From 1e0db5c7253e1f44b6f66d92e57683c4f5e16e8d Mon Sep 17 00:00:00 2001 From: CevicX <134785214+CevicX@users.noreply.github.com> Date: Fri, 11 Aug 2023 17:04:01 +0100 Subject: [PATCH] done --- your-code/continuous.ipynb | 226 ++++++++++++++++++++++----- your-code/discrete.ipynb | 151 +++++++++++++++--- your-code/pop_sample_CLT.ipynb | 269 +++++++++++++++++++++++++++++++++ 3 files changed, 588 insertions(+), 58 deletions(-) create mode 100644 your-code/pop_sample_CLT.ipynb diff --git a/your-code/continuous.ipynb b/your-code/continuous.ipynb index 32218e9..e7fb781 100644 --- a/your-code/continuous.ipynb +++ b/your-code/continuous.ipynb @@ -35,11 +35,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2.15747023 2.54910341 2.38050212 2.23544503 2.47972762 2.10384408\n", + " 2.40975206 2.67012909 2.03600426 2.37387728]\n" + ] + } + ], "source": [ "from scipy.stats import uniform\n", + "import numpy as np\n", "x = uniform.rvs(size=10)\n", "a = 2\n", "b = 3\n", @@ -73,11 +83,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "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", + "import matplotlib.pyplot as plt\n", + "def uniform_random(bottom,celling,count):\n", + " x = uniform.rvs(size=count)\n", + " a = bottom\n", + " b = celling\n", + " randoms = a + (b-a)*x\n", + " return randoms\n", + "fig, (chart1, chart2) = plt.subplots(1, 2, sharey=True)\n", + "chart1.hist(uniform_random(10,15,100),bins=10)\n", + "chart2.hist(uniform_random(10,60,1000), bins=10)\n", + "plt.show()" ] }, { @@ -89,11 +121,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'Both plots are uniform but the second plot shows more variation'" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your answer here:\n" + "# your answer here:\n", + "'Both plots are uniform but the second plot shows more variation'" ] }, { @@ -114,11 +158,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "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", + "from scipy.stats import *\n", + "def norm_random(count,std,avg):\n", + " x = norm.rvs(size=count,loc=avg,scale=std)\n", + " return x\n", + "fig, (chart1, chart2) = plt.subplots(1, 2, sharey=True)\n", + "chart1.hist(norm_random(1000,1,10),bins=30)\n", + "chart2.hist(norm_random(1000,50,10),bins=30)\n", + "plt.show()" ] }, { @@ -130,11 +193,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'They look similar but the second has a wider range due its high deviation'" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your answer here:\n" + "# your answer here:\n", + "'They look similar but the second has a wider range due its high deviation'" ] }, { @@ -158,11 +233,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "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", + "import pandas as pd\n", + "vehicles=pd.read_csv('vehicles.csv')\n", + "vehicles.head(1)\n", + "plt.hist(x=vehicles['Fuel Barrels/Year'])\n", + "plt.show()" ] }, { @@ -174,11 +265,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "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(x=vehicles['CO2 Emission Grams/Mile'])\n", + "plt.show()" ] }, { @@ -190,11 +294,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "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(x=vehicles['Combined MPG'])\n", + "plt.show()" ] }, { @@ -206,11 +323,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'All of them, based on the shape they form where the mean next to the center'" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# you answer here:\n" + "# you answer here:\n", + "'All of them, based on the shape they form where the mean next to the center'" ] }, { @@ -237,11 +366,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "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", + "def random_exp(size,scale):\n", + " return np.random.exponential(size=size,scale=scale)\n", + "fig, (chart1, chart2) = plt.subplots(1, 2, sharey=True)\n", + "chart1.hist(random_exp(1000,1),bins=100)\n", + "chart2.hist(random_exp(1000,100),bins=100)\n", + "plt.show()\n" ] }, { @@ -253,11 +399,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ - "# your answer here:\n" + "# your answer here:\n", + "'The range of both are different'" ] }, { @@ -273,12 +420,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "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", + "mean = 10\n", + "lambda_inv = 1/10\n", + "minutes = 15 \n", + "expon_dist = expon(scale=lambda_inv)\n", + "\n", + "print(expon_dist.cdf(10))" ] }, { @@ -290,11 +443,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ - "# your answer here\n" + "# your answer here\n", + "print(1-expon_dist.cdf(15))" ] } ], @@ -314,7 +468,7 @@ "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..5ec0327 100644 --- a/your-code/discrete.ipynb +++ b/your-code/discrete.ipynb @@ -33,17 +33,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6\n", + "0.4\n" + ] + } + ], "source": [ + "from scipy.stats import *\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", "\"\"\"\n", "Calculate:\n", "p = probability that the fruit is an apple \n", "q = probability that the fruit is an orange\n", "\"\"\"\n", "\n", - "# your code here\n" + "# your code here\n", + "p=.6\n", + "bernoulli_dist = bernoulli(p)\n", + "print(bernoulli_dist.pmf(1))\n", + "q=.4\n", + "bernoulli_dist2= bernoulli(q)\n", + "print(np.round(bernoulli_dist2.pmf(1),3))\n" ] }, { @@ -61,11 +79,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.078\n", + "0.0\n" + ] + } + ], "source": [ - "# your code here\n" + "# your code here\n", + "\n", + "p = 0.6**5\n", + "print(round(p,3))\n", + "\n", + "q=.078**15\n", + "print(round(q,3))" ] }, { @@ -83,11 +116,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.075\n" + ] + } + ], "source": [ - "# your code here\n" + "# your code here\n", + "p=0.4\n", + "n=20\n", + "binomial_dist = binom(n,p)\n", + "\n", + "print(round(binomial_dist.pmf(5),3))" ] }, { @@ -101,11 +147,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9490480468058334\n" + ] + } + ], "source": [ - "# your code here\n" + "# your code here\n", + "print(1 - binomial_dist.cdf(4))" ] }, { @@ -119,12 +174,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "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,n)\n", + "\n", + "plt.plot(X, binomial_dist.pmf(X), \"o\")\n", + "plt.xlabel('Number of Apples')\n", + "plt.ylabel('Probability')\n", + "plt.title('Binomial Distribution (PDF)')\n", + "plt.show()\n" ] }, { @@ -151,11 +224,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "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", + "mu=2.3\n", + "x=5\n", + "poisson_dist=poisson(mu)\n", + "print(poisson_dist.pmf(5))\n", + "\n" ] }, { @@ -167,12 +255,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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iorRp0ya1bdvW0eUApRbBCADuAo8//rj279+vQ4cOccIzcBs4xwgASrElS5bohx9+0OrVq/Xuu+8SioDbxB4jACjFLBaLPD091aNHD82dO1dlyvDvXeB28A4CgFKMf9sCRYvvMQIAADARjAAAAEwcSrMjKytLp06dUvny5TmREQCAUsIwDF28eFH+/v5ycircvh+CkR2nTp3K8btKAACgdDhx4oSqV69eqHUJRnaUL19e0u9P7M2/rwQAAEqm9PR0BQQEWD/HC4NgZEf24bMKFSoQjAAAKGVu5zQYTr4GAAAwEYwAAABMBCMAAAATwQgAAMBEMAIAADARjAAAAEwEIwAAABPBCAAAwEQwAgAAMBGMAAAATAQjAAAAE8EIAADARDACAAAwEYwAAABMBCMAAABTGUcXgNIhaNTqIhnn2MTORTIOAAB3AnuMAAAATAQjAAAAE8EIAADARDACAAAwEYwAAABMBCMAAAATwQgAAMBEMAIAADARjAAAAEwEIwAAABPBCAAAwEQwAgAAMBGMAAAATAQjAAAAE8EIAADAVCKC0axZsxQUFCQ3NzeFhoZq27ZtufadN2+e2rZtq4oVK6pixYqKiIjI0b9fv36yWCw2t44dO97paQAAgFLO4cFo6dKliomJUWxsrHbu3Kng4GBFRkbqzJkzdvsnJiaqV69e2rBhg5KSkhQQEKAOHTro559/tunXsWNHnT592nr79NNPi2M6AACgFHN4MJo2bZoGDBig/v37q2HDhpo7d67KlSun+fPn2+3/8ccfa/DgwWratKnq16+vv//978rKylJCQoJNP1dXV/n5+VlvFStWLI7pAACAUsyhwSgjI0M7duxQRESEtc3JyUkRERFKSkrK1xhXrlzR9evXdd9999m0JyYmqkqVKqpXr54GDRqk8+fP5zrGtWvXlJ6ebnMDAAD3HocGo3PnzikzM1O+vr427b6+vkpJScnXGK+88or8/f1twlXHjh21ePFiJSQkaNKkSdq4caM6deqkzMxMu2PExcXJy8vLegsICCj8pAAAQKlVxtEF3I6JEydqyZIlSkxMlJubm7W9Z8+e1r8bN26sJk2aqFatWkpMTNSjjz6aY5zRo0crJibGupyenk44AgDgHuTQPUY+Pj5ydnZWamqqTXtqaqr8/PzyXHfKlCmaOHGivv76azVp0iTPvjVr1pSPj48OHTpk935XV1dVqFDB5gYAAO49Dg1GLi4uCgkJsTlxOvtE6rCwsFzXmzx5siZMmKD4+Hi1aNHilo9z8uRJnT9/XlWrVi2SugEAwN3J4VelxcTEaN68eVq0aJH279+vQYMG6fLly+rfv78kqU+fPho9erS1/6RJk/T6669r/vz5CgoKUkpKilJSUnTp0iVJ0qVLlzRy5Eht2bJFx44dU0JCgrp27aratWsrMjLSIXMEAAClg8PPMerRo4fOnj2rMWPGKCUlRU2bNlV8fLz1hOzjx4/Lyel/+W3OnDnKyMjQn//8Z5txYmNjNXbsWDk7O+uHH37QokWLlJaWJn9/f3Xo0EETJkyQq6trsc4NAACULhbDMAxHF1HSpKeny8vLSxcuXOB8I1PQqNVFMs6xiZ2LZBwAAG5WFJ/fDj+UBgAAUFIQjAAAAEwEIwAAABPBCAAAwEQwAgAAMBGMAAAATAQjAAAAE8EIAADARDACAAAwEYwAAABMBCMAAAATwQgAAMBEMAIAADARjAAAAEwEIwAAABPBCAAAwEQwAgAAMBGMAAAATAQjAAAAUxlHF4CiEzRqdZGNdWxi5yIbCwCA0oI9RgAAACaCEQAAgIlgBAAAYCIYAQAAmAhGAAAAJoIRAACAiWAEAABgIhgBAACYCEYAAAAmghEAAICJYAQAAGAiGAEAAJgIRgAAACaCEQAAgIlgBAAAYCIYAQAAmAhGAAAAJoIRAACAiWAEAABgIhgBAACYCEYAAAAmghEAAICJYAQAAGAiGAEAAJgIRgAAACaCEQAAgIlgBAAAYCIYAQAAmAhGAAAAJoIRAACAiWAEAABgIhgBAACYCEYAAAAmghEAAICJYAQAAGAq4+gCJGnWrFl6++23lZKSouDgYM2cOVMtW7a023fevHlavHix9uzZI0kKCQnRW2+9ZdPfMAzFxsZq3rx5SktLU+vWrTVnzhzVqVOnWOaDggkatbpIxjk2sXORjAMAuHc5fI/R0qVLFRMTo9jYWO3cuVPBwcGKjIzUmTNn7PZPTExUr169tGHDBiUlJSkgIEAdOnTQzz//bO0zefJkzZgxQ3PnztXWrVvl4eGhyMhIXb16tbimBQAASiGHB6Np06ZpwIAB6t+/vxo2bKi5c+eqXLlymj9/vt3+H3/8sQYPHqymTZuqfv36+vvf/66srCwlJCRI+n1v0fTp0/Xaa6+pa9euatKkiRYvXqxTp05p5cqVxTgzAABQ2jg0GGVkZGjHjh2KiIiwtjk5OSkiIkJJSUn5GuPKlSu6fv267rvvPknS0aNHlZKSYjOml5eXQkNDcx3z2rVrSk9Pt7kBAIB7j0OD0blz55SZmSlfX1+bdl9fX6WkpORrjFdeeUX+/v7WIJS9XkHGjIuLk5eXl/UWEBBQ0KkAAIC7gMMPpd2OiRMnasmSJVqxYoXc3NwKPc7o0aN14cIF6+3EiRNFWCUAACgtHHpVmo+Pj5ydnZWammrTnpqaKj8/vzzXnTJliiZOnKh169apSZMm1vbs9VJTU1W1alWbMZs2bWp3LFdXV7m6uhZyFgAA4G7h0D1GLi4uCgkJsZ44Lcl6InVYWFiu602ePFkTJkxQfHy8WrRoYXNfjRo15OfnZzNmenq6tm7dmueYAAAADv8eo5iYGPXt21ctWrRQy5YtNX36dF2+fFn9+/eXJPXp00fVqlVTXFycJGnSpEkaM2aMPvnkEwUFBVnPG/L09JSnp6csFouGDx+uN954Q3Xq1FGNGjX0+uuvy9/fX1FRUY6aJgAAKAUcHox69Oihs2fPasyYMUpJSVHTpk0VHx9vPXn6+PHjcnL6346tOXPmKCMjQ3/+859txomNjdXYsWMlSS+//LIuX76s559/XmlpaWrTpo3i4+Nv6zwkAABw93N4MJKk6OhoRUdH270vMTHRZvnYsWO3HM9isWj8+PEaP358EVQHAADuFaX6qjQAAICiRDACAAAwEYwAAABMBCMAAAATwQgAAMBEMAIAADARjAAAAEwEIwAAABPBCAAAwEQwAgAAMBGMAAAATAQjAAAAE8EIAADARDACAAAwEYwAAABMBCMAAAATwQgAAMBEMAIAADARjAAAAEwEIwAAABPBCAAAwEQwAgAAMBGMAAAATAQjAAAAE8EIAADARDACAAAwEYwAAABMBCMAAAATwQgAAMBEMAIAADARjAAAAEwEIwAAABPBCAAAwEQwAgAAMBGMAAAATAQjAAAAE8EIAADARDACAAAwEYwAAABMBCMAAABToYLRhg0biroOAAAAhytUMOrYsaNq1aqlN954QydOnCjqmgAAAByiUMHo559/VnR0tP75z3+qZs2aioyM1GeffaaMjIyirg8AAKDYFCoY+fj46MUXX9Tu3bu1detW1a1bV4MHD5a/v7+GDh2q77//vqjrBAAAuONu++Tr5s2ba/To0YqOjtalS5c0f/58hYSEqG3bttq7d29R1AgAAFAsCh2Mrl+/rn/+85967LHHFBgYqDVr1ui9995TamqqDh06pMDAQHXr1q0oawUAALijyhRmpf/7v//Tp59+KsMw9Mwzz2jy5Ml64IEHrPd7eHhoypQp8vf3L7JCAQAA7rRCBaN9+/Zp5syZeuqpp+Tq6mq3j4+PD5f1AwCAUqVQh9JiY2PVrVu3HKHoxo0b2rRpkySpTJkyCg8Pv/0KAQAAikmhgtHDDz+sX375JUf7hQsX9PDDD992UQAAAI5QqGBkGIYsFkuO9vPnz8vDw+O2iwIAAHCEAp1j9NRTT0mSLBaL+vXrZ3MoLTMzUz/88INatWpVtBUCAAAUkwIFIy8vL0m/7zEqX7683N3drfe5uLjooYce0oABA4q2QgAAgGJSoGC0YMECSVJQUJBGjBjBYTMAAHBXKdTl+rGxsUVdBwAAgMPl++Tr5s2b69dff5UkNWvWTM2bN8/1VhCzZs1SUFCQ3NzcFBoaqm3btuXad+/evXr66acVFBQki8Wi6dOn5+gzduxYWSwWm1v9+vULVBMAALg35XuPUdeuXa0nW0dFRRXJgy9dulQxMTGaO3euQkNDNX36dEVGRio5OVlVqlTJ0f/KlSuqWbOmunXrphdffDHXcRs1aqR169ZZl8uUKdSOMQAAcI/Jd2L44+GzojqUNm3aNA0YMED9+/eXJM2dO1erV6/W/PnzNWrUqBz9H3zwQT344IOSZPf+bGXKlJGfn1+R1AgAAO4dhf4R2duVkZGhHTt2KCIi4n/FODkpIiJCSUlJtzX2wYMH5e/vr5o1a6p37946fvz47ZYLAADuAfneY1SxYkW7X+poj71vxb7ZuXPnlJmZKV9fX5t2X19fHThwIL9l5RAaGqqFCxeqXr16On36tMaNG6e2bdtqz549Kl++vN11rl27pmvXrlmX09PTC/34AACg9Mp3MLJ3onNJ1KlTJ+vfTZo0UWhoqAIDA/XZZ5/pueees7tOXFycxo0bV1wlAgCAEirfwahv375F+sA+Pj5ydnZWamqqTXtqamqRnh/k7e2tunXr6tChQ7n2GT16tGJiYqzL6enpCggIKLIaAABA6ZDvc4z+eHgpPT09z1t+uLi4KCQkRAkJCda2rKwsJSQkKCwsrABTyNulS5d0+PBhVa1aNdc+rq6uqlChgs0NAADcewp0jtHp06dVpUoVeXt72z3fKPvHZTMzM/M1ZkxMjPr27asWLVqoZcuWmj59ui5fvmy9Sq1Pnz6qVq2a4uLiJP1+wva+ffusf//888/avXu3PD09Vbt2bUnSiBEj1KVLFwUGBurUqVOKjY2Vs7OzevXqld+pAgCAe1S+g9H69et13333SZI2bNhQJA/eo0cPnT17VmPGjFFKSoqaNm2q+Ph46wnZx48fl5PT/3ZqnTp1Ss2aNbMuT5kyRVOmTFF4eLgSExMlSSdPnlSvXr10/vx5Va5cWW3atNGWLVtUuXLlIqkZAADcvfIdjMLDw+3+fbuio6MVHR1t977ssJMtKChIhmHkOd6SJUuKqjQAAHCPKfRXQv/666/68MMPtX//fklSw4YN1b9/f+teJQAAgNKmUF/wuGnTJgUFBWnGjBn69ddf9euvv2rGjBmqUaOGNm3aVNQ1AgAAFItC7TEaMmSIevTooTlz5sjZ2VmSlJmZqcGDB2vIkCH68ccfi7RIAACA4lCoPUaHDh3SSy+9ZA1FkuTs7KyYmJg8vy8IAACgJCtUMGrevLn13KI/2r9/v4KDg2+7KAAAAEfI96G0H374wfr30KFDNWzYMB06dEgPPfSQJGnLli2aNWuWJk6cWPRVAgAAFIN8B6OmTZvKYrHYXC7/8ssv5+j3l7/8RT169Cia6gAAAIpRvoPR0aNH72QdAAAADpfvYBQYGHgn6wAAAHC4Qn/BoyTt27dPx48fV0ZGhk37E088cVtFAQAAOEKhgtGRI0f05JNP6scff7Q57yj7h2Xz+yOyAAAAJUmhLtcfNmyYatSooTNnzqhcuXLau3evNm3apBYtWuT4fTMAAIDSolB7jJKSkrR+/Xr5+PjIyclJTk5OatOmjeLi4jR06FDt2rWrqOsEAAC44wq1xygzM1Ply5eXJPn4+OjUqVOSfj9BOzk5ueiqAwAAKEaF2mP0wAMP6Pvvv1eNGjUUGhqqyZMny8XFRR988IFq1qxZ1DUCAAAUi0IFo9dee02XL1+WJI0fP16PP/642rZtq0qVKmnp0qVFWiAAAEBxKVQwioyMtP5du3ZtHThwQL/88osqVqxovTINAACgtLmt7zGSpBMnTkiSAgICbrsYAAAARyrUydc3btzQ66+/Li8vLwUFBSkoKEheXl567bXXdP369aKuEQAAoFgUao/R//3f/2n58uWaPHmywsLCJP1+Cf/YsWN1/vx5zZkzp0iLBAAAKA6FCkaffPKJlixZok6dOlnbmjRpooCAAPXq1YtgBAAASqVCHUpzdXVVUFBQjvYaNWrIxcXldmsCAABwiEIFo+joaE2YMEHXrl2ztl27dk1vvvmmoqOji6w4AACA4pTvQ2lPPfWUzfK6detUvXp1BQcHS5K+//57ZWRk6NFHHy3aCgEAAIpJvoORl5eXzfLTTz9ts8zl+gAAoLTLdzBasGDBnawDAADA4W7rCx7Pnj1r/dHYevXqqXLlykVSFAAAgCMU6uTry5cv69lnn1XVqlXVrl07tWvXTv7+/nruued05cqVoq4RAACgWBQqGMXExGjjxo364osvlJaWprS0NK1atUobN27USy+9VNQ1AgAAFItCHUr7/PPP9c9//lPt27e3tj322GNyd3dX9+7d+YJHAABQKhVqj9GVK1fk6+ubo71KlSocSgMAAKVWoYJRWFiYYmNjdfXqVWvbb7/9pnHjxll/Ow0AAKC0KdShtOnTp6tjx445vuDRzc1Na9asKdICAQAAikuhglHjxo118OBBffzxxzpw4IAkqVevXurdu7fc3d2LtEAAAIDiUuBgdP36ddWvX19ffvmlBgwYcCdqAgAAcIgCn2NUtmxZm3OLAAAA7haFOvl6yJAhmjRpkm7cuFHU9QAAADhMoc4x+u6775SQkKCvv/5ajRs3loeHh839y5cvL5LiAAAAilOhgpG3t7eefvrpoq4FKHJBo1YXyTjHJnYuknEAACVbgYJRVlaW3n77bf33v/9VRkaGHnnkEY0dO5Yr0QAAwF2hQOcYvfnmm3r11Vfl6empatWqacaMGRoyZMidqg0AAKBYFSgYLV68WLNnz9aaNWu0cuVKffHFF/r444+VlZV1p+oDAAAoNgUKRsePH9djjz1mXY6IiJDFYtGpU6eKvDAAAIDiVqBgdOPGDbm5udm0lS1bVtevXy/SogAAAByhQCdfG4ahfv36ydXV1dp29epVvfDCCzaX7HO5PgAAKI0KFIz69u2bo+2vf/1rkRUDAADgSAUKRgsWLLhTdQAAADhcoX4SBAAA4G5EMAIAADARjAAAAEwEIwAAABPBCAAAwEQwAgAAMBGMAAAATAQjAAAAE8EIAADA5PBgNGvWLAUFBcnNzU2hoaHatm1brn337t2rp59+WkFBQbJYLJo+ffptjwkAAJDNocFo6dKliomJUWxsrHbu3Kng4GBFRkbqzJkzdvtfuXJFNWvW1MSJE+Xn51ckYwIAAGRzaDCaNm2aBgwYoP79+6thw4aaO3euypUrp/nz59vt/+CDD+rtt99Wz5495erqWiRjAgAAZHNYMMrIyNCOHTsUERHxv2KcnBQREaGkpKRiHfPatWtKT0+3uQEAgHtPGUc98Llz55SZmSlfX1+bdl9fXx04cKBYx4yLi9O4ceMK9ZiFETRqdZGMc2xi5yIZBwAA/M7hJ1+XBKNHj9aFCxestxMnTji6JAAA4AAO22Pk4+MjZ2dnpaam2rSnpqbmemL1nRrT1dU113OWAADAvcNhe4xcXFwUEhKihIQEa1tWVpYSEhIUFhZWYsYEAAD3DoftMZKkmJgY9e3bVy1atFDLli01ffp0Xb58Wf3795ck9enTR9WqVVNcXJyk30+u3rdvn/Xvn3/+Wbt375anp6dq166drzEBAABy49Bg1KNHD509e1ZjxoxRSkqKmjZtqvj4eOvJ08ePH5eT0/92ap06dUrNmjWzLk+ZMkVTpkxReHi4EhMT8zUmAABAbhwajCQpOjpa0dHRdu/LDjvZgoKCZBjGbY0JAACQG65KAwAAMBGMAAAATAQjAAAAE8EIAADARDACAAAwEYwAAABMBCMAAAATwQgAAMBEMAIAADARjAAAAEwEIwAAABPBCAAAwEQwAgAAMBGMAAAATAQjAAAAE8EIAADARDACAAAwEYwAAABMBCMAAAATwQgAAMBEMAIAADARjAAAAEwEIwAAABPBCAAAwEQwAgAAMBGMAAAATAQjAAAAE8EIAADARDACAAAwEYwAAABMBCMAAAATwQgAAMBEMAIAADARjAAAAEwEIwAAABPBCAAAwEQwAgAAMBGMAAAATAQjAAAAE8EIAADARDACAAAwEYwAAABMBCMAAAATwQgAAMBEMAIAADARjAAAAEwEIwAAABPBCAAAwEQwAgAAMBGMAAAATAQjAAAAE8EIAADARDACAAAwEYwAAABMZRxdAFAaBY1aXWRjHZvYucjGAgDcnhKxx2jWrFkKCgqSm5ubQkNDtW3btjz7L1u2TPXr15ebm5saN26sr776yub+fv36yWKx2Nw6dux4J6cAAADuAg4PRkuXLlVMTIxiY2O1c+dOBQcHKzIyUmfOnLHb/9tvv1WvXr303HPPadeuXYqKilJUVJT27Nlj069jx446ffq09fbpp58Wx3QAAEAp5vBgNG3aNA0YMED9+/dXw4YNNXfuXJUrV07z58+32//dd99Vx44dNXLkSDVo0EATJkxQ8+bN9d5779n0c3V1lZ+fn/VWsWLF4pgOAAAoxRwajDIyMrRjxw5FRERY25ycnBQREaGkpCS76yQlJdn0l6TIyMgc/RMTE1WlShXVq1dPgwYN0vnz53Ot49q1a0pPT7e5AQCAe49Dg9G5c+eUmZkpX19fm3ZfX1+lpKTYXSclJeWW/Tt27KjFixcrISFBkyZN0saNG9WpUydlZmbaHTMuLk5eXl7WW0BAwG3ODAAAlEZ35VVpPXv2tP7duHFjNWnSRLVq1VJiYqIeffTRHP1Hjx6tmJgY63J6ejrhCACAe5BD9xj5+PjI2dlZqampNu2pqany8/Ozu46fn1+B+ktSzZo15ePjo0OHDtm939XVVRUqVLC5AQCAe49Dg5GLi4tCQkKUkJBgbcvKylJCQoLCwsLsrhMWFmbTX5LWrl2ba39JOnnypM6fP6+qVasWTeEAAOCu5PCr0mJiYjRv3jwtWrRI+/fv16BBg3T58mX1799fktSnTx+NHj3a2n/YsGGKj4/X1KlTdeDAAY0dO1bbt29XdHS0JOnSpUsaOXKktmzZomPHjikhIUFdu3ZV7dq1FRkZ6ZA5AgCA0sHh5xj16NFDZ8+e1ZgxY5SSkqKmTZsqPj7eeoL18ePH5eT0v/zWqlUrffLJJ3rttdf06quvqk6dOlq5cqUeeOABSZKzs7N++OEHLVq0SGlpafL391eHDh00YcIEubq6OmSOAACgdHB4MJKk6Oho6x6fmyUmJuZo69atm7p162a3v7u7u9asWVOU5QEAgHuEww+lAQAAlBQEIwAAABPBCAAAwEQwAgAAMBGMAAAATAQjAAAAE8EIAADARDACAAAwEYwAAABMBCMAAAATwQgAAMBEMAIAADARjAAAAEwEIwAAABPBCAAAwEQwAgAAMBGMAAAATAQjAAAAE8EIAADARDACAAAwEYwAAABMBCMAAAATwQgAAMBEMAIAADARjAAAAEwEIwAAABPBCAAAwEQwAgAAMBGMAAAATAQjAAAAE8EIAADARDACAAAwlXF0AQBsBY1aXSTjHJvYuUjGAYB7CXuMAAAATAQjAAAAE8EIAADARDACAAAwEYwAAABMBCMAAAATwQgAAMBEMAIAADARjAAAAEwEIwAAABPBCAAAwEQwAgAAMBGMAAAATAQjAAAAE8EIAADARDACAAAwEYwAAABMBCMAAABTGUcXAKD4BI1aXSTjHJvYuUjGAYCShj1GAAAAJoIRAACAiWAEAABgKhHBaNasWQoKCpKbm5tCQ0O1bdu2PPsvW7ZM9evXl5ubmxo3bqyvvvrK5n7DMDRmzBhVrVpV7u7uioiI0MGDB+/kFAAAwF3A4cFo6dKliomJUWxsrHbu3Kng4GBFRkbqzJkzdvt/++236tWrl5577jnt2rVLUVFRioqK0p49e6x9Jk+erBkzZmju3LnaunWrPDw8FBkZqatXrxbXtAAAQCnk8KvSpk2bpgEDBqh///6SpLlz52r16tWaP3++Ro0alaP/u+++q44dO2rkyJGSpAkTJmjt2rV67733NHfuXBmGoenTp+u1115T165dJUmLFy+Wr6+vVq5cqZ49exbf5IB7CFe8AbgbOHSPUUZGhnbs2KGIiAhrm5OTkyIiIpSUlGR3naSkJJv+khQZGWntf/ToUaWkpNj08fLyUmhoaK5jAgAASA7eY3Tu3DllZmbK19fXpt3X11cHDhywu05KSord/ikpKdb7s9ty63Oza9eu6dq1a9blCxcuSJLS09MLMJv8y7p2pUjGubm+ohr3To5t7zktjWPzXBfv2A/ErimSsfeMiyyScQCUTNn//zAMo9BjOPxQWkkQFxencePG5WgPCAhwQDX55zW99I1dGmsurWOXxppL89gASo6LFy/Ky8urUOs6NBj5+PjI2dlZqampNu2pqany8/Ozu46fn1+e/bP/m5qaqqpVq9r0adq0qd0xR48erZiYGOtyVlaWfvnlF1WqVEkWi6XA87pd6enpCggI0IkTJ1ShQoVif/ziwBxLv7t9fhJzvFswx7tDfuZoGIYuXrwof3//Qj+OQ4ORi4uLQkJClJCQoKioKEm/h5KEhARFR0fbXScsLEwJCQkaPny4tW3t2rUKCwuTJNWoUUN+fn5KSEiwBqH09HRt3bpVgwYNsjumq6urXF1dbdq8vb1va25FoUKFCnftCzwbcyz97vb5SczxbsEc7w63mmNh9xRlc/ihtJiYGPXt21ctWrRQy5YtNX36dF2+fNl6lVqfPn1UrVo1xcXFSZKGDRum8PBwTZ06VZ07d9aSJUu0fft2ffDBB5Iki8Wi4cOH64033lCdOnVUo0YNvf766/L397eGLwAAAHscHox69Oihs2fPasyYMUpJSVHTpk0VHx9vPXn6+PHjcnL638VzrVq10ieffKLXXntNr776qurUqaOVK1fqgQcesPZ5+eWXdfnyZT3//PNKS0tTmzZtFB8fLzc3t2KfHwAAKD0cHowkKTo6OtdDZ4mJiTnaunXrpm7duuU6nsVi0fjx4zV+/PiiKrFYubq6KjY2NsfhvbsJcyz97vb5SczxbsEc7w7FNUeLcTvXtAEAANxFHP6TIAAAACUFwQgAAMBEMAIAADARjAAAAEwEIweZNWuWgoKC5ObmptDQUG3bti3P/suWLVP9+vXl5uamxo0b66uvviqmSgsuLi5ODz74oMqXL68qVaooKipKycnJea6zcOFCWSwWm1tJ/nqFsWPH5qi3fv36ea5TmrahJAUFBeWYo8Vi0ZAhQ+z2Lw3bcNOmTerSpYv8/f1lsVi0cuVKm/sNw9CYMWNUtWpVubu7KyIiQgcPHrzluAV9P99Jec3x+vXreuWVV9S4cWN5eHjI399fffr00alTp/IcszCv9zvlVtuwX79+OWrt2LHjLcctLdtQkt33pcVi0dtvv53rmCVpG0r5+5y4evWqhgwZokqVKsnT01NPP/10jl++uFlh38N/RDBygKVLlyomJkaxsbHauXOngoODFRkZqTNnztjt/+2336pXr1567rnntGvXLkVFRSkqKkp79uwp5srzZ+PGjRoyZIi2bNmitWvX6vr16+rQoYMuX76c53oVKlTQ6dOnrbeffvqpmCounEaNGtnU+8033+Tat7RtQ0n67rvvbOa3du1aScrzqzJK+ja8fPmygoODNWvWLLv3T548WTNmzNDcuXO1detWeXh4KDIyUlevXs11zIK+n++0vOZ45coV7dy5U6+//rp27typ5cuXKzk5WU888cQtxy3I6/1OutU2lKSOHTva1Prpp5/mOWZp2oaSbOZ2+vRpzZ8/XxaLRU8//XSe45aUbSjl73PixRdf1BdffKFly5Zp48aNOnXqlJ566qk8xy3MezgHA8WuZcuWxpAhQ6zLmZmZhr+/vxEXF2e3f/fu3Y3OnTvbtIWGhhoDBw68o3UWlTNnzhiSjI0bN+baZ8GCBYaXl1fxFXWbYmNjjeDg4Hz3L+3b0DAMY9iwYUatWrWMrKwsu/eXtm0oyVixYoV1OSsry/Dz8zPefvtta1taWprh6upqfPrpp7mOU9D3c3G6eY72bNu2zZBk/PTTT7n2KejrvbjYm1/fvn2Nrl27Fmic0r4Nu3btajzyyCN59imp2zDbzZ8TaWlpRtmyZY1ly5ZZ++zfv9+QZCQlJdkdo7Dv4Zuxx6iYZWRkaMeOHYqIiLC2OTk5KSIiQklJSXbXSUpKsukvSZGRkbn2L2kuXLggSbrvvvvy7Hfp0iUFBgYqICBAXbt21d69e4ujvEI7ePCg/P39VbNmTfXu3VvHjx/PtW9p34YZGRn66KOP9Oyzz+b5w8qlbRv+0dGjR5WSkmKznby8vBQaGprrdirM+7mkuXDhgiwWyy1/H7Igr3dHS0xMVJUqVVSvXj0NGjRI58+fz7Vvad+GqampWr16tZ577rlb9i3J2/Dmz4kdO3bo+vXrNtulfv36uv/++3PdLoV5D9tDMCpm586dU2ZmpvUnT7L5+voqJSXF7jopKSkF6l+SZGVlafjw4WrdurXNz7bcrF69epo/f75WrVqljz76SFlZWWrVqpVOnjxZjNXmX2hoqBYuXKj4+HjNmTNHR48eVdu2bXXx4kW7/UvzNpSklStXKi0tTf369cu1T2nbhjfL3hYF2U6FeT+XJFevXtUrr7yiXr165fmjnAV9vTtSx44dtXjxYiUkJGjSpEnauHGjOnXqpMzMTLv9S/s2XLRokcqXL3/LQ0wleRva+5xISUmRi4tLjsB+q8/K7D75XceeEvGTILh7DRkyRHv27LnlseywsDCFhYVZl1u1aqUGDRro/fff14QJE+50mQXWqVMn699NmjRRaGioAgMD9dlnn+XrX26lzYcffqhOnTrJ398/1z6lbRve665fv67u3bvLMAzNmTMnz76l6fXes2dP69+NGzdWkyZNVKtWLSUmJurRRx91YGV3xvz589W7d+9bXuhQkrdhfj8nigt7jIqZj4+PnJ2dc5xZn5qaKj8/P7vr+Pn5Fah/SREdHa0vv/xSGzZsUPXq1Qu0btmyZdWsWTMdOnToDlVXtLy9vVW3bt1c6y2t21CSfvrpJ61bt05/+9vfCrReaduG2duiINupMO/nkiA7FP30009au3ZtnnuL7LnV670kqVmzpnx8fHKttbRuQ0n6z3/+o+Tk5AK/N6WSsw1z+5zw8/NTRkaG0tLSbPrf6rMyu09+17GHYFTMXFxcFBISooSEBGtbVlaWEhISbP61/UdhYWE2/SVp7dq1ufZ3NMMwFB0drRUrVmj9+vWqUaNGgcfIzMzUjz/+qKpVq96BCovepUuXdPjw4VzrLW3b8I8WLFigKlWqqHPnzgVar7Rtwxo1asjPz89mO6Wnp2vr1q25bqfCvJ8dLTsUHTx4UOvWrVOlSpUKPMatXu8lycmTJ3X+/Plcay2N2zDbhx9+qJCQEAUHBxd4XUdvw1t9ToSEhKhs2bI22yU5OVnHjx/PdbsU5j2cW3EoZkuWLDFcXV2NhQsXGvv27TOef/55w9vb20hJSTEMwzCeeeYZY9SoUdb+mzdvNsqUKWNMmTLF2L9/vxEbG2uULVvW+PHHHx01hTwNGjTI8PLyMhITE43Tp09bb1euXLH2uXmO48aNM9asWWMcPnzY2LFjh9GzZ0/Dzc3N2Lt3ryOmcEsvvfSSkZiYaBw9etTYvHmzERERYfj4+BhnzpwxDKP0b8NsmZmZxv3332+88sorOe4rjdvw4sWLxq5du4xdu3YZkoxp06YZu3btsl6RNXHiRMPb29tYtWqV8cMPPxhdu3Y1atSoYfz222/WMR555BFj5syZ1uVbvZ+LW15zzMjIMJ544gmjevXqxu7du23en9euXbOOcfMcb/V6Lynzu3jxojFixAgjKSnJOHr0qLFu3TqjefPmRp06dYyrV6/mOr/StA2zXbhwwShXrpwxZ84cu2OU5G1oGPn7nHjhhReM+++/31i/fr2xfft2IywszAgLC7MZp169esby5cuty/l5D98KwchBZs6cadx///2Gi4uL0bJlS2PLli3W+8LDw42+ffva9P/ss8+MunXrGi4uLkajRo2M1atXF3PF+SfJ7m3BggXWPjfPcfjw4dbnw9fX13jssceMnTt3Fn/x+dSjRw+jatWqhouLi1GtWjWjR48exqFDh6z3l/ZtmG3NmjWGJCM5OTnHfaVxG27YsMHuazN7HllZWcbrr79u+Pr6Gq6ursajjz6aY+6BgYFGbGysTVte7+filtccjx49muv7c8OGDdYxbp7jrV7vxSmv+V25csXo0KGDUblyZaNs2bJGYGCgMWDAgBwBpzRvw2zvv/++4e7ubqSlpdkdoyRvQ8PI3+fEb7/9ZgwePNioWLGiUa5cOePJJ580Tp8+nWOcP66Tn/fwrVjMgQEAAO55nGMEAABgIhgBAACYCEYAAAAmghEAAICJYAQAAGAiGAEAAJgIRgAAACaCEYAS5dixY7JYLNq9e7ejS7E6cOCAHnroIbm5ualp06aOLkeSFBQUpOnTpzu6DOCuQzACYKNfv36yWCyaOHGiTfvKlStlsVgcVJVjxcbGysPDQ8nJyTl+8+6PUlJSNGzYMNWuXVtubm7y9fVV69atNWfOHF25cqUYKwZQWGUcXQCAksfNzU2TJk3SwIEDVbFiRUeXUyQyMjLk4uJSqHUPHz6szp07KzAwMNc+R44cUevWreXt7a233npLjRs3lqurq3788Ud98MEHqlatmp544onClg+gmLDHCEAOERER8vPzU1xcXK59xo4dm+Ow0vTp0xUUFGRd7tevn6KiovTWW2/J19dX3t7eGj9+vG7cuKGRI0fqvvvuU/Xq1bVgwYIc4x84cECtWrWSm5ubHnjgAW3cuNHm/j179qhTp07y9PSUr6+vnnnmGZ07d856f/v27RUdHa3hw4fLx8dHkZGRdueRlZWl8ePHq3r16nJ1dVXTpk0VHx9vvd9isWjHjh0aP368LBaLxo4da3ecwYMHq0yZMtq+fbu6d++uBg0aqGbNmuratatWr16tLl26WPseP35cXbt2laenpypUqKDu3bsrNTXVev/hw4fVtWtX+fr6ytPTUw8++KDWrVtn93Gl33+pfOzYsbr//vvl6uoqf39/DR06NNf+AHJHMAKQg7Ozs9566y3NnDlTJ0+evK2x1q9fr1OnTmnTpk2aNm2aYmNj9fjjj6tixYraunWrXnjhBQ0cODDH44wcOVIvvfSSdu3apbCwMHXp0kXnz5+XJKWlpemRRx5Rs2bNtH37dsXHxys1NVXdu3e3GWPRokVycXHR5s2bNXfuXLv1vfvuu5o6daqmTJmiH374QZGRkXriiSd08OBBSdLp06fVqFEjvfTSSzp9+rRGjBiRY4zz58/r66+/1pAhQ+Th4WH3cbIPQ2ZlZalr16765ZdftHHjRq1du1ZHjhxRjx49rH0vXbqkxx57TAkJCdq1a5c6duyoLl266Pjx43bH/vzzz/XOO+/o/fff18GDB7Vy5Uo1btzYbl8At1Don8YFcFfq27ev0bVrV8MwDOOhhx4ynn32WcMwDGPFihXGH/+XERsbawQHB9us+8477xiBgYE2YwUGBhqZmZnWtnr16hlt27a1Lt+4ccPw8PAwPv30U8MwDOsvwE+cONHa5/r160b16tWNSZMmGYZhGBMmTDA6dOhg89gnTpwwJFl/STs8PNxo1qzZLefr7+9vvPnmmzZtDz74oDF48GDrcnBwcI5fY/+jLVu2GJKM5cuX27RXqlTJ8PDwMDw8PIyXX37ZMAzD+Prrrw1nZ2fj+PHj1n579+41JBnbtm3L9TEaNWpkzJw507ocGBhovPPOO4ZhGMbUqVONunXrGhkZGbecL4C8sccIQK4mTZqkRYsWaf/+/YUeo1GjRnJy+t//anx9fW32Zjg7O6tSpUo6c+aMzXphYWHWv8uUKaMWLVpY6/j++++1YcMGeXp6Wm/169eX9PthqGwhISF51paenq5Tp06pdevWNu2tW7e+rTln27Ztm3bv3q1GjRrp2rVrkqT9+/crICBAAQEB1n4NGzaUt7e39TEvXbqkESNGqEGDBvL29panp6f279+f6x6jbt266bffflPNmjU1YMAArVixQjdu3Ljt+oF7EcEIQK7atWunyMhIjR49Osd9Tk5OMgzDpu369es5+pUtW9Zm2WKx2G3LysrKd12XLl1Sly5dtHv3bpvbwYMH1a5dO2u/3A5rFbXatWvLYrEoOTnZpr1mzZqqXbu23N3dCzTeiBEjtGLFCr311lv6z3/+o927d6tx48bKyMiw2z8gIEDJycmaPXu23N3dNXjwYLVr187u9gCQN4IRgDxNnDhRX3zxhZKSkmzaK1eurJSUFJtwVJTfPbRlyxbr3zdu3NCOHTvUoEEDSVLz5s21d+9eBQUFqXbt2ja3goShChUqyN/fX5s3b7Zp37x5sxo2bJjvcSpVqqQ//elPeu+993T58uU8+zZo0EAnTpzQiRMnrG379u1TWlqa9TE3b96sfv366cknn1Tjxo3l5+enY8eO5Tmuu7u7unTpohkzZigxMVFJSUn68ccf8z0HAL8jGAHIU+PGjdW7d2/NmDHDpr19+/Y6e/asJk+erMOHD2vWrFn697//XWSPO2vWLK1YsUIHDhzQkCFD9Ouvv+rZZ5+VJA0ZMkS//PKLevXqpe+++06HDx/WmjVr1L9/f2VmZhbocUaOHKlJkyZp6dKlSk5O1qhRo7R7924NGzasQOPMnj1bN27cUIsWLbR06VLt379fycnJ+uijj3TgwAE5OztL+v2Kv+zndOfOndq2bZv69Omj8PBwtWjRQpJUp04dLV++XLt379b333+vv/zlL3nuUVu4cKE+/PBD7dmzR0eOHNFHH30kd3f3PL9eAIB9BCMAtzR+/PgcH8wNGjTQ7NmzNWvWLAUHB2vbtm12r9gqrIkTJ2rixIkKDg7WN998o3/961/y8fGRJOtenszMTHXo0EGNGzfW8OHD5e3tbXM+U34MHTpUMTExeumll9S4cWPFx8frX//6l+rUqVOgcWrVqqVdu3YpIiJCo0ePVnBwsFq0aKGZM2dqxIgRmjBhgqTfDxuuWrVKFStWVLt27RQREaGaNWtq6dKl1rGmTZumihUrqlWrVurSpYsiIyPVvHnzXB/b29tb8+bNU+vWrdWkSROtW7dOX3zxhSpVqlSgOQCQLMbNJwkAAADco9hjBAAAYCIYAQAAmAhGAAAAJoIRAACAiWAEAABgIhgBAACYCEYAAAAmghEAAICJYAQAAGAiGAEAAJgIRgAAACaCEQAAgOn/A7o09cPW3rbTAAAAAElFTkSuQmCC", + "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", + "\n", + "X = range(0,n)\n", + "\n", + "plt.bar(X, poisson_dist.pmf(X))\n", + "plt.xlabel('Number of Goals')\n", + "plt.ylabel('Probability')\n", + "plt.title('Poisson Probability Plot')\n", + "plt.show()\n" ] } ], @@ -192,7 +299,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.11.4" } }, "nbformat": 4, diff --git a/your-code/pop_sample_CLT.ipynb b/your-code/pop_sample_CLT.ipynb new file mode 100644 index 0000000..c817326 --- /dev/null +++ b/your-code/pop_sample_CLT.ipynb @@ -0,0 +1,269 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sample Mean variation" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "47.021" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "### randomly generate a population of ages of people\n", + "population=np.random.randint(0,95,10000)\n", + "\n", + "np.mean(population)" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "55.6" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "##small-ish sample size - large variation due to sampling\n", + "\n", + "pop_sample=np.random.choice(population,10)#Picks 10 elements at random\n", + "np.mean(pop_sample)" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "42.05" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "##larger sample size - large variation due to sampling\n", + "\n", + "pop_sample=np.random.choice(population,100)#Picks 100 elements at random\n", + "np.mean(pop_sample)" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "27.381993335036803" + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "true_sigma=population.std(ddof=0)\n", + "true_sigma" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "30.80476153670619" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop_sample=np.random.choice(population,10)\n", + "pop_sample.std(ddof=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "29.070188485557825" + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pop_sample=np.random.choice(population,100)\n", + "pop_sample.std(ddof=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plotting the popullation distribution\n", + "plt.hist(population)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dist_of_means=[]\n", + "\n", + "for i in range(1000):\n", + " pop_sample=np.random.choice(population,10)\n", + " dist_of_means.append(np.mean(pop_sample))\n", + "plt.hist(dist_of_means)\n", + "dist_of_means_100=[]\n", + "\n", + "for i in range(1000):\n", + " pop_sample=np.random.choice(population,100)\n", + " dist_of_means_100.append(np.mean(pop_sample))\n", + "plt.hist(dist_of_means_100)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "47.346599999999995\n", + "47.054809999999996\n" + ] + } + ], + "source": [ + "print(np.mean(dist_of_means))\n", + "print(np.mean(dist_of_means_100))" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "47.021" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#remenber population mean\n", + "np.mean(population)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +}