(n, p)\n", + "\n", + "x = np.arange(0, n+1) \n", + "\n", + "# Calculate the PDF values\n", + "pdf_values = binomial_apple.pmf(x)\n", + "\n", + "plt.plot(x, pdf_values, 'o')\n", + "plt.xlabel('Try s 0 to 20')\n", + "plt.ylabel('Probability of getting apples')\n", + "plt.title('Binomial Distribution of Apples in a Sample of 20 Fruits')\n", + "plt.show()" ] }, { @@ -153,9 +257,23 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.053775025581946814\n" + ] + } + ], "source": [ - "# your code here\n" + "# your code here\n", + "import math\n", + "\n", + "\n", + "mu = 2.3\n", + "poisson_dist = poisson(mu)\n", + "print(poisson_dist.pmf(5))" ] }, { @@ -169,10 +287,28 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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an/38uLq66pZbbtHOnTsdxg0JCbF97opSx+X7u2TJEg0bNkwzZsxQr1691L59e0VERBTq9a5du7Zmz56tI0eO6Ndff9XMmTO1YsWKq57QjdJBAEK50L17d0mX/rBcbvny5Tpz5ozt8Wvx9PRUnz59NHXqVJ0/f9522ezl/Pz8NGLECA0ePFgHDx7U2bNn1bRpU91www1aunSp3aGQM2fOaPny5barQsqD7t2765tvvrEFnjzvv/++qlWr5nAp87Jly+z26ddff9WWLVtsX/QWi0WGYdj+bz3PW2+9VeAhln/+859297ds2aJff/21RBaovNaswahRo+Tm5qb77rtPBw8e1KOPPnrd20xMTNRbb72lqKgo3XzzzYV6TkHvtaLMBBbGihUr7BbIzMrK0urVq9WpUye5urpKunTFYHp6uv773//a+p0/f15ffvmlw3ju7u6Fqq2sPhORkZHy8PBw+Oz/9ttvtsO9xTFlyhTl5ORo9OjRhTrMVJT9zVvA9HKff/55kRd2bdCggR599FH16NFDu3btKtJzcf1YBwjlQo8ePdSrVy9NnjxZmZmZio6O1p49exQXF6e2bdtq6NChBT73wQcflIeHh6KjoxUQEKC0tDTNnDlTPj4+ateunaRLl83269dPrVq1Us2aNXXgwAF98MEHdn/UXnjhBd13333q16+fHn74YZ07d04vvviiTp8+reeff75MXofCiIuL02effaZu3brpmWeeka+vr/75z3/q888/1wsvvCAfHx+7/unp6brjjjv04IMPKiMjQ3FxcbJarZoyZYqkS+u/dO7cWS+++KJq166thg0basOGDXr77bcLXIhw586dGjVqlO6++24dPXpUU6dO1Q033HDNK5oKI+//zOfMmaPhw4eratWqatq0qW3Buxo1amjYsGFauHChgoKCbLN4hZGbm2tb5+fcuXNKSUnRF198oX/9619q3ry5/vWvf131+YV5r4WGhkqSFi1aJC8vL1mtVgUHBxd7dszV1VU9evRQbGyscnNzNWvWLGVmZtqWRJCkQYMG6ZlnntE999yjSZMmKTs7W3Pnzs03wLZs2VLr16/X6tWrFRAQIC8vr3xn0FxcXMrkM1GjRg09/fTTevLJJzVs2DANHjxYJ06cUHx8vKxWq+Li4oo1bnR0tObPn6/HHntMYWFheuihh9SiRQu5uLgoNTVVy5cvlyTb7GtR9rdfv35avHixmjVrplatWikpKUkvvvii3SKa+cnIyFC3bt107733qlmzZvLy8tKOHTu0du1a3XnnncXaT1wHp51+DdMo7JUbf/31lzF58mQjKCjIqFq1qhEQEGCMGTPGOHXqlF2/K6+Wee+994xu3boZfn5+hpubmxEYGGgMHDjQ2LNnj63PE088YURERBg1a9Y03N3djUaNGhkTJ040jh8/bjf2qlWrjA4dOhhWq9Xw9PQ0unfvbnz33Xd2ffKuePnjjz/y3c/8rl663LWuRsoTFBRk9O3bN9/HfvzxRyMmJsbw8fEx3NzcjNatW9tdaWIY/7uq54MPPjDGjRtn1KlTx3B3dzc6depk7Ny5067vb7/9Ztx1111GzZo1DS8vL6N3797G3r17Ha7aytvHdevWGUOHDjVq1Khhu4Ln0KFDDvtZnKvADMMwpkyZYgQGBhouLi75XlG1fv16Q5Lx/PPPF/j6XWn48OGGJNvNw8PDaNCggRETE2O88847xrlz5xyeU5z3mmEYxuzZs43g4GDD1dXVbv+6dOlitGjRIt/6CroKbNasWUZ8fLxRr149w83NzWjbtq3x5ZdfOjx/zZo1Rps2bQwPDw+jUaNGxmuvvZbvVWDJyclGdHS0Ua1aNUOSbZtXXgWWpyw+E4ZhGG+99ZbRqlUrw83NzfDx8TH69+9v7Nu3L9/xrvW35Mr9vf/++43g4GDD3d3dsFqtxo033mgMGzYs36vxCrO/p06dMkaOHGnUrVvXqFatmnHzzTcbmzZtKvDfMO/fPzs72xg9erTRqlUrw9vb2/Dw8DCaNm1qxMXFGWfOnCn0PqFkWAyjEJc+AEA58vjjj2vhwoU6evRoqZ93BKBy4hAYgApj27Zt+ve//60FCxbo4YcfJvwAKDZmgABUGBaLRdWqVdNtt92md99997rX/gFgXswAAagw+P81ACWFy+ABAIDpEIAAAIDpEIAAAIDpcA5QPnJzc3Xs2DF5eXkVedl6AADgHIZhKCsrS4GBgXY/apsfAlA+jh075vBL2wAAoGI4evToNVfmJgDlI2/J/aNHjxb4I5UAAKB8yczMVP369W3f41dDAMpH3mEvb29vAhAAABVMYU5f4SRoAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOqwEjUonJ9fQ9sMnlZ6VrbpeVrUP9pWrCz9qCwD4HwIQKpW1e1MVv3q/UjOybW0BPlbFxYSod2iAEysDAJQnHAJDpbF2b6rGLNllF34kKS0jW2OW7NLavalOqgwAUN4QgFAp5OQail+9X0Y+j+W1xa/er5zc/HoAAMyGAIRKYfvhkw4zP5czJKVmZGv74ZNlVxQAoNwiAKFSSM8qOPwUpx8AoHIjAKFSqOtlLdF+AIDKjQCESqF9sK8CfKwq6GJ3iy5dDdY+2LcsywIAlFMEIFQKri4WxcWESJJDCMq7HxcTwnpAAABJBCBUIr1DA7RwSJj8fewPc/n7WLVwSBjrAAEAbFgIEZVK79AA9QjxZyVoAMBVEYBQ6bi6WBTZuJazywAAlGMcAgMAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKbj9AC0YMECBQcHy2q1Kjw8XJs2bSqw74oVK9SjRw/VqVNH3t7eioyM1JdffmnXZ/HixbJYLA637Ozs0t4VAABQQTg1ACUkJGjChAmaOnWqdu/erU6dOqlPnz5KSUnJt//GjRvVo0cPrVmzRklJSerWrZtiYmK0e/duu37e3t5KTU21u1mt1nzHBAAA5mMxDMNw1sY7dOigsLAwLVy40NbWvHlzDRgwQDNnzizUGC1atNCgQYP0zDPPSLo0AzRhwgSdPn262HVlZmbKx8dHGRkZ8vb2LvY4AACg7BTl+9tpM0Dnz59XUlKSevbsadfes2dPbdmypVBj5ObmKisrS76+vnbtf/75p4KCglSvXj3169fPYYboSufOnVNmZqbdDQAAVF5OC0DHjx9XTk6O/Pz87Nr9/PyUlpZWqDFefvllnTlzRgMHDrS1NWvWTIsXL9ann36qZcuWyWq1Kjo6WocOHSpwnJkzZ8rHx8d2q1+/fvF2CgAAVAhOPwnaYrHY3TcMw6EtP8uWLdO0adOUkJCgunXr2to7duyoIUOGqHXr1urUqZP+9a9/6aabbtK8efMKHGvKlCnKyMiw3Y4ePVr8HQIAAOVeFWdtuHbt2nJ1dXWY7UlPT3eYFbpSQkKCRo4cqY8++ki33nrrVfu6uLioXbt2V50Bcnd3l7u7e+GLBwAAFZrTZoDc3NwUHh6uxMREu/bExERFRUUV+Lxly5ZpxIgRWrp0qfr27XvN7RiGoeTkZAUEBFx3zQAAoHJw2gyQJMXGxmro0KGKiIhQZGSkFi1apJSUFI0ePVrSpUNTv//+u95//31Jl8LPsGHDNGfOHHXs2NE2e+Th4SEfHx9JUnx8vDp27KgmTZooMzNTc+fOVXJysubPn++cnQQAAOWOUwPQoEGDdOLECU2fPl2pqakKDQ3VmjVrFBQUJElKTU21WxPojTfe0MWLFzV27FiNHTvW1j58+HAtXrxYknT69Gk99NBDSktLk4+Pj9q2bauNGzeqffv2ZbpvAACg/HLqOkDlFesAAQBQ8VSIdYAAAACchQAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMp4qzC0D5lpNraPvhk0rPylZdL6vaB/vK1cXi7LIAALguBCAUaO3eVMWv3q/UjGxbW4CPVXExIeodGuDEygAAuD4cAkO+1u5N1Zglu+zCjySlZWRrzJJdWrs31UmVAQBw/QhAcJCTayh+9X4Z+TyW1xa/er9ycvPrAQBA+UcAgoPth086zPxczpCUmpGt7YdPll1RAACUIAIQHKRnFRx+itMPAIDyhgAEB3W9rCXaDwCA8oYABAftg30V4GNVQRe7W3TparD2wb5lWRYAACWGAAQHri4WxcWESJJDCMq7HxcTwnpAAIAKiwCEfPUODdDCIWHy97E/zOXvY9XCIWGsAwQAqNBYCBEF6h0aoB4h/qwEDQCodAhAuCpXF4siG9dydhkAAJQoDoEBAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTqeLsAgBIObmGth8+qfSsbNX1sqp9sK9cXSzOLgsAKi0CEOBka/emKn71fqVmZNvaAnysiosJUe/QACdWBgCVF4fAACdauzdVY5bssgs/kpSWka0xS3Zp7d5UJ1UGAJUbAQhwkpxcQ/Gr98vI57G8tvjV+5WTm18PAMD1IAABTrL98EmHmZ/LGZJSM7K1/fDJsisKAEzC6QFowYIFCg4OltVqVXh4uDZt2lRg3xUrVqhHjx6qU6eOvL29FRkZqS+//NKh3/LlyxUSEiJ3d3eFhIRo5cqVpbkLQLGkZxUcforTDwBQeE4NQAkJCZowYYKmTp2q3bt3q1OnTurTp49SUlLy7b9x40b16NFDa9asUVJSkrp166aYmBjt3r3b1mfr1q0aNGiQhg4dqh9++EFDhw7VwIED9f3335fVbgGFUtfLWqL9AACFZzEMw2knGHTo0EFhYWFauHChra158+YaMGCAZs6cWagxWrRooUGDBumZZ56RJA0aNEiZmZn64osvbH169+6tmjVratmyZYUaMzMzUz4+PsrIyJC3t3cR9ggovJxcQzfP+kZpGdn5ngdkkeTvY9XmybdwSTwAFEJRvr+dNgN0/vx5JSUlqWfPnnbtPXv21JYtWwo1Rm5urrKysuTr62tr27p1q8OYvXr1KvSYQFlxdbEoLiZE0qWwc7m8+3ExIYQfACgFTgtAx48fV05Ojvz8/Oza/fz8lJaWVqgxXn75ZZ05c0YDBw60taWlpRV5zHPnzikzM9PuBpSF3qEBWjgkTP4+9oe5/H2sWjgkjHWAAKCUOH0hRIvF/v9uDcNwaMvPsmXLNG3aNH3yySeqW7fudY05c+ZMxcfHF6FqoOT0Dg1QjxB/VoIGgDLktABUu3Ztubq6OszMpKenO8zgXCkhIUEjR47URx99pFtvvdXuMX9//yKPOWXKFMXGxtruZ2Zmqn79+oXdFeC6ubpYFNm4lrPLAADTcNohMDc3N4WHhysxMdGuPTExUVFRUQU+b9myZRoxYoSWLl2qvn37OjweGRnpMOa6deuuOqa7u7u8vb3tbgAAoPJy6iGw2NhYDR06VBEREYqMjNSiRYuUkpKi0aNHS7o0M/P777/r/fffl3Qp/AwbNkxz5sxRx44dbTM9Hh4e8vHxkSSNHz9enTt31qxZs9S/f3998skn+uqrr7R582bn7CQAACh3nLoO0KBBgzR79mxNnz5dbdq00caNG7VmzRoFBQVJklJTU+3WBHrjjTd08eJFjR07VgEBAbbb+PHjbX2ioqL04Ycf6t1331WrVq20ePFiJSQkqEOHDmW+fwAAoHxy6jpA5RXrAAEAUPFUiHWAAAAAnIUABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATKdYAWjx4sU6e/ZsSdcCAABQJooVgKZMmSJ/f3+NHDlSW7ZsKemaAAAASlWxAtBvv/2mJUuW6NSpU+rWrZuaNWumWbNmKS0traTrAwAAKHHFCkCurq66/fbbtWLFCh09elQPPfSQ/vnPf6pBgwa6/fbb9cknnyg3N7ekawUAACgR130SdN26dRUdHa3IyEi5uLjoxx9/1IgRI9S4cWOtX7++BEoEAAAoWcUOQP/973/10ksvqUWLFuratasyMzP12Wef6fDhwzp27JjuvPNODR8+vCRrBQAAKBEWwzCMoj4pJiZGX375pW666SaNGjVKw4YNk6+vr12fY8eOqV69ehXyUFhmZqZ8fHyUkZEhb29vZ5cDAAAKoSjf31WKs4G6detqw4YNioyMLLBPQECADh8+XJzhAQAASlWxDoF16dJFYWFhDu3nz5/X+++/L0myWCwKCgq65lgLFixQcHCwrFarwsPDtWnTpgL7pqam6t5771XTpk3l4uKiCRMmOPRZvHixLBaLwy07O7vwOwgAACq1YgWg+++/XxkZGQ7tWVlZuv/++ws9TkJCgiZMmKCpU6dq9+7d6tSpk/r06aOUlJR8+587d0516tTR1KlT1bp16wLH9fb2Vmpqqt3NarUWui4AAFC5FSsAGYYhi8Xi0P7bb7/Jx8en0OO88sorGjlypEaNGqXmzZtr9uzZql+/vhYuXJhv/4YNG2rOnDkaNmzYVbdjsVjk7+9vdwMAAMhTpHOA2rZtazuk1L17d1Wp8r+n5+Tk6PDhw+rdu3ehxjp//rySkpL0xBNP2LX37NnzuleX/vPPPxUUFKScnBy1adNGzz77rNq2bVtg/3PnzuncuXO2+5mZmde1fQAAUL4VKQANGDBAkpScnKxevXqpevXqtsfc3NzUsGFD3XXXXYUa6/jx48rJyZGfn59du5+f33WtKN2sWTMtXrxYLVu2VGZmpubMmaPo6Gj98MMPatKkSb7PmTlzpuLj44u9TQAAULEUKQDFxcVJunQoatCgQSVyXs2Vh9IKOrxWWB07dlTHjh1t96OjoxUWFqZ58+Zp7ty5+T5nypQpio2Ntd3PzMxU/fr1i10DAAAo34p1GXxJLHBYu3Ztubq6Osz2pKenO8wKXQ8XFxe1a9dOhw4dKrCPu7u73N3dS2ybAACgfCv0SdC+vr46fvy4JKlmzZry9fUt8FYYbm5uCg8PV2Jiol17YmKioqKiirALV2cYhpKTkxUQEFBiYwIAgIqt0DNAr776qry8vGz/fT2HqfLExsZq6NChioiIUGRkpBYtWqSUlBSNHj1a0qVDU7///rttbSHp0vlH0qUTnf/44w8lJyfLzc1NISEhkqT4+Hh17NhRTZo0UWZmpubOnavk5GTNnz//uusFAACVQ6ED0OWHvUaMGFEiGx80aJBOnDih6dOnKzU1VaGhoVqzZo1tAcXU1FSHNYEuv5orKSlJS5cuVVBQkI4cOSJJOn36tB566CGlpaXJx8dHbdu21caNG9W+ffsSqRkAAFR8hf4tsKJcGl7Rfz+L3wIDAKDiKZXfAqtRo8Y1D3vlXcGVk5NT2GEBAADKXKED0LfffluadQAAAJSZQgegLl26lGYdAAAAZabQAWjPnj0KDQ2Vi4uL9uzZc9W+rVq1uu7CAAAASkuhA1CbNm2UlpamunXrqk2bNrJYLMrv/GnOAQIAAOVdoQPQ4cOHVadOHdt/AwAAVFSFDkB5a/Nc+d8AAAAVTbF+C0ySDh48qHnz5unAgQOyWCxq1qyZHnvsMTVt2rQk6wMAAChxhf4tsMt9/PHHCg0NVVJSklq3bq1WrVpp165dCg0N1UcffVTSNQIAAJSoQq8EfblGjRppyJAhmj59ul17XFycPvjgA/3yyy8lVqAzsBI0AAAVT1G+v4s1A5SWlqZhw4Y5tA8ZMkRpaWnFGRIAAKDMFCsAde3aVZs2bXJo37x5szp16nTdRQEAAJSmQp8E/emnn9r++/bbb9fkyZOVlJSkjh07SpK2bdumjz76SPHx8SVfJQAAQAkq9DlALi6FmyyqDAshcg4QAAAVT6n8Gnxubu51FwYAAFAeFOscIAAAgIqs2AshnjlzRhs2bFBKSorOnz9v99i4ceOuuzAAAIDSUqwAtHv3bt122206e/aszpw5I19fXx0/flzVqlVT3bp1CUAAAKBcK9YhsIkTJyomJkYnT56Uh4eHtm3bpl9//VXh4eF66aWXSrpGAACAElWsAJScnKzHH39crq6ucnV11blz51S/fn298MILevLJJ0u6RgAAgBJVrABUtWpVWSwWSZKfn59SUlIkST4+Prb/BgAAKK+KdQ5Q27ZttXPnTt10003q1q2bnnnmGR0/flwffPCBWrZsWdI1AgAAlKhizQDNmDFDAQEBkqRnn31WtWrV0pgxY5Senq5FixaVaIEAAAAlrVi/Bl/ZsRI0AAAVT6msBJ2f9PR0HTx4UBaLRU2bNlWdOnWuZzgAAIAyUaxDYJmZmRo6dKhuuOEGdenSRZ07d1ZgYKCGDBmijIyMkq4RAACgRBUrAI0aNUrff/+9PvvsM50+fVoZGRn67LPPtHPnTj344IMlXSMAAECJKtY5QJ6envryyy91880327Vv2rRJvXv31pkzZ0qsQGfgHCAAACqeonx/F2sGqFatWvLx8XFo9/HxUc2aNYszJAAAQJkpVgB66qmnFBsbq9TUVFtbWlqaJk2apKeffrrEigMAACgNhb4KrG3btrbVnyXp0KFDCgoKUoMGDSRJKSkpcnd31x9//KGHH3645CsFUK7k5Brafvik0rOyVdfLqvbBvnJ1sVz7iQBQDhQ6AA0YMKAUywBQkazdm6r41fuVmpFtawvwsSouJkS9QwOcWBkAFA4LIeaDk6CBgq3dm6oxS3bpyj8ceXM/C4eEEYIAOEWZLYSYlJSkAwcOyGKxKCQkRG3btr2e4QCUczm5huJX73cIP5Jk6FIIil+9Xz1C/DkcBqBcK1YASk9P1z333KP169erRo0aMgxDGRkZ6tatmz788ENWhAYqqe2HT9od9rqSISk1I1vbD59UZONaZVcYABRRsa4Ce+yxx5SZmal9+/bp5MmTOnXqlPbu3avMzEyNGzeupGsEUE6kZxUcforTDwCcpVgzQGvXrtVXX32l5s2b29pCQkI0f/589ezZs8SKA1C+1PWylmg/AHCWYs0A5ebmqmrVqg7tVatWVW5u7nUXBaB8ah/sqwAfqwo6u8eiS1eDtQ/2LcuyAKDIihWAbrnlFo0fP17Hjh2ztf3++++aOHGiunfvXmLFAShfXF0siosJkSSHEJR3Py4mhBOgAZR7xQpAr732mrKystSwYUM1btxYN954o4KDg5WVlaV58+aVdI0AypHeoQFaOCRM/j72h7n8faxcAg+gwriudYASExP1008/yTAMhYSE6NZbby3J2pyGdYCAa2MlaADlTVG+v4scgC5evCir1ark5GSFhoZeV6HlFQEIAICKp1R/Db5KlSoKCgpSTk5OsQsEAABwpmL/GvyUKVN08uTJkq4HAACg1BVrHaC5c+fq559/VmBgoIKCguTp6Wn3+K5du0qkOAAAgNJQrAA0YMAAWSwW8TuqAACgIipSADp79qwmTZqkVatW6cKFC+revbvmzZun2rVrl1Z9AAAAJa5I5wDFxcVp8eLF6tu3rwYPHqyvvvpKY8aMKa3aAAAASkWRZoBWrFiht99+W/fcc48k6b777lN0dLRycnLk6upaKgUCAACUtCLNAB09elSdOnWy3W/fvr2qVKli95MYAAAA5V2RAlBOTo7c3Nzs2qpUqaKLFy+WaFEAAAClqUiHwAzD0IgRI+Tu7m5ry87O1ujRo+0uhV+xYkXJVViJ8NMBAACUD0WaARo+fLjq1q0rHx8f223IkCEKDAy0ayuKBQsWKDg4WFarVeHh4dq0aVOBfVNTU3XvvfeqadOmcnFx0YQJE/Ltt3z5coWEhMjd3V0hISFauXJlkWoqDWv3purmWd9o8JvbNP7DZA1+c5tunvWN1u5NdXZpAACYTpFmgN59990S3XhCQoImTJigBQsWKDo6Wm+88Yb69Omj/fv3q0GDBg79z507pzp16mjq1Kl69dVX8x1z69atGjRokJ599lndcccdWrlypQYOHKjNmzerQ4cOJVp/Ya3dm6oxS3bpylWT0jKyNWbJLn5BGwCAMnZdvwZ/vTp06KCwsDAtXLjQ1ta8eXMNGDBAM2fOvOpzu3btqjZt2mj27Nl27YMGDVJmZqa++OILW1vv3r1Vs2ZNLVu2rFB1leSPoebkGrp51jdKzcjO93GLJH8fqzZPvoXDYQAAXIdS/THUknL+/HklJSWpZ8+edu09e/bUli1bij3u1q1bHcbs1avXVcc8d+6cMjMz7W4lZfvhkwWGH0kyJKVmZGv7YX5XDQCAsuK0AHT8+HHl5OTIz8/Prt3Pz09paWnFHjctLa3IY86cOdPuHKb69esXe/tXSs8qOPwUpx8AALh+TgtAeSwW+8M+hmE4tJX2mFOmTFFGRobtdvTo0eva/uXqellLtB8AALh+xfox1JJQu3Ztubq6OszMpKenO8zgFIW/v3+Rx3R3d7e7tL8ktQ/2VYCPVWkZ2Q4nQUv/OweofbBvqWwfAAA4ctoMkJubm8LDw5WYmGjXnpiYqKioqGKPGxkZ6TDmunXrrmvM6+HqYlFcTIikS2Hncnn342JCOAEaAIAy5LQZIEmKjY3V0KFDFRERocjISC1atEgpKSkaPXq0pEuHpn7//Xe9//77tuckJydLkv7880/98ccfSk5Olpubm0JCLoWM8ePHq3Pnzpo1a5b69++vTz75RF999ZU2b95c5vuXp3dogBYOCVP86v12J0T7+1gVFxPCJfAAAJQxp14GL11aCPGFF15QamqqQkND9eqrr6pz586SpBEjRujIkSNav369rX9+5/IEBQXpyJEjtvsff/yxnnrqKf3yyy9q3LixnnvuOd15552FrqkkL4O/HCtBAwBQeory/e30AFQelVYAAgAApadCrAMEAADgLAQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOlWcXQAAlJWcXEPbD59Uela26npZ1T7YV64uFmeXBcAJCEAATGHt3lTFr96v1IxsW1uAj1VxMSHqHRrgxMoAOAOHwABUemv3pmrMkl124UeS0jKyNWbJLq3dm+qkygA4CwEIQKWWk2sofvV+Gfk8ltcWv3q/cnLz6wGgsiIAAajUth8+6TDzczlDUmpGtrYfPll2RQFwOqcHoAULFig4OFhWq1Xh4eHatGnTVftv2LBB4eHhslqtatSokV5//XW7xxcvXiyLxeJwy84u+A8ggMorPatwn/3C9gNQOTg1ACUkJGjChAmaOnWqdu/erU6dOqlPnz5KSUnJt//hw4d12223qVOnTtq9e7eefPJJjRs3TsuXL7fr5+3trdTUVLub1Woti10CUM7U9SrcZ7+w/QBUDk4NQK+88opGjhypUaNGqXnz5po9e7bq16+vhQsX5tv/9ddfV4MGDTR79mw1b95co0aN0gMPPKCXXnrJrp/FYpG/v7/dDYA5tQ/2VYCPVQVd7G7RpavB2gf7lmVZAJzMaQHo/PnzSkpKUs+ePe3ae/bsqS1btuT7nK1btzr079Wrl3bu3KkLFy7Y2v78808FBQWpXr166tevn3bv3l3yOwCgQnB1sSguJkSSHEJQ3v24mBDWAwJMxmkB6Pjx48rJyZGfn59du5+fn9LS0vJ9TlpaWr79L168qOPHj0uSmjVrpsWLF+vTTz/VsmXLZLVaFR0drUOHDhVYy7lz55SZmWl3A1B59A4N0MIhYfL3sT/M5e9j1cIhYawDBJiQ0xdCtFjs/6/LMAyHtmv1v7y9Y8eO6tixo+3x6OhohYWFad68eZo7d26+Y86cOVPx8fHFqh9AxdA7NEA9QvxZCRqAJCcGoNq1a8vV1dVhtic9Pd1hliePv79/vv2rVKmiWrVq5fscFxcXtWvX7qozQFOmTFFsbKztfmZmpurXr1/YXQFQQbi6WBTZOP+/FQDMxWmHwNzc3BQeHq7ExES79sTEREVFReX7nMjISIf+69atU0REhKpWrZrvcwzDUHJysgICCp7idnd3l7e3t90NAABUXk69Ciw2NlZvvfWW3nnnHR04cEATJ05USkqKRo8eLenSzMywYcNs/UePHq1ff/1VsbGxOnDggN555x29/fbb+tvf/mbrEx8fry+//FK//PKLkpOTNXLkSCUnJ9vGBAAAcOo5QIMGDdKJEyc0ffp0paamKjQ0VGvWrFFQUJAkKTU11W5NoODgYK1Zs0YTJ07U/PnzFRgYqLlz5+quu+6y9Tl9+rQeeughpaWlycfHR23bttXGjRvVvn37Mt8/AABQPlmMvLOIYZOZmSkfHx9lZGRwOAwAgAqiKN/fTv8pDAAAgLJGAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZDAAIAAKZTxdkFAAAKlpNraPvhk0rPylZdL6vaB/vK1cXi7LKACo8ABADl1Nq9qYpfvV+pGdm2tgAfq+JiQtQ7NMCJlQEVH4fAAKAcWrs3VWOW7LILP5KUlpGtMUt2ae3eVCdVBlQOBCAAKGdycg3Fr94vI5/H8triV+9XTm5+PQAUBgEIAMqZ7YdPOsz8XM6QlJqRre2HT5ZdUUAlQwACgHImPavg8FOcfgAcEYAAoJyp62Ut0X4AHBGAAKCcaR/sqwAfqwq62N2iS1eDtQ/2LcuygEqFAAQA5Yyri0VxMSGS5BCC8u7HxYSwHhBwHQhAAFAO9Q4N0MIhYfL3sT/M5e9j1cIhYawDBFwnFkIEgHKqd2iAeoT4sxI0UAoIQABQjrm6WBTZuJazywAqHQ6BAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA0+EqMABAicrJNbh0H+UeAQgAUGLW7k1V/Or9dr9mH+BjVVxMCIs3olzhEBgAoESs3ZuqMUt22YUfSUrLyNaYJbu0dm+qkyoDHBGAAADXLSfXUPzq/TLyeSyvLX71fuXk5tcDKHsEIADAddt++KTDzM/lDEmpGdnafvhk2RUFXAUBCABw3dKzCg4/xekHlDYCEADgutX1sl67UxH6AaWNq8AAANetfbCvAnysSsvIzvc8IIskf59Ll8SXV1y+by4EIADAdXN1sSguJkRjluySRbILQXkRIi4mpNwGiop6+T6hrfgshmFwSv4VMjMz5ePjo4yMDHl7ezu7HACoMCpikMi7fP/KL8O8GLFwSFi5rL0ivtZS6Ya2onx/O/0coAULFig4OFhWq1Xh4eHatGnTVftv2LBB4eHhslqtatSokV5//XWHPsuXL1dISIjc3d0VEhKilStXllb5AIDL9A4N0ObJt2jZgx015542WvZgR22efEu5/UKuqJfvV9Q1l9buTdXNs77R4De3afyHyRr85jbdPOsbp9Tr1ACUkJCgCRMmaOrUqdq9e7c6deqkPn36KCUlJd/+hw8f1m233aZOnTpp9+7devLJJzVu3DgtX77c1mfr1q0aNGiQhg4dqh9++EFDhw7VwIED9f3335fVbgGAqbm6WBTZuJb6t7lBkY1rletDMhXx8n1CW8lw6iGwDh06KCwsTAsXLrS1NW/eXAMGDNDMmTMd+k+ePFmffvqpDhw4YGsbPXq0fvjhB23dulWSNGjQIGVmZuqLL76w9endu7dq1qypZcuWFaouDoEBgDl8kvy7xn+YfM1+c+5po/5tbij9ggph639OaPCb267Zb9mDHRXZuFYZVHRtObmGbp71TYFhM+8k+c2Tb7muwFwhDoGdP39eSUlJ6tmzp117z549tWXLlnyfs3XrVof+vXr10s6dO3XhwoWr9iloTEk6d+6cMjMz7W4AgMqvIl6+XxHXXCqPM21OC0DHjx9XTk6O/Pz87Nr9/PyUlpaW73PS0tLy7X/x4kUdP378qn0KGlOSZs6cKR8fH9utfv36xdklAEAFk3f5fkFzDhZdOrG4PF2+T2grGU4/CdpisX/bGYbh0Hat/le2F3XMKVOmKCMjw3Y7evRooesHAFRceZfvS3IIQeX18n1CW8lwWgCqXbu2XF1dHWZm0tPTHWZw8vj7++fbv0qVKqpVq9ZV+xQ0piS5u7vL29vb7gYAMIfeoQFaOCRM/j72X77+PtZyeQk8oa1kOC0Aubm5KTw8XImJiXbtiYmJioqKyvc5kZGRDv3XrVuniIgIVa1a9ap9ChoTAICKdvk+oe36OfUqsISEBA0dOlSvv/66IiMjtWjRIr355pvat2+fgoKCNGXKFP3+++96//33JV26DD40NFQPP/ywHnzwQW3dulWjR4/WsmXLdNddd0mStmzZos6dO+u5555T//799cknn+ipp57S5s2b1aFDh0LVxVVgAICKoKKtBF3aizcW5fvbqT+FMWjQIJ04cULTp09XamqqQkNDtWbNGgUFBUmSUlNT7dYECg4O1po1azRx4kTNnz9fgYGBmjt3ri38SFJUVJQ+/PBDPfXUU3r66afVuHFjJSQkFDr8AABQUeStuVRR9A4NUI8Q/3IR2vgpjHwwAwQAQMVTIdYBAgAAcBYCEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB2n/hRGeZW3OHZmZqaTKwEAAIWV971dmB+5IADlIysrS5JUv359J1cCAACKKisrSz4+Plftw2+B5SM3N1fHjh2Tl5eXLJaS/YG2zMxM1a9fX0ePHuV3xkoRr3PZ4HUuG7zOZYfXumyU1utsGIaysrIUGBgoF5ern+XDDFA+XFxcVK9evVLdhre3Nx+uMsDrXDZ4ncsGr3PZ4bUuG6XxOl9r5icPJ0EDAADTIQABAADTIQCVMXd3d8XFxcnd3d3ZpVRqvM5lg9e5bPA6lx1e67JRHl5nToIGAACmwwwQAAAwHQIQAAAwHQIQAAAwHQIQAAAwHQJQGVqwYIGCg4NltVoVHh6uTZs2ObukSmfmzJlq166dvLy8VLduXQ0YMEAHDx50dlmV2syZM2WxWDRhwgRnl1Ip/f777xoyZIhq1aqlatWqqU2bNkpKSnJ2WZXKxYsX9dRTTyk4OFgeHh5q1KiRpk+frtzcXGeXVqFt3LhRMTExCgwMlMVi0apVq+weNwxD06ZNU2BgoDw8PNS1a1ft27evzOojAJWRhIQETZgwQVOnTtXu3bvVqVMn9enTRykpKc4urVLZsGGDxo4dq23btikxMVEXL15Uz549debMGWeXVint2LFDixYtUqtWrZxdSqV06tQpRUdHq2rVqvriiy+0f/9+vfzyy6pRo4azS6tUZs2apddff12vvfaaDhw4oBdeeEEvvvii5s2b5+zSKrQzZ86odevWeu211/J9/IUXXtArr7yi1157TTt27JC/v7969Ohh+z3OUmegTLRv394YPXq0XVuzZs2MJ554wkkVmUN6erohydiwYYOzS6l0srKyjCZNmhiJiYlGly5djPHjxzu7pEpn8uTJxs033+zsMiq9vn37Gg888IBd25133mkMGTLESRVVPpKMlStX2u7n5uYa/v7+xvPPP29ry87ONnx8fIzXX3+9TGpiBqgMnD9/XklJSerZs6dde8+ePbVlyxYnVWUOGRkZkiRfX18nV1L5jB07Vn379tWtt97q7FIqrU8//VQRERG6++67VbduXbVt21Zvvvmms8uqdG6++WZ9/fXX+ve//y1J+uGHH7R582bddtttTq6s8jp8+LDS0tLsvhfd3d3VpUuXMvte5MdQy8Dx48eVk5MjPz8/u3Y/Pz+lpaU5qarKzzAMxcbG6uabb1ZoaKizy6lUPvzwQ+3atUs7duxwdimV2i+//KKFCxcqNjZWTz75pLZv365x48bJ3d1dw4YNc3Z5lcbkyZOVkZGhZs2aydXVVTk5OXruuec0ePBgZ5dWaeV99+X3vfjrr7+WSQ0EoDJksVjs7huG4dCGkvPoo49qz5492rx5s7NLqVSOHj2q8ePHa926dbJarc4up1LLzc1VRESEZsyYIUlq27at9u3bp4ULFxKASlBCQoKWLFmipUuXqkWLFkpOTtaECRMUGBio4cOHO7u8Ss2Z34sEoDJQu3Ztubq6Osz2pKenO6RflIzHHntMn376qTZu3Kh69eo5u5xKJSkpSenp6QoPD7e15eTkaOPGjXrttdd07tw5ubq6OrHCyiMgIEAhISF2bc2bN9fy5cudVFHlNGnSJD3xxBO65557JEktW7bUr7/+qpkzZxKASom/v7+kSzNBAQEBtvay/F7kHKAy4ObmpvDwcCUmJtq1JyYmKioqyklVVU6GYejRRx/VihUr9M033yg4ONjZJVU63bt3148//qjk5GTbLSIiQvfdd5+Sk5MJPyUoOjraYRmHf//73woKCnJSRZXT2bNn5eJi/3Xo6urKZfClKDg4WP7+/nbfi+fPn9eGDRvK7HuRGaAyEhsbq6FDhyoiIkKRkZFatGiRUlJSNHr0aGeXVqmMHTtWS5cu1SeffCIvLy/brJuPj488PDycXF3l4OXl5XBOlaenp2rVqsW5ViVs4sSJioqK0owZMzRw4EBt375dixYt0qJFi5xdWqUSExOj5557Tg0aNFCLFi20e/duvfLKK3rggQecXVqF9ueff+rnn3+23T98+LCSk5Pl6+urBg0aaMKECZoxY4aaNGmiJk2aaMaMGapWrZruvffesimwTK41g2EYhjF//nwjKCjIcHNzM8LCwrg0uxRIyvf27rvvOru0So3L4EvP6tWrjdDQUMPd3d1o1qyZsWjRImeXVOlkZmYa48ePNxo0aGBYrVajUaNGxtSpU41z5845u7QK7dtvv8337/Hw4cMNw7h0KXxcXJzh7+9vuLu7G507dzZ+/PHHMqvPYhiGUTZRCwAAoHzgHCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAA5cqRI0dksViUnJzs7FJsfvrpJ3Xs2FFWq1Vt2rRxdjmSpIYNG2r27NnOLgOosAhAAOyMGDFCFotFzz//vF37qlWryuxXmsubuLg4eXp66uDBg/r6668L7JeWlqbx48frxhtvlNVqlZ+fn26++Wa9/vrrOnv2bBlWDOBa+C0wAA6sVqtmzZqlhx9+WDVr1nR2OSXi/PnzcnNzK9Zz//Of/6hv375X/RHSX375RdHR0apRo4ZmzJihli1b6uLFi/r3v/+td955R4GBgbr99tuLWz6AEsYMEAAHt956q/z9/TVz5swC+0ybNs3hcNDs2bPVsGFD2/0RI0ZowIABmjFjhvz8/FSjRg3Fx8fr4sWLmjRpknx9fVWvXj298847DuP/9NNPioqKktVqVYsWLbR+/Xq7x/fv36/bbrtN1atXl5+fn4YOHarjx4/bHu/ataseffRRxcbGqnbt2urRo0e++5Gbm6vp06erXr16cnd3V5s2bbR27Vrb4xaLRUlJSZo+fbosFoumTZuW7ziPPPKIqlSpop07d2rgwIFq3ry5WrZsqbvuukuff/65YmJibH1TUlLUv39/Va9eXd7e3ho4cKD++9//2h7/z3/+o/79+8vPz0/Vq1dXu3bt9NVXX+W73TzTpk1TgwYN5O7ursDAQI0bN+6q/QGzIwABcODq6qoZM2Zo3rx5+u23365rrG+++UbHjh3Txo0b9corr2jatGnq16+fatasqe+//16jR4/W6NGjdfToUbvnTZo0SY8//rh2796tqKgo3X777Tpx4oQkKTU1VV26dFGbNm20c+dOrV27Vv/97381cOBAuzHee+89ValSRd99953eeOONfOubM2eOXn75Zb300kvas2ePevXqpdtvv12HDh2ybatFixZ6/PHHlZqaqr/97W8OY5w4cULr1q3T2LFj5enpme928g4fGoahAQMG6OTJk9qwYYMSExP1n//8R4MGDbL1/fPPP3Xbbbfpq6++0u7du9WrVy/FxMQoJSUl37E//vhjvfrqq3rjjTd06NAhrVq1Si1btsy3L4D/r8x+dhVAhTB8+HCjf//+hmEYRseOHY0HHnjAMAzDWLlypXH5n4y4uDijdevWds999dVXjaCgILuxgoKCjJycHFtb06ZNjU6dOtnuX7x40fD09DSWLVtmGIZhHD582JBkPP/887Y+Fy5cMOrVq2fMmjXLMAzDePrpp42ePXvabfvo0aOGJOPgwYOGYVz6hfo2bdpcc38DAwON5557zq6tXbt2xiOPPGK737p1ayMuLq7AMbZt22ZIMlasWGHXXqtWLcPT09Pw9PQ0/v73vxuGYRjr1q0zXF1djZSUFFu/ffv2GZKM7du3F7iNkJAQY968ebb7QUFBxquvvmoYhmG8/PLLxk033WScP3/+mvsL4BJmgAAUaNasWXrvvfe0f//+Yo/RokULubj870+Nn5+f3eyEq6uratWqpfT0dLvnRUZG2v67SpUqioiI0IEDByRJSUlJ+vbbb1W9enXbrVmzZpIuHT7KExERcdXaMjMzdezYMUVHR9u1R0dH27ZVFFeeJL59+3YlJyerRYsWOnfunCTpwIEDql+/vurXr2/rFxISoho1ati2eebMGf3973+3tVevXl0//fRTgTNAd999t/766y81atRIDz74oFauXKmLFy8WuX7ATAhAAArUuXNn9erVS08++aTDYy4uLjIMw67twoULDv2qVq1qd99iseTblpube8168gJGbm6uYmJilJycbHc7dOiQOnfubOtf0OGogsbNYxhGka54u/HGG2WxWPTTTz/ZtTdq1Eg33nijPDw8rjn25e2TJk3S8uXL9dxzz2nTpk1KTk5Wy5Ytdf78+Xy3X79+fR08eFDz58+Xh4eHHnnkEXXu3Dnffw8AlxCAAFzV888/r9WrV2vLli127XXq1FFaWppdCCrJtXu2bdtm+++LFy8qKSnJNssTFhamffv2qWHDhrrxxhvtboUNPZLk7e2twMBAbd682a59y5Ytat68eaHHqVWrlnr06KHXXntNZ86cuWrfkJAQpaSk2J3ztH//fmVkZNi2uWnTJo0YMUJ33HGHWrZsKX9/fx05cuSq43p4eOj222/X3LlztX79em3dulU//vhjofcBMBsCEICratmype677z7NmzfPrr1r1676448/9MILL+g///mP5s+fry+++KLEtjt//nytXLlSP/30k8aOHatTp07pgQcekCSNHTtWJ0+e1ODBg7V9+3b98ssvWrdunR544AHl5OQUaTuTJk3SrFmzlJCQoIMHD+qJJ55QcnKyxo8fX6RxFixYoIsXLyoiIkIJCQk6cOCADh48qCVLluinn36Sq6urpEtX2LVq1Ur33Xefdu3ape3bt2vYsGHq0qWL7ZDdjTfeqBUrVig5OVk//PCD7r333qvOkC1evFhvv/229u7dq19++UUffPCBPDw8rnrZPmB2BCAA1/Tss886HO5q3ry5FixYoPnz56t169bavn17vldIFdfzzz+vWbNmqXXr1tq0aZM++eQT1a5dW5IUGBio7777Tjk5OerVq5dCQ0M1fvx4+fj42J1vVBjjxo3T448/rscff1wtW7bU2rVr9emnn6pJkyZFGqdx48bavXu3br31Vk2ZMkWtW7dWRESE5s2bp7/97W969tlnJV063LZq1SrVrFlTnTt31q233qpGjRopISHBNtarr76qmjVrKioqSjExMerVq5fCwsIK3HaNGjX05ptvKjo6Wq1atdLXX3+t1atXq1atWkXaB8BMLMaVf9UAAAAqOWaAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6fw/4T/xghkPke8AAAAASUVORK5CYII=", + "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, 11)\n", + "plt.plot(X, poisson_dist.pmf(X), \"o\")\n", + "plt.xlabel('Number of Goals')\n", + "plt.ylabel('Probability')\n", + "plt.title('Poisson Probability Distribution of Goals')\n", + "\n", + "plt.show()" ] } ], @@ -192,7 +328,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.10.9" } }, "nbformat": 4,