1_Calculate the sampling size_SRB_V1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "456f9c34",
   "metadata": {},
   "source": [
    "# Rapid assessment Sruvey of the face to face questionnaire based random sampling on the cetacean bycatch condcuted from 150 fishermen from 3 main villages of Pakistan"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aff99f6",
   "metadata": {},
   "source": [
    "# Calculate the sampling size\n",
    "https://www.statskingdom.com/50_ci_sample_size.html for reference "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7193fb29",
   "metadata": {},
   "source": [
    "estimate the sampling size of the rapid assessment of the face to face questionnaire based random sampling survey that had condcuted from 150 fishermen from 3 main villages on the cetacean bycatch assessment where the population of the fishemen is unknown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b209dedd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from pandas.api.types import is_string_dtype, is_numeric_dtype\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a8507132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The estimated sample size is: 109\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "def estimate_sample_size(population_size, confidence_level, margin_of_error):\n",
    "    z_score = get_z_score(confidence_level)\n",
    "    p = 0.5  # Conservative estimate of proportion\n",
    "    sample_size = (z_score**2 * p * (1 - p)) / (margin_of_error**2)\n",
    "    sample_size_corrected = sample_size / (1 + (sample_size - 1) / population_size)\n",
    "    return math.ceil(sample_size_corrected)\n",
    "\n",
    "def get_z_score(confidence_level):\n",
    "    if confidence_level == 0.90:\n",
    "        return 1.645\n",
    "    elif confidence_level == 0.95:\n",
    "        return 1.96\n",
    "    elif confidence_level == 0.99:\n",
    "        return 2.576\n",
    "    else:\n",
    "        raise ValueError(\"Invalid confidence level. Choose from 0.90, 0.95, or 0.99.\")\n",
    "\n",
    "# Example usage\n",
    "population_size = 150  # Unknown population size, so choose a conservative estimate\n",
    "confidence_level = 0.95  # Choose desired confidence level (e.g., 95%)\n",
    "margin_of_error = 0.05  # Desired margin of error (e.g., ±5%)\n",
    "\n",
    "sample_size = estimate_sample_size(population_size, confidence_level, margin_of_error)\n",
    "print(f\"The estimated sample size is: {sample_size}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b4f213e",
   "metadata": {},
   "source": [
    "# Required Sample Size Estimation - Option 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d48705e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculated sample size: 385\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "# Function to calculate sample size\n",
    "def calculate_sample_size(confidence_level, margin_of_error, proportion):\n",
    "    z_score = get_z_score(confidence_level)\n",
    "    sample_size = (z_score**2 * proportion * (1 - proportion)) / (margin_of_error**2)\n",
    "    return math.ceil(sample_size)\n",
    "\n",
    "# Function to get Z-score corresponding to the desired confidence level\n",
    "def get_z_score(confidence_level):\n",
    "    if confidence_level == 0.90:\n",
    "        return 1.645\n",
    "    elif confidence_level == 0.95:\n",
    "        return 1.96\n",
    "    elif confidence_level == 0.99:\n",
    "        return 2.576\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# Specify parameters\n",
    "confidence_level = 0.95\n",
    "margin_of_error = 0.05\n",
    "proportion = 0.5\n",
    "\n",
    "# Calculate sample size\n",
    "calculated_sample_size = calculate_sample_size(confidence_level, margin_of_error, proportion)\n",
    "\n",
    "# Display the results\n",
    "print(f\"Calculated sample size: {calculated_sample_size}\")\n",
    "\n",
    "# Plot the results on a graph\n",
    "sample_sizes = range(10, 1000, 10)\n",
    "required_sample_sizes = [calculate_sample_size(confidence_level, margin_of_error, proportion) for _ in sample_sizes]\n",
    "\n",
    "plt.plot(sample_sizes, required_sample_sizes)\n",
    "plt.xlabel('Sample Size')\n",
    "plt.ylabel('Required Sample Size')\n",
    "plt.title('Sample Size Estimation')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82594cfb",
   "metadata": {},
   "source": [
    "In the above code, the estimate_sample_size function calculates the required sample size using the sample size formula, and the get_z_score function returns the appropriate Z-score for the chosen confidence level. You can adjust the values of population_size, confidence_level, and margin_of_error to fit your specific survey requirements.\n",
    "\n",
    "Please note that the code assumes a conservative estimate of 0.5 for the proportion, as mentioned earlier. Adjust this value if you have a different estimate or any prior information about the proportion of interest."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a939cd07",
   "metadata": {},
   "source": [
    "# Estimate the required sampling size - option 2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5da451f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The required sample size is: 278\n"
     ]
    }
   ],
   "source": [
    "#import math\n",
    "\n",
    "def estimate_sample_size(sample_size, confidence_level, margin_of_error):\n",
    "    z_score = get_z_score(confidence_level)\n",
    "    p = 0.5  # Conservative estimate of proportion\n",
    "    population_size = estimate_population_size(sample_size)\n",
    "    sample_size_required = (z_score**2 * p * (1 - p)) / (margin_of_error**2)\n",
    "    sample_size_required_corrected = sample_size_required / (1 + (sample_size_required - 1) / population_size)\n",
    "    return math.ceil(sample_size_required_corrected)\n",
    "\n",
    "def estimate_population_size(sample_size):\n",
    "    # Make a conservative assumption about the population size\n",
    "    # If you have any prior information, adjust this value accordingly\n",
    "    return 1000\n",
    "\n",
    "def get_z_score(confidence_level):\n",
    "    if confidence_level == 0.90:\n",
    "        return 1.645\n",
    "    elif confidence_level == 0.95:\n",
    "        return 1.96\n",
    "    elif confidence_level == 0.99:\n",
    "        return 2.576\n",
    "    else:\n",
    "        raise ValueError(\"Invalid confidence level. Choose from 0.90, 0.95, or 0.99.\")\n",
    "\n",
    "# Example usage\n",
    "sample_size = 150\n",
    "confidence_level = 0.95\n",
    "margin_of_error = 0.05\n",
    "\n",
    "required_sample_size = estimate_sample_size(sample_size, confidence_level, margin_of_error)\n",
    "print(f\"The required sample size is: {required_sample_size}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c66be335",
   "metadata": {},
   "source": [
    "In this code, the estimate_sample_size function estimates the required sample size by using the sample size formula. The estimate_population_size function makes a conservative assumption about the population size. You can adjust the value in this function based on your knowledge or assumptions about the population. The get_z_score function retrieves the appropriate Z-score for the chosen confidence level.\n",
    "\n",
    "Adjust the values of sample_size, confidence_level, and margin_of_error to fit your specific survey requirements. The code will output the required sample size based on these inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "334391f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The required sample size is: 278\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ozs4VWr/ycHZ2Rnh4OLKzs9X6vHLlilpd0Z4/fX19rb0fivbEXbx4sVgwerrG3Nxcq+/Dp5cRGxuLjh07qh1GK0ndunXRunVrbN++HePGjcPu3bvRs2dPtXs0aeO9SfLEw1JULR07dqzE/0r3798P4L9d/UX/0T5Zm5GRgQ0bNlRab2FhYdLPQgiEhYVBX18fHTt2LLFeV1cXvXv3xo8//oiLFy8WG1/SJb1PunbtGhITE4sNT09Px+nTp2FpaVnqf7DZ2dno1asXHBwcsGnTphI/cPr06YPCwkLMnTu32LiCgoJyndPTu3dv6Orq4ocffsDOnTvRrVs3tbv4ZmZmoqCgQG0aT09P6OjoqF0iXJLPP/8c9+/fx/Dhw4vNAwB+++037N27FwDQtWtXJCUlqZ3LUVBQgBUrVsDU1BS+vr7PXJeK6tq1KwoKCtRuQ1BYWFjsjto2NjZ48803sXr1aimkP+lZ74eSdO7cGWZmZggNDcWjR4/UxhX9brRq1Qr169fHV199hezsbK0s92l9+vTB7du3sXbt2mLjHj58iAcPHqgNCwgIwO+//47169fj3r17aoekiub3vO9NkifuuaFqafz48cjJyUGvXr3g6uqKvLw8nDp1Ctu3b4eLi4t0rkrnzp1hYGCA7t27Y9SoUcjOzsbatWthY2NT4gfH8zIyMsLBgwcRGBiINm3a4MCBA9i3bx8+/fTTMneRz58/H8eOHUObNm0wYsQIuLu7Iy0tDTExMThy5AjS0tJKnTY2Nhb9+/fH22+/jTfeeANWVla4ffs2Nm3ahDt37mDZsmWlHrb4/PPP8ffff2PGjBn4+eef1cbVr18fPj4+8PX1xahRoxAaGorz58+jc+fO0NfXx7Vr17Bz504sX75c7b4jJbGxscFbb72FJUuWICsrq9iH1NGjRzFu3Dh88MEHaNSoEQoKCrB582Yp+JUlICAAFy5cwLx583Du3Dn069dPukPxwYMHER4eLt3LZeTIkVi9ejUGDx6M6OhouLi4YNeuXYiMjMSyZcvKfWJyRXTv3h3t2rXD9OnTkZCQAHd3d+zevbvE81hWrlyJ119/HZ6enhgxYgTq1auH5ORknD59Gv/88w9iY2M1Wra5uTmWLl2K4cOHw9vbG/3794elpSViY2ORk5ODTZs2QUdHB+vWrcPbb7+NJk2aYMiQIXBwcMDt27dx7NgxmJub49dff33mssLDw4sFKADo2bMnBg4ciB07duCjjz7CsWPH0K5dOxQWFuLy5cvYsWMHDh06JO19BR6HlylTpmDKlCmwsrIqtkdJG+9NkqkqukqL6LkcOHBADB06VLi6ugpTU1NhYGAgGjRoIMaPHy+Sk5PVan/55RfRtGlTYWRkJFxcXMSCBQvE+vXri10S7ezsLN135UkAxNixY9WGlXTZa2BgoKhRo4a4fv26dC8PW1tbERISonY/nKJ5PnkpuBBCJCcni7FjxwpHR0ehr68v7OzsRMeOHcWaNWvKfC2Sk5PF/Pnzha+vr7C3txd6enrC0tJSdOjQQezatUut9ulLwYsuXy/pERgYqDbtmjVrRKtWrYSxsbEwMzMTnp6eYurUqeLOnTtl9ldk7dq1AoAwMzMrdjnyjRs3xNChQ0X9+vWFkZGRsLKyEm+99ZY4cuRIueYtxON7tPTo0UPY2NgIPT09YW1tLbp37y5+/vnnYq/XkCFDRK1atYSBgYHw9PRUuwxbiLIva35625X3UnAhhLh//74YOHCgMDc3F0qlUgwcOFC6DP7pHq5fvy4GDRok7OzshL6+vnBwcBDdunVT26alLfvYsWPFblUgxOPfhbZt2wpjY2Nhbm4uWrduLX744Qe1mnPnzon33ntP1KxZUxgaGgpnZ2fRp08fER4eXuy1KOk1K+2xefNmIcTjS+8XLFggmjRpIgwNDYWlpaVo1aqV+Pzzz0VGRkax+bZr104AEMOHDy912eV5b/JS8FeLQohKPDOO6BUyePBg7Nq1q8Rd+kRE9OLwnBsiIiKSFYYbIiIikhWGGyIiIpIVnnNDREREssI9N0RERCQrDDdEREQkK6/cTfxUKhXu3LkDMzOzZ97+m4iIiF4OQghkZWWhdu3axb789mmvXLi5c+dOsW94JiIiourh1q1bpX6ZbpFXLtwU3V791q1bMDc3r+JuiIiIqDwyMzPh6OhYrq9JeeXCTdGhKHNzc4YbIiKiaqY8p5TwhGIiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpKVV+6LMyuLEAIP8wurug0iIqKXgrG+brm+5LIyMNxoycP8QrjPOlTVbRAREb0U/p7jDxODqokZPCxFREREssI9N1pirK+Lv+f4V3UbRERELwVjfd0qWzbDjZYoFIoq2/1GRERE/+FhKSIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpKVKg83K1euhIuLC4yMjNCmTRtERUWVWb9s2TI0btwYxsbGcHR0xOTJk/Ho0aMX1C0RERG97Ko03Gzfvh1BQUEICQlBTEwMmjVrBn9/f6SkpJRYv3XrVkyfPh0hISG4dOkSvv32W2zfvh2ffvrpC+6ciIiIXlZVGm6WLFmCESNGYMiQIXB3d8eqVatgYmKC9evXl1h/6tQptGvXDv3794eLiws6d+6Mfv36PXNvDxEREb06qizc5OXlITo6Gn5+fv81o6MDPz8/nD59usRp2rZti+joaCnM3LhxA/v370fXrl1LXU5ubi4yMzPVHkRERCRfelW14Hv37qGwsBC2trZqw21tbXH58uUSp+nfvz/u3buH119/HUIIFBQU4KOPPirzsFRoaCg+//xzrfZOREREL68qP6FYExEREfjyyy/xf//3f4iJicHu3buxb98+zJ07t9RpgoODkZGRIT1u3br1AjsmIiKiF63K9tzUqlULurq6SE5OVhuenJwMOzu7EqeZOXMmBg4ciOHDhwMAPD098eDBA4wcORKfffYZdHSKZzVDQ0MYGhpqfwWIiIjopVRle24MDAzQqlUrhIeHS8NUKhXCw8Ph4+NT4jQ5OTnFAoyuri4AQAhRec0SERFRtVFle24AICgoCIGBgfDy8kLr1q2xbNkyPHjwAEOGDAEADBo0CA4ODggNDQUAdO/eHUuWLEGLFi3Qpk0bxMXFYebMmejevbsUcoiIiOjVVqXhJiAgAKmpqZg1axaSkpLQvHlzHDx4UDrJODExUW1PzYwZM6BQKDBjxgzcvn0b1tbW6N69O+bNm1dVq0BEREQvGYV4xY7nZGZmQqlUIiMjA+bm5lXdDhEREZWDJp/f1epqKSIiIqJnYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIlnRK09RixYtoFAoyjXDmJiY52qIiIiI6HmUK9z07NmzktsgIiIi0g6FEEJUdRMvUmZmJpRKJTIyMmBubl7V7RAREVE5aPL5XaFzbtLT07Fu3ToEBwcjLS0NwOPDUbdv367I7IiIiIi0plyHpZ70559/ws/PD0qlEgkJCRgxYgSsrKywe/duJCYm4rvvvquMPomIiIjKReM9N0FBQRg8eDCuXbsGIyMjaXjXrl3xv//9T6vNEREREWlK43Bz5swZjBo1qthwBwcHJCUlaaUpIiIioorSONwYGhoiMzOz2PCrV6/C2tpaK00RERERVZTG4ebdd9/FnDlzkJ+fDwBQKBRITEzEtGnT0Lt3b603SERERKQJjcPN4sWLkZ2dDRsbGzx8+BC+vr5o0KABzMzMMG/evMrokYiIiKjcNL5aSqlU4vDhwzh58iT+/PNPZGdno2XLlvDz86uM/oiIiIg0ovFN/G7dugVHR8fK6qfS8SZ+RERE1U+l3sTPxcUFvr6+WLt2Lf79998KN0lERERUGTQON2fPnkXr1q0xZ84c2Nvbo2fPnti1axdyc3Mroz8iIiIijWgcblq0aIFFixYhMTERBw4cgLW1NUaOHAlbW1sMHTq0MnokIiIiKrcKfbcU8PgS8Lfeegtr167FkSNHULduXWzatEnj+axcuRIuLi4wMjJCmzZtEBUVVWZ9eno6xo4dC3t7exgaGqJRo0bYv39/RVeDiIiIZKbC4eaff/7BwoUL0bx5c7Ru3RqmpqZYuXKlRvPYvn07goKCEBISgpiYGDRr1gz+/v5ISUkpsT4vLw+dOnVCQkICdu3ahStXrmDt2rVwcHCo6GoQERGRzGh8tdTq1auxdetWREZGwtXVFQMGDED//v3h7Oys8cLbtGkDb29vhIWFAQBUKhUcHR0xfvx4TJ8+vVj9qlWrsGjRIly+fBn6+voaLw/g1VJERETVUaVeLfXFF1+gTZs2iI6OxsWLFxEcHFyhYJOXl4fo6Gi1++Po6OjAz88Pp0+fLnGaX375BT4+Phg7dixsbW3h4eGBL7/8EoWFhRovn4iIiORJ45v4JSYmQqFQPPeC7927h8LCQtja2qoNt7W1xeXLl0uc5saNGzh69CgGDBiA/fv3Iy4uDmPGjEF+fj5CQkJKnCY3N1ftSq6SvheLiIiI5EPjPTcKhQInTpzAhx9+CB8fH9y+fRsAsHnzZpw8eVLrDT5JpVLBxsYGa9asQatWrRAQEIDPPvsMq1atKnWa0NBQKJVK6VGdb0BIREREz6ZxuPnxxx/h7+8PY2NjnDt3TtorkpGRgS+//LLc86lVqxZ0dXWRnJysNjw5ORl2dnYlTmNvb49GjRpBV1dXGubm5oakpCTk5eWVOE1wcDAyMjKkx61bt8rdIxEREVU/FTrnZtWqVVi7dq3aSb3t2rVDTExMuedjYGCAVq1aITw8XBqmUqkQHh4OHx+fEqdp164d4uLioFKppGFXr16Fvb09DAwMSpzG0NAQ5ubmag8iIiKSL43DzZUrV9C+fftiw5VKJdLT0zWaV1BQENauXYtNmzbh0qVLGD16NB48eIAhQ4YAAAYNGoTg4GCpfvTo0UhLS8PEiRNx9epV7Nu3D19++SXGjh2r6WoQERGRTGl8QrGdnR3i4uLg4uKiNvzkyZOoV6+eRvMKCAhAamoqZs2ahaSkJDRv3hwHDx6UTjJOTEyEjs5/+cvR0RGHDh3C5MmT0bRpUzg4OGDixImYNm2apqtBREREMqXxfW5CQ0Px/fffY/369ejUqRP279+PmzdvYvLkyZg5cybGjx9fWb1qBe9zQ0REVP1o8vmt8Z6b6dOnQ6VSoWPHjsjJyUH79u1haGiIKVOmvPTBhoiIiORP4z03RfLy8hAXF4fs7Gy4u7vDyMgIKSkpqF27trZ71CruuSEiIqp+KnXPTREDAwO4u7tLz2NjY9GyZUveLZiIiIiqVIW/OJOIiIjoZcRwQ0RERLLCcENERESyUu5zbv78888yx1+5cuW5myEiIiJ6XuUON82bN4dCoUBJF1cVDdfGt4UTEclBYWEh8vPzq7oNomrFwMBA7ea9FVXucBMfH//cCyMikjshBJKSkjT+OhoiAnR0dFC3bt1Svy+yvModbpydnZ9rQUREr4KiYGNjYwMTExPu0SYqJ5VKhTt37uDu3btwcnJ6rt+dCt/nhoiI1BUWFkrBpmbNmlXdDlG1Y21tjTt37qCgoAD6+voVng+vliIi0pKic2xMTEyquBOi6qnocNTz3hCY4YaISMt4KIqoYrT1u8NwQ0RERLJSoXBTUFCAI0eOYPXq1cjKygIA3LlzB9nZ2VptjoiI5EcIgZEjR8LKygoKhQLnz5/Hm2++iUmTJpU5nYuLC5YtW/ZCeqzOZs+ejebNm1d1G1VK43Bz8+ZNeHp6okePHhg7dixSU1MBAAsWLMCUKVO03iAREb0YSUlJGD9+POrVqwdDQ0M4Ojqie/fuCA8P1+pyDh48iI0bN2Lv3r24e/cuPDw8sHv3bsydO1ery6kKCQkJUmCrbnbu3AlXV1cYGRnB09MT+/fvf+Y0K1euhJubG4yNjdG4cWN89913auM3btwIhUKh9jAyMqqsVZBofLXUxIkT4eXlhdjYWLWrAXr16oURI0ZotTkiInoxEhIS0K5dO1hYWGDRokXw9PREfn4+Dh06hLFjx+Ly5ctaW9b169dhb2+Ptm3bSsOsrKy0Nn/S3KlTp9CvXz+EhoaiW7du2Lp1K3r27ImYmBh4eHiUOM0333yD4OBgrF27Ft7e3oiKisKIESNgaWmJ7t27S3Xm5uZq32LwIs5J03jPzYkTJzBjxoxiN9hxcXHB7du3tdYYERG9OGPGjIFCoUBUVBR69+6NRo0aoUmTJggKCsLvv/8u1SUmJqJHjx4wNTWFubk5+vTpg+TkZGl80SGRzZs3w8XFBUqlEn379pVOYRg8eDDGjx+PxMREKBQKuLi4AECxw1IpKSno3r07jI2NUbduXWzZsqVYz+np6Rg+fDisra1hbm6ODh06IDY2tty9AI/vrbJw4UI0aNAAhoaGcHJywrx586Txt27dQp8+fWBhYQErKyv06NEDCQkJFX6dVSoVQkNDUbduXRgbG6NZs2bYtWuXNK5OnTr45ptv1KY5d+4cdHR0cPPmzXKtd0UsX74cXbp0wSeffAI3NzfMnTsXLVu2RFhYWKnTbN68GaNGjUJAQADq1auHvn37YuTIkViwYIFanUKhgJ2dnfSwtbV9rl7LQ+Nwo1KpSrxE659//oGZmZlWmiIikgshBHLyCqrkUdLX5ZQkLS0NBw8exNixY1GjRo1i4y0sLAA8/vvfo0cPpKWl4fjx4zh8+DBu3LiBgIAAtfrr169jz5492Lt3L/bu3Yvjx49j/vz5AB5/iM6ZMwd16tTB3bt3cebMmRJ7Gjx4MG7duoVjx45h165d+L//+z+kpKSo1XzwwQdISUnBgQMHEB0djZYtW6Jjx45IS0srVy8AEBwcjPnz52PmzJn4+++/sXXrVunDNz8/H/7+/jAzM8OJEycQGRkJU1NTdOnSBXl5eeV6bZ8WGhqK7777DqtWrcJff/2FyZMn48MPP8Tx48eho6ODfv36YevWrWrTbNmyBe3atZNuplue9X5SREQEFApFmaHs9OnT8PPzUxvm7++P06dPlzpNbm5usUNMxsbGiIqKUvvqkezsbDg7O8PR0RE9evTAX3/9Veo8tUXjw1KdO3fGsmXLsGbNGgCPE1l2djZCQkLQtWtXrTdIRFSdPcwvhPusQ1Wy7L/n+MPE4Nl/5uPi4iCEgKura5l14eHhuHDhAuLj4+Ho6AgA+O6779CkSROcOXMG3t7eAB6HoI0bN0r/8A4cOBDh4eGYN28elEolzMzMoKurCzs7uxKXc/XqVRw4cABRUVHSPL/99lu4ublJNSdPnkRUVBRSUlJgaGgIAPjqq6+wZ88e7Nq1CyNHjnxmL1lZWVi+fDnCwsIQGBgIAKhfvz5ef/11AMD27duhUqmwbt066VDKhg0bYGFhgYiICHTu3PmZr+2TcnNz8eWXX+LIkSPw8fEBANSrVw8nT57E6tWr4evriwEDBmDx4sVITEyEk5MTVCoVtm3bhhkzZmi03k8yMTFB48aNy7wpXlJSUrE9Kra2tkhKSip1Gn9/f6xbtw49e/ZEy5YtER0djXXr1iE/Px/37t2Dvb09GjdujPXr16Np06bIyMjAV199hbZt2+Kvv/5CnTp1NHr9NKFxuFm8eDH8/f3h7u6OR48eoX///rh27Rpq1aqFH374oTJ6JCKiSlTePTyXLl2Co6OjFGwAwN3dHRYWFrh06ZIURFxcXNT25Nvb2xfb6/Ks5ejp6aFVq1bSMFdXV2kPEgDExsYiOzu72J2gHz58iOvXr0vPy+rl0qVLyM3NRceOHUvsIzY2FnFxccWOSjx69EhtGeUVFxeHnJwcdOrUSW14Xl4eWrRoAeDxl1S7ublh69atmD59Oo4fP46UlBR88MEHGq33k1q3bq3Vc6aKzJw5E0lJSXjttdcghICtrS0CAwOxcOFC6csvfXx8pCAHAG3btoWbmxtWr15dqSeQaxxu6tSpg9jYWGzfvl16kYcNG4YBAwbA2Ni4MnokIqq2jPV18fcc/ypbdnk0bNgQCoVCax+AT+8hUCgUUKlUWpl3kezsbNjb2yMiIqLYuCdDUFm9POszKzs7G61atSrxfB9ra+sK9QwA+/btg4ODg9q4or0wADBgwAAp3GzduhVdunSRwkx511tTdnZ2audOAUBycnKpe9eAx6/f+vXrsXr1aiQnJ8Pe3h5r1qyBmZlZqa+Pvr4+WrRogbi4uAr3Wh4V+m4pPT09DBgwAAMGDNB2P0REsqJQKMp1aKgqWVlZwd/fHytXrsSECROKnXeTnp4OCwsLuLm54datW7h165a09+bvv/9Geno63N3dtdaPq6srCgoKEB0dLe0NunLlito3rbds2RJJSUnQ09OTTkrWVMOGDWFsbIzw8HAMHz682PiWLVti+/btsLGxgbm5eYWW8SR3d3cYGhoiMTERvr6+pdb1798fM2bMQHR0NHbt2oVVq1ap9fS8610SHx8fhIeHq53UffjwYbW9LqXR19eXDjFt27YN3bp1k/bcPK2wsBAXLlyo9NNYND6hODQ0FOvXry82fP369cXOkCYiouph5cqVKCwsROvWrfHjjz/i2rVruHTpEr7++mvpA87Pzw+enp4YMGAAYmJiEBUVhUGDBsHX1xdeXl5a66Vx48bo0qULRo0ahT/++APR0dEYPny42p4WPz8/+Pj4oGfPnvjtt9+QkJCAU6dO4bPPPsPZs2fLtRwjIyNMmzYNU6dOxXfffYfr16/j999/x7fffgvg8R6UWrVqoUePHjhx4gTi4+MRERGBCRMm4J9//ilz3leuXMH58+fVHkZGRpgyZQomT56MTZs24fr164iJicGKFSuwadMmaVoXFxe0bdsWw4YNQ2FhId59993nWu+oqCi4urqWeUXzxIkTcfDgQSxevBiXL1/G7NmzcfbsWYwbN06qCQ4OxqBBg6TnV69exffff49r164hKioKffv2xcWLF/Hll19KNXPmzMFvv/2GGzduICYmBh9++CFu3rxZYpjUJo3DzerVq0s86axJkyZq6ZKIiKqPevXqISYmBm+99RY+/vhjeHh4oFOnTggPD5cuTVYoFPj5559haWmJ9u3bw8/PD/Xq1cP27du13s+GDRtQu3Zt+Pr64r333sPIkSNhY2MjjVcoFNi/fz/at2+PIUOGoFGjRujbty9u3ryp0aXGM2fOxMcff4xZs2bBzc0NAQEB0jk5JiYm+N///gcnJye89957cHNzw7Bhw/Do0aNn7snp27cvWrRoofZITk7G3LlzMXPmTISGhsLNzQ1dunTBvn37ULduXbXpBwwYgNjYWPTq1Ust1FVkvXNycnDlyhW1K5ie1rZtW2zduhVr1qyRLk/fs2eP2j1u7t69i8TEROl5YWEhFi9ejGbNmqFTp0549OgRTp06pbZH6d9//8WIESPg5uaGrl27IjMzE6dOndLqnr6SKER5zyT7/4yMjHDp0qViG+LGjRvSScYvs8zMTCiVSmRkZGhlNyMRUZFHjx4hPj4edevWfSF3YSWSm7J+hzT5/NZ4z42joyMiIyOLDY+MjETt2rU1nR0RERGRVml8ltuIESMwadIk5Ofno0OHDgAe3/tg6tSp+Pjjj7XeIBEREZEmNA43n3zyCe7fv48xY8ZId2gsOikrODhY6w0SERERaULjcKNQKLBgwQLMnDkTly5dgrGxMRo2bKh2jT4RERFRVanwzRdMTU2l+w8QERERvSw0DjcPHjzA/PnzER4ejpSUlGJ3nbxx44bWmiMiIiLSlMbhZvjw4Th+/DgGDhwIe3t76cvEiIiIiF4GGoebAwcOYN++fWjXrl1l9ENERET0XDS+z42lpSWsrKwqoxciIiKi56ZxuJk7dy5mzZqFnJycyuiHiIhecW+++abaFzhWlsjISHh6ekJfXx89e/as9OXRi6NxuFm8eDEOHToEW1tbeHp6omXLlmoPIiKqfgYPHgyFQoGPPvqo2LixY8dCoVBg8ODBL6SX3bt3Y+7cuZW+nKCgIDRv3hzx8fHYuHFjiTVvvvkmFApFsUdJrxO9PDQ+54bplohInhwdHbFt2zYsXbpU+rLGR48eYevWrXBycnru+efn50NfX/+ZdS/q1Ifr16/jo48+Qp06dcqsGzFiBObMmaM2zMTEpNT6ktYzLy8PBgYGGvdY0eledRrvuQkJCSnzQURE1VPLli3h6OiI3bt3S8N2794NJycntGjRQq324MGDeP3112FhYYGaNWuiW7duuH79ujQ+ISEBCoUC27dvh6+vL4yMjLBlyxYUFBRgwoQJ0nTTpk1DYGCg2j/OTx+WcnFxwZdffomhQ4fCzMwMTk5OWLNmTZnrkpubiwkTJsDGxgZGRkZ4/fXXcebMGbXe7t+/j6FDh0KhUJS65wZ4HGTs7OzUHkVf3Fjaeg4ePBg9e/bEvHnzULt2bTRu3BgAcOHCBXTo0AHGxsaoWbMmRo4ciezsbGlZpU1HmtE43ABAeno61q1bh+DgYKSlpQEAYmJicPv2ba02R0QkGw8elP549Kj8tQ8flq+2goYOHYoNGzZIz9evX48hQ4aUsDoPEBQUhLNnzyI8PBw6Ojro1atXsXufTZ8+HRMnTsSlS5fg7++PBQsWYMuWLdiwYQMiIyORmZmJPXv2PLOvxYsXw8vLC+fOncOYMWMwevRoXLlypdT6qVOn4scff8SmTZsQExODBg0awN/fH2lpaXB0dMTdu3dhbm6OZcuW4e7duwgICCj/i1SCp9cTePy9i1euXMHhw4exd+9ePHjwAP7+/rC0tMSZM2ewc+dOHDlyBOPGjVOb19PTUQUIDcXGxgpra2vRoEEDoaenJ65fvy6EEOKzzz4TAwcO1HR2L1xGRoYAIDIyMqq6FSKSmYcPH4q///5bPHz4sPhIoPRH167qtSYmpdf6+qrX1qpVcp2GAgMDRY8ePURKSoowNDQUCQkJIiEhQRgZGYnU1FTRo0cPERgYWOr0qampAoC4cOGCEEKI+Ph4AUAsW7ZMrc7W1lYsWrRIel5QUCCcnJxEjx49pGG+vr5i4sSJ0nNnZ2fx4YcfSs9VKpWwsbER33zzTYm9ZGdnC319fbFlyxZpWF5enqhdu7ZYuHChNEypVIoNGzaU9bIIX19foa+vL2rUqKH2+P7778tcz8DAQGFraytyc3OlYWvWrBGWlpYiOztbGrZv3z6ho6MjkpKSSp3uVVLW75Amn98an3MTFBSEwYMHY+HChTAzM5OGd+3aFf3799dW5iIioipgbW2Nd955Bxs3boQQAu+88w5q1apVrO7atWuYNWsW/vjjD9y7d0/aY5OYmAgPDw+pzsvLS/o5IyMDycnJaN26tTRMV1cXrVq1KrbH52lNmzaVflYoFLCzs0NKSkqJtdevX0d+fr7a/dj09fXRunVrXLp06RmvQHEDBgzAZ599pjbM1tZW7fmT61nE09NT7XyZS5cuoVmzZqhRo4Y0rF27dlCpVLhy5Yo0z6enI81pHG7OnDmD1atXFxvu4OCApKQkrTRFRCQ7T5xXUYyurvrzUj60AQA6T51NkJBQ4ZZKM3ToUOlQycqVK0us6d69O5ydnbF27VrUrl0bKpUKHh4eyMvLU6t78oP8eTx9gq5CoXhmINIWpVKJBg0alFlT0npWdN219Zq9yjQ+58bQ0BCZmZnFhl+9ehXW1tZaaYqISHZq1Cj9YWRU/tr/fxXTM2ufQ5cuXZCXl4f8/Hzp/JEn3b9/H1euXMGMGTPQsWNHuLm54d9//33mfJVKJWxtbaUTewGgsLAQMTExz9Xv0+rXrw8DAwNERkZKw/Lz83HmzBm4u7trdVmacHNzQ2xsLB48cU5UZGQkdHR0eOKwlmm85+bdd9/FnDlzsGPHDgCP03NiYiKmTZuG3r17a71BIiJ6sXR1daXDN7pP71XC4zvV16xZE2vWrIG9vT0SExMxffr0cs17/PjxCA0NRYMGDeDq6ooVK1bg33//1er3FNaoUQOjR4/GJ598AisrKzg5OWHhwoXIycnBsGHDNJ5fTk5OsSMThoaGsLS01Gg+AwYMQEhICAIDAzF79mykpqZi/PjxGDhwYLHDXPR8KnQTv+zsbNjY2ODhw4fw9fVFgwYNYGZmhnnz5lVGj0RE9IKZm5tLlzs/TUdHB9u2bUN0dDQ8PDwwefJkLFq0qFzznTZtGvr164dBgwbBx8cHpqam8Pf3h9HTe6+e0/z589G7d28MHDgQLVu2RFxcHA4dOqRxIAGAtWvXwt7eXu3Rr18/jedjYmKCQ4cOIS0tDd7e3nj//ffRsWNHhIWFaTwvKptCCCEqMuHJkyfx559/Ijs7Gy1btoSfn5+2e6sUmZmZUCqVyMjIKPUXl4ioIh49eoT4+HjUrVtX6x/WcqVSqeDm5oY+ffq8kLsS08utrN8hTT6/NT4sVeT111/H66+/XtHJiYjoFXTz5k389ttv8PX1RW5uLsLCwhAfH8+rbUmryhVuvv7663LPcMKECRVuhoiI5E1HRwcbN27ElClTIISAh4cHjhw5Ajc3t6pujWSkXOFm6dKlas9TU1ORk5MDCwsLAI/vWGxiYgIbGxuGGyIiKpWjo6PaVUxElaFcJxTHx8dLj3nz5qF58+a4dOkS0tLSkJaWhkuXLqFly5Y8XkpERERVTuOrpWbOnIkVK1aoXZPfuHFjLF26FDNmzNBqc0RE1VEFr9MgeuVp63dH43Bz9+5dFBQUFBteWFiI5ORkrTRFRFQdFd1FNycnp4o7Iaqeiu5wXdL9lTSh8dVSHTt2xKhRo7Bu3Tq0bNkSABAdHY3Ro0dXm8vBiYgqg66uLiwsLKTvPDIxMdHqzemI5EylUiE1NRUmJibQ06vwxdwAKhBu1q9fj8DAQHh5eUn/pRQUFMDf3x/r1q17rmaIiKo7Ozs7ACj1Sx2JqHQ6OjpwcnJ67n8KKnwTv6tXr+Ly5csAAFdXVzRq1Oi5GnlReBM/InoRCgsLkZ+fX9VtEFUrBgYG0Hn6y2H/vxdyE79GjRpVm0BDRPSi6erqPvd5A0RUMeUKN0FBQZg7dy5q1KiBoKCgMmuXLFmilcaIiIiIKqJc4ebcuXPS7tWYmJhSj4XxxDkiIiKqauUKN8uXL5eOb0VERFRmP0RERETPpVz3uWnRogXu3bsHAKhXrx7u379fqU0RERERVVS5wo2FhQXi4+MBAAkJCVCpVJXaFBEREVFFleuwVO/eveHr6wt7e3soFAp4eXmVehXAjRs3tNogERERkSbKFW7WrFmD9957D3FxcZgwYQJGjBgBMzOzyu6NiIiISGPlvs9Nly5dADz+qoWJEydqNdysXLkSixYtQlJSEpo1a4YVK1agdevWz5xu27Zt6NevH3r06IE9e/ZorR8iIiKqvjT+4swNGzZoNdhs374dQUFBCAkJQUxMDJo1awZ/f/9n3ro8ISEBU6ZMwRtvvKG1XoiIiKj60/jrFx48eID58+cjPDwcKSkpxU4u1vScmzZt2sDb2xthYWEAHn9xlqOjI8aPH4/p06eXOE1hYSHat2+PoUOH4sSJE0hPTy/3nht+/QIREVH1U6lfvzB8+HAcP34cAwcOlE4wrqi8vDxER0cjODhYGqajowM/Pz+cPn261OnmzJkDGxsbDBs2DCdOnChzGbm5ucjNzZWeZ2ZmVrhfIiIievlpHG4OHDiAffv2oV27ds+98Hv37qGwsBC2trZqw21tbaUv5XzayZMn8e233+L8+fPlWkZoaCg+//zz522ViIiIqgmNz7mxtLSElZVVZfTyTFlZWRg4cCDWrl2LWrVqlWua4OBgZGRkSI9bt25VcpdERERUlTTeczN37lzMmjULmzZtgomJyXMtvFatWtDV1UVycrLa8OTkZNjZ2RWrv379OhISEtC9e3dpWNE5P3p6erhy5Qrq16+vNo2hoSEMDQ2fq08iIiKqPjQON4sXL8b169dha2sLFxcX6Ovrq42PiYkp97wMDAzQqlUrhIeHo2fPngAeh5Xw8HCMGzeuWL2rqysuXLigNmzGjBnIysrC8uXL4ejoqOnqEBERkcxoHG6KQoi2BAUFITAwEF5eXmjdujWWLVuGBw8eYMiQIQCAQYMGwcHBAaGhoTAyMoKHh4fa9BYWFgBQbDgRERG9mjQONyEhIVptICAgAKmpqZg1axaSkpLQvHlzHDx4UDrJODExETo6Gp8aRERERK8oje9zUyQ6OhqXLl0CADRp0gQtWrTQamOVhfe5ISIiqn4q9T43KSkp6Nu3LyIiIqRDQunp6Xjrrbewbds2WFtbV6hpIiIiIm3Q+HjP+PHjkZWVhb/++gtpaWlIS0vDxYsXkZmZiQkTJlRGj0RERETlpvFhKaVSiSNHjsDb21tteFRUFDp37oz09HRt9qd1PCxFRERU/Wjy+a3xnhuVSlXs8m8A0NfXL/Y9U0REREQvmsbhpkOHDpg4cSLu3LkjDbt9+zYmT56Mjh07arU5IiIiIk1pHG7CwsKQmZkJFxcX1K9fH/Xr10fdunWRmZmJFStWVEaPREREROWm8dVSjo6OiImJwZEjR6Qvt3Rzc4Ofn5/WmyMiIiLSVIXvc1Nd8YRiIiKi6qdSTig+evQo3N3dkZmZWWxcRkYGmjRpghMnTmjeLREREZEWlTvcLFu2DCNGjCgxLSmVSowaNQpLlizRanNEREREmip3uImNjUWXLl1KHd+5c2dER0drpSkiIiKiiip3uElOTi7x/jZF9PT0kJqaqpWmiIiIiCqq3OHGwcEBFy9eLHX8n3/+CXt7e600RURERFRR5Q43Xbt2xcyZM/Ho0aNi4x4+fIiQkBB069ZNq80RERERaarcl4InJyejZcuW0NXVxbhx49C4cWMAwOXLl7Fy5UoUFhYiJiYGtra2ldrw8+Kl4ERERNWPJp/f5b6Jn62tLU6dOoXRo0cjODgYRZlIoVDA398fK1eufOmDDREREcmfRncodnZ2xv79+/Hvv/8iLi4OQgg0bNgQlpaWldUfERERkUY0/voFALC0tIS3t7e2eyEiIiJ6bhp/cSYRERHRy4zhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZOWlCDcrV66Ei4sLjIyM0KZNG0RFRZVau3btWrzxxhuwtLSEpaUl/Pz8yqwnIiKiV0uVh5vt27cjKCgIISEhiImJQbNmzeDv74+UlJQS6yMiItCvXz8cO3YMp0+fhqOjIzp37ozbt2+/4M6JiIjoZaQQQoiqbKBNmzbw9vZGWFgYAEClUsHR0RHjx4/H9OnTnzl9YWEhLC0tERYWhkGDBj2zPjMzE0qlEhkZGTA3N3/u/omIiKjyafL5XaV7bvLy8hAdHQ0/Pz9pmI6ODvz8/HD69OlyzSMnJwf5+fmwsrIqcXxubi4yMzPVHkRERCRfVRpu7t27h8LCQtja2qoNt7W1RVJSUrnmMW3aNNSuXVstID0pNDQUSqVSejg6Oj5330RERPTyqvJzbp7H/PnzsW3bNvz0008wMjIqsSY4OBgZGRnS49atWy+4SyIiInqR9Kpy4bVq1YKuri6Sk5PVhicnJ8POzq7Mab/66ivMnz8fR44cQdOmTUutMzQ0hKGhoVb6JSIiopdfle65MTAwQKtWrRAeHi4NU6lUCA8Ph4+PT6nTLVy4EHPnzsXBgwfh5eX1IlolIiKiaqJK99wAQFBQEAIDA+Hl5YXWrVtj2bJlePDgAYYMGQIAGDRoEBwcHBAaGgoAWLBgAWbNmoWtW7fCxcVFOjfH1NQUpqamVbYeRERE9HKo8nATEBCA1NRUzJo1C0lJSWjevDkOHjwonWScmJgIHZ3/djB98803yMvLw/vvv682n5CQEMyePftFtk5EREQvoSq/z82LxvvcEBERVT/V5j43RERERNrGcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESyolfVDVSZBw8AXd3iw3V1ASMj9brS6OgAxsYVq83JAYQouVahAExMKlb78CGgUpXeR40aFat99AgoLNROrYnJ474BIDcXKCjQTq2x8ePXGQDy8oD8fO3UGhn9917RpDY//3F9aQwNAT09zWsLCh6/FqUxMAD09TWvLSx8vO1Ko6//uF7TWpXq8XtNG7V6eo9fC+Dx70ROjnZqNfm959+Ikmv5N0LzWv6NePyzJn8jyku8YjIyMgQAkfH4T0HxR9eu6hOYmJRcBwjh66teW6tW6bVeXuq1zs6l17q7q9e6u5de6+ysXuvlVXptrVrqtb6+pdeamKjXdu1aeu3Tb6P33y+7Njv7v9rAwLJrU1L+qx0zpuza+Pj/aqdMKbv24sX/akNCyq6NivqvduHCsmuPHfuvNiys7Nq9e/+r3bCh7NodO/6r3bGj7NoNG/6r3bu37NqwsP9qjx0ru3bhwv9qo6LKrg0J+a/24sWya6dM+a82Pr7s2jFj/qtNSSm7NjDwv9rs7LJr339fqCmrln8jHj/4N+K/B/9GPH5U8t8I6fM7I0M8Cw9LERERkawohBCiqpt4kTIzM6FUKpFx5w7Mzc2LF3CXc8m13OWseS13OT/+mYelKlbLvxGPf+bfCM1rZfo3Qvr8zsgo+fP7Ca9uuCnHi0NEREQvB00+v3lYioiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGRFr6obeNGKvgQ9MzOzijshIiKi8ir63C76HC/LKxdusrKyAACOjo5V3AkRERFpKisrC0qlsswahShPBJIRlUqFO3fuwMzMDAqFoqrbeellZmbC0dERt27dgrm5eVW3Q6XgdqoeuJ2qB26nl5MQAllZWahduzZ0dMo+q+aV23Ojo6ODOnXqVHUb1Y65uTl/yasBbqfqgdupeuB2evk8a49NEZ5QTERERLLCcENERESywnBDZTI0NERISAgMDQ2ruhUqA7dT9cDtVD1wO1V/r9wJxURERCRv3HNDREREssJwQ0RERLLCcENERESywnBDREREssJw8woKDQ2Ft7c3zMzMYGNjg549e+LKlStqNY8ePcLYsWNRs2ZNmJqaonfv3khOTlarSUxMxDvvvAMTExPY2Njgk08+QUFBwYtclVfK/PnzoVAoMGnSJGkYt9PL4fbt2/jwww9Rs2ZNGBsbw9PTE2fPnpXGCyEwa9Ys2Nvbw9jYGH5+frh27ZraPNLS0jBgwACYm5vDwsICw4YNQ3Z29oteFdkqLCzEzJkzUbduXRgbG6N+/fqYO3eu2vcUcTvJiKBXjr+/v9iwYYO4ePGiOH/+vOjatatwcnIS2dnZUs1HH30kHB0dRXh4uDh79qx47bXXRNu2baXxBQUFwsPDQ/j5+Ylz586J/fv3i1q1aong4OCqWCXZi4qKEi4uLqJp06Zi4sSJ0nBup6qXlpYmnJ2dxeDBg8Uff/whbty4IQ4dOiTi4uKkmvnz5wulUin27NkjYmNjxbvvvivq1q0rHj58KNV06dJFNGvWTPz+++/ixIkTokGDBqJfv35VsUqyNG/ePFGzZk2xd+9eER8fL3bu3ClMTU3F8uXLpRpuJ/lguCGRkpIiAIjjx48LIYRIT08X+vr6YufOnVLNpUuXBABx+vRpIYQQ+/fvFzo6OiIpKUmq+eabb4S5ubnIzc19sSsgc1lZWaJhw4bi8OHDwtfXVwo33E4vh2nTponXX3+91PEqlUrY2dmJRYsWScPS09OFoaGh+OGHH4QQQvz9998CgDhz5oxUc+DAAaFQKMTt27crr/lXyDvvvCOGDh2qNuy9994TAwYMEEJwO8kND0sRMjIyAABWVlYAgOjoaOTn58PPz0+qcXV1hZOTE06fPg0AOH36NDw9PWFrayvV+Pv7IzMzE3/99dcL7F7+xo4di3feeUdtewDcTi+LX375BV5eXvjggw9gY2ODFi1aYO3atdL4+Ph4JCUlqW0npVKJNm3aqG0nCwsLeHl5STV+fn7Q0dHBH3/88eJWRsbatm2L8PBwXL16FQAQGxuLkydP4u233wbA7SQ3r9wXZ5I6lUqFSZMmoV27dvDw8AAAJCUlwcDAABYWFmq1tra2SEpKkmqe/MAsGl80jrRj27ZtiImJwZkzZ4qN43Z6Ody4cQPffPMNgoKC8Omnn+LMmTOYMGECDAwMEBgYKL3OJW2HJ7eTjY2N2ng9PT1YWVlxO2nJ9OnTkZmZCVdXV+jq6qKwsBDz5s3DgAEDAIDbSWYYbl5xY8eOxcWLF3Hy5MmqboWecuvWLUycOBGHDx+GkZFRVbdDpVCpVPDy8sKXX34JAGjRogUuXryIVatWITAwsIq7oyI7duzAli1bsHXrVjRp0gTnz5/HpEmTULt2bW4nGeJhqVfYuHHjsHfvXhw7dgx16tSRhtvZ2SEvLw/p6elq9cnJybCzs5Nqnr4qp+h5UQ09n+joaKSkpKBly5bQ09ODnp4ejh8/jq+//hp6enqwtbXldnoJ2Nvbw93dXW2Ym5sbEhMTAfz3Ope0HZ7cTikpKWrjCwoKkJaWxu2kJZ988gmmT5+Ovn37wtPTEwMHDsTkyZMRGhoKgNtJbhhuXkFCCIwbNw4//fQTjh49irp166qNb9WqFfT19REeHi4Nu3LlChITE+Hj4wMA8PHxwYULF9R+0Q8fPgxzc/Nif+ipYjp27IgLFy7g/Pnz0sPLywsDBgyQfuZ2qnrt2rUrdiuFq1evwtnZGQBQt25d2NnZqW2nzMxM/PHHH2rbKT09HdHR0VLN0aNHoVKp0KZNmxewFvKXk5MDHR31jzxdXV2oVCoA3E6yU9VnNNOLN3r0aKFUKkVERIS4e/eu9MjJyZFqPvroI+Hk5CSOHj0qzp49K3x8fISPj480vugS486dO4vz58+LgwcPCmtra15iXMmevFpKCG6nl0FUVJTQ09MT8+bNE9euXRNbtmwRJiYm4vvvv5dq5s+fLywsLMTPP/8s/vzzT9GjR48SLzFu0aKF+OOPP8TJkydFw4YNeYmxFgUGBgoHBwfpUvDdu3eLWrVqialTp0o13E7ywXDzCgJQ4mPDhg1SzcOHD8WYMWOEpaWlMDExEb169RJ3795Vm09CQoJ4++23hbGxsahVq5b4+OOPRX5+/gtem1fL0+GG2+nl8OuvvwoPDw9haGgoXF1dxZo1a9TGq1QqMXPmTGFraysMDQ1Fx44dxZUrV9Rq7t+/L/r16ydMTU2Fubm5GDJkiMjKynqRqyFrmZmZYuLEicLJyUkYGRmJevXqic8++0ztlgjcTvKhEOKJ2zMSERERVXM854aIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiGRDoVBgz549lbqM2bNno3nz5pW6DCJ6Pgw3RFRuqampGD16NJycnGBoaAg7Ozv4+/sjMjKyqlvTmp9++gmvvfYalEolzMzM0KRJE0yaNEkaP2XKFLXvHyKil49eVTdARNVH7969kZeXh02bNqFevXpITk5GeHg47t+/X9WtaUV4eDgCAgIwb948vPvuu1AoFPj7779x+PBhqcbU1BSmpqZV2CURPVNVf/8DEVUP//77rwAgIiIiyqxbvHix8PDwECYmJqJOnTpi9OjRat+9s2HDBqFUKsWvv/4qGjVqJIyNjUXv3r3FgwcPxMaNG4Wzs7OwsLAQ48ePFwUFBdJ0zs7OYs6cOaJv377CxMRE1K5dW4SFhaktG4D46aefpOeJiYnigw8+EEqlUlhaWop3331XxMfHl9r7xIkTxZtvvlnm+oWEhIhmzZqpLfPph7OzszT+woULokuXLqJGjRrCxsZGfPjhhyI1NbXMZRDR8+FhKSIql6I9Fnv27EFubm6pdTo6Ovj666/x119/YdOmTTh69CimTp2qVpOTk4Ovv/4a27Ztw8GDBxEREYFevXph//792L9/PzZv3ozVq1dj165datMtWrQIzZo1w7lz5zB9+nRMnDhRba/Kk/Lz8+Hv7w8zMzOcOHECkZGRMDU1RZcuXZCXl1fiNHZ2dvjrr79w8eLFcr8ud+/elR5xcXFo0KAB2rdvDwBIT09Hhw4d0KJFC5w9exYHDx5EcnIy+vTpU+75E1EFVHW6IqLqY9euXcLS0lIYGRmJtm3biuDgYBEbG1vmNDt37hQ1a9aUnm/YsEEAEHFxcdKwUaNGCRMTE7U9PP7+/mLUqFHSc2dnZ9GlSxe1eQcEBIi3335beo4n9txs3rxZNG7cWKhUKml8bm6uMDY2FocOHSqx1+zsbNG1a1dp70tAQID49ttvxaNHj6Sap/fcFFGpVKJXr16iVatWIicnRwghxNy5c0Xnzp3V6m7duiUAFPu2aSLSHu65IaJy6927N+7cuYNffvkFXbp0QUREBFq2bImNGzdKNUeOHEHHjh3h4OAAMzMzDBw4EPfv30dOTo5UY2Jigvr160vPbW1t4eLionYui62tLVJSUtSW7+PjU+z5pUuXSuw1NjYWcXFxMDMzk/Y6WVlZ4dGjR7h+/XqJ09SoUQP79u1DXFwcZsyYAVNTU3z88cdo3bq1Wv8l+fTTT3H69Gn8/PPPMDY2lno4duyYtHxTU1O4uroCQKk9ENHz4wnFRKQRIyMjdOrUCZ06dcLMmTMxfPhwhISEYPDgwUhISEC3bt0wevRozJs3D1ZWVjh58iSGDRuGvLw8mJiYAAD09fXV5qlQKEocplKpKtxndnY2WrVqhS1bthQbZ21tXea09evXR/369TF8+HB89tlnaNSoEbZv344hQ4aUWP/9999j6dKliIiIgIODg1oP3bt3x4IFC4pNY29vr+EaEVF5MdwQ0XNxd3eX7i0THR0NlUqFxYsXQ0fn8Y7hHTt2aG1Zv//+e7Hnbm5uJda2bNkS27dvh42NDczNzSu8TBcXF5iYmODBgwcljj99+jSGDx+O1atX47XXXivWw48//ggXFxfo6fHPLdGLwsNSRFQu9+/fR4cOHfD999/jzz//RHx8PHbu3ImFCxeiR48eAIAGDRogPz8fK1aswI0bN7B582asWrVKaz1ERkZi4cKFuHr1KlauXImdO3di4sSJJdYOGDAAtWrVQo8ePXDixAnEx8cjIiICEyZMwD///FPiNLNnz8bUqVMRERGB+Ph4nDt3DkOHDkV+fj46depUrD4pKQm9evVC37594e/vj6SkJCQlJSE1NRUAMHbsWKSlpaFfv344c+YMrl+/jkOHDmHIkCEoLCzU2utCROoYboioXExNTdGmTRssXboU7du3h4eHB2bOnIkRI0YgLCwMANCsWTMsWbIECxYsgIeHB7Zs2YLQ0FCt9fDxxx/j7NmzaNGiBb744gssWbIE/v7+JdaamJjgf//7H5ycnPDee+/Bzc0Nw4YNw6NHj0rdk+Pr64sbN25g0KBBcHV1xdtvv42kpCT89ttvaNy4cbH6y5cvIzk5GZs2bYK9vb308Pb2BgDUrl0bkZGRKCwsROfOneHp6YlJkybBwsJC2rNFRNqnEEKIqm6CiOhZXFxcMGnSJLW7BRMRlYT/OhAREZGsMNwQERGRrPCwFBEREckK99wQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGs/D8Y1fhbkCcjhgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def estimate_sample_size(sample_size, confidence_level, margin_of_error):\n",
    "    z_score = get_z_score(confidence_level)\n",
    "    p = 0.5  # Conservative estimate of proportion\n",
    "    population_size = estimate_population_size(sample_size)\n",
    "    sample_size_required = (z_score**2 * p * (1 - p)) / (margin_of_error**2)\n",
    "    sample_size_required_corrected = sample_size_required / (1 + (sample_size_required - 1) / population_size)\n",
    "    return math.ceil(sample_size_required_corrected)\n",
    "\n",
    "def estimate_population_size(sample_size):\n",
    "    # Make a conservative assumption about the population size\n",
    "    # If you have any prior information, adjust this value accordingly\n",
    "    return 1000\n",
    "\n",
    "def get_z_score(confidence_level):\n",
    "    if confidence_level == 0.90:\n",
    "        return 1.645\n",
    "    elif confidence_level == 0.95:\n",
    "        return 1.96\n",
    "    elif confidence_level == 0.99:\n",
    "        return 2.576\n",
    "    else:\n",
    "        raise ValueError(\"Invalid confidence level. Choose from 0.90, 0.95, or 0.99.\")\n",
    "\n",
    "def plot_sample_size(sample_sizes, confidence_level, margin_of_error):\n",
    "    confidence_levels = [confidence_level] * len(sample_sizes)  # Create a list of the same length as sample_sizes\n",
    "    plt.plot(sample_sizes, confidence_levels, label=f\"Confidence Level: {confidence_level}\")\n",
    "    plt.xlabel(\"Sample Size\")\n",
    "    plt.ylabel(\"Confidence Level\")\n",
    "    plt.axhline(y=margin_of_error, color='r', linestyle='--', label=\"Margin of Error\")\n",
    "    plt.legend()\n",
    "    plt.title(\"Sample Size vs Confidence Level\")\n",
    "    plt.show()\n",
    "\n",
    "# Example usage\n",
    "sample_size = 150\n",
    "confidence_level = 0.95\n",
    "margin_of_error = 0.05\n",
    "\n",
    "required_sample_size = estimate_sample_size(sample_size, confidence_level, margin_of_error)\n",
    "print(f\"The required sample size is: {required_sample_size}\")\n",
    "\n",
    "# Generate sample sizes for plotting\n",
    "sample_sizes = range(100, 1000, 50)\n",
    "\n",
    "# Plot the results\n",
    "plot_sample_size(sample_sizes, confidence_level, margin_of_error)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a04ba46",
   "metadata": {},
   "source": [
    " the estimate_sample_size function estimates the required sample size using the sample size formula. The estimate_population_size function makes a conservative assumption about the population size. The get_z_score function retrieves the appropriate Z-score for the chosen confidence level.\n",
    "\n",
    "The code calculates the required sample size for your specific survey and prints the result. It also generates a plot showing the relationship between sample size and confidence level, with the margin of error indicated by a dashed red line.\n",
    "\n",
    "Adjust the values of sample_size, confidence_level, and margin_of_error to fit your specific survey requirements. The code will output the required sample size and display a graph illustrating the relationship between sample size and confidence level.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ce0f3873",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The required sample size is: 385.0\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "samplesize_confint_proportion() got an unexpected keyword argument 'nobs'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[6], line 20\u001b[0m\n\u001b[0;32m     17\u001b[0m sample_sizes \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m10\u001b[39m, N\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m10\u001b[39m)\n\u001b[0;32m     19\u001b[0m \u001b[38;5;66;03m# Calculate confidence levels for each sample size\u001b[39;00m\n\u001b[1;32m---> 20\u001b[0m confidence_levels \u001b[38;5;241m=\u001b[39m [samplesize_confint_proportion(P, e, alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m-\u001b[39mlevel, nobs\u001b[38;5;241m=\u001b[39mn, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnormal\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m n \u001b[38;5;129;01min\u001b[39;00m sample_sizes]\n\u001b[0;32m     22\u001b[0m \u001b[38;5;66;03m# Plot the results\u001b[39;00m\n\u001b[0;32m     23\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(sample_sizes, confidence_levels)\n",
      "Cell \u001b[1;32mIn[6], line 20\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m     17\u001b[0m sample_sizes \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m10\u001b[39m, N\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m10\u001b[39m)\n\u001b[0;32m     19\u001b[0m \u001b[38;5;66;03m# Calculate confidence levels for each sample size\u001b[39;00m\n\u001b[1;32m---> 20\u001b[0m confidence_levels \u001b[38;5;241m=\u001b[39m [\u001b[43msamplesize_confint_proportion\u001b[49m\u001b[43m(\u001b[49m\u001b[43mP\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnormal\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m n \u001b[38;5;129;01min\u001b[39;00m sample_sizes]\n\u001b[0;32m     22\u001b[0m \u001b[38;5;66;03m# Plot the results\u001b[39;00m\n\u001b[0;32m     23\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(sample_sizes, confidence_levels)\n",
      "\u001b[1;31mTypeError\u001b[0m: samplesize_confint_proportion() got an unexpected keyword argument 'nobs'"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.stats.proportion import samplesize_confint_proportion\n",
    "\n",
    "e = 0.05  # Margin of error\n",
    "P = 0.5  # Proportion\n",
    "N = 150  # Population size\n",
    "level = 0.95  # Confidence level\n",
    "\n",
    "# Calculate the sample size\n",
    "sample_size = samplesize_confint_proportion(P, e, alpha=1-level, method='normal')\n",
    "sample_size = np.ceil(sample_size)  # Round up to the nearest integer\n",
    "\n",
    "print(f\"The required sample size is: {sample_size}\")\n",
    "\n",
    "# Generate sample sizes for plotting\n",
    "sample_sizes = np.arange(10, N+1, 10)\n",
    "\n",
    "# Calculate confidence levels for each sample size\n",
    "confidence_levels = [samplesize_confint_proportion(P, e, alpha=1-level, nobs=n, method='normal')[0] for n in sample_sizes]\n",
    "\n",
    "# Plot the results\n",
    "plt.plot(sample_sizes, confidence_levels)\n",
    "plt.xlabel(\"Sample Size\")\n",
    "plt.ylabel(\"Confidence Level\")\n",
    "plt.title(\"Sample Size vs Confidence Level\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b57229d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wald Interval: (0.3868294816129595, 0.5465038517203739)\n",
      "Binomial Interval: (0.3868294816129595, 0.5465038517203739)\n",
      "Wilson Score Interval: (0.38866050759928206, 0.5463375086919231)\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "from scipy.stats import norm\n",
    "\n",
    "def calculate_proportion_confidence_interval(sample_size, successes, confidence_level):\n",
    "    # Calculate the sample proportion\n",
    "    sample_proportion = successes / sample_size\n",
    "\n",
    "    # Calculate the standard error using the normal distribution approximation (Wald interval)\n",
    "    standard_error_wald = math.sqrt((sample_proportion * (1 - sample_proportion)) / sample_size)\n",
    "\n",
    "    # Calculate the z-score for the desired confidence level\n",
    "    z_score = norm.ppf(1 - (1 - confidence_level) / 2)\n",
    "\n",
    "    # Calculate the confidence interval using the normal distribution approximation (Wald interval)\n",
    "    confidence_interval_wald = (\n",
    "        sample_proportion - z_score * standard_error_wald,\n",
    "        sample_proportion + z_score * standard_error_wald\n",
    "    )\n",
    "\n",
    "    # Calculate the confidence interval using the binomial distribution\n",
    "    standard_error_binomial = math.sqrt((sample_proportion * (1 - sample_proportion)) / sample_size)\n",
    "    confidence_interval_binomial = (\n",
    "        sample_proportion - z_score * standard_error_binomial,\n",
    "        sample_proportion + z_score * standard_error_binomial\n",
    "    )\n",
    "\n",
    "    # Calculate the confidence interval using the Wilson score interval\n",
    "    z_squared = z_score ** 2\n",
    "    denominator = 1 + (z_squared / sample_size)\n",
    "    adjusted_proportion = sample_proportion + (z_squared / (2 * sample_size))\n",
    "    adjusted_standard_error = math.sqrt((sample_proportion * (1 - sample_proportion) + (z_squared / (4 * sample_size))) / sample_size)\n",
    "    confidence_interval_wilson = (\n",
    "        (adjusted_proportion - (z_score * adjusted_standard_error)) / denominator,\n",
    "        (adjusted_proportion + (z_score * adjusted_standard_error)) / denominator\n",
    "    )\n",
    "\n",
    "    # Return the calculated confidence intervals\n",
    "    return {\n",
    "        'Wald Interval': confidence_interval_wald,\n",
    "        'Binomial Interval': confidence_interval_binomial,\n",
    "        'Wilson Score Interval': confidence_interval_wilson\n",
    "    }\n",
    "\n",
    "# Example usage\n",
    "sample_size = 150\n",
    "successes = 70\n",
    "confidence_level = 0.95\n",
    "\n",
    "confidence_intervals = calculate_proportion_confidence_interval(sample_size, successes, confidence_level)\n",
    "\n",
    "# Display the results\n",
    "for interval_name, interval in confidence_intervals.items():\n",
    "    print(f\"{interval_name}: {interval}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0b51a6d",
   "metadata": {},
   "source": [
    "The results you provided are intervals calculated for a proportion or a probability, using different methods. Here's an explanation of each interval:\n",
    "\n",
    "1. Wald Interval:\n",
    "   - Lower bound: 0.3868294816129595\n",
    "   - Upper bound: 0.5465038517203739\n",
    "\n",
    "   The Wald interval is calculated using a normal approximation to the binomial distribution. It assumes that the sample proportion follows a normal distribution. This interval is computed as the sample proportion plus and minus the z-score (corresponding to the desired confidence level) multiplied by the standard error. The Wald interval is a simple and commonly used method, but it may not be accurate for small sample sizes or extreme proportions.\n",
    "\n",
    "2. Binomial Interval:\n",
    "   - Lower bound: 0.3868294816129595\n",
    "   - Upper bound: 0.5465038517203739\n",
    "\n",
    "   The binomial interval is also known as the Clopper-Pearson interval or the exact interval. It is calculated using the binomial distribution directly, providing exact confidence intervals for the proportion. This interval takes into account the variability of the binomial distribution and provides a conservative estimate of the confidence interval. It is particularly useful when dealing with small sample sizes or extreme proportions.\n",
    "\n",
    "3. Wilson Score Interval:\n",
    "   - Lower bound: 0.38866050759928206\n",
    "   - Upper bound: 0.5463375086919231\n",
    "\n",
    "   The Wilson score interval is an improvement over the Wald interval, especially for small sample sizes. It uses the Wilson score statistic to determine the bounds of the interval. This interval takes into account both the sample size and the observed proportion and provides a more accurate estimate of the confidence interval. It is a popular choice when dealing with binary data and small sample sizes.\n",
    "\n",
    "In summary, the three intervals provide estimates for the range of possible values for the proportion or probability of interest, with each method making certain assumptions or considerations. The Binomial interval is the most conservative and exact, while the Wald and Wilson Score intervals provide approximations based on different statistical techniques."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d40b6d29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wald Interval:\n",
      "Lower Bound: 0.3868294816129595\n",
      "Upper Bound: 0.5465038517203739\n",
      "\n",
      "Binomial Interval:\n",
      "Lower Bound: 0.3858091337647864\n",
      "Upper Bound: 0.5475241995685469\n",
      "\n",
      "Wilson Interval:\n",
      "Lower Bound: 0.38866050759928206\n",
      "Upper Bound: 0.5463375086919231\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "from scipy.stats import norm\n",
    "\n",
    "def proportion_confidence_interval_wald(successes, trials, confidence=0.95):\n",
    "    p_hat = successes / trials\n",
    "    z = norm.ppf(1 - (1 - confidence) / 2)\n",
    "    margin_of_error = z * math.sqrt((p_hat * (1 - p_hat)) / trials)\n",
    "    lower_bound = p_hat - margin_of_error\n",
    "    upper_bound = p_hat + margin_of_error\n",
    "    return lower_bound, upper_bound\n",
    "\n",
    "def proportion_confidence_interval_binomial(successes, trials, confidence=0.95):\n",
    "    p_hat = successes / trials\n",
    "    z = norm.ppf(1 - (1 - confidence) / 2)\n",
    "    margin_of_error = z * math.sqrt((p_hat * (1 - p_hat)) / trials + (z**2) / (4 * trials**2))\n",
    "    lower_bound = p_hat - margin_of_error\n",
    "    upper_bound = p_hat + margin_of_error\n",
    "    return lower_bound, upper_bound\n",
    "\n",
    "def proportion_confidence_interval_wilson(successes, trials, confidence=0.95):\n",
    "    p_hat = successes / trials\n",
    "    z = norm.ppf(1 - (1 - confidence) / 2)\n",
    "    denominator = 1 + (z**2) / trials\n",
    "    center_adjusted_probability = p_hat + (z**2) / (2 * trials)\n",
    "    margin_of_error = z * math.sqrt((p_hat * (1 - p_hat) + (z**2) / (4 * trials)) / trials)\n",
    "    lower_bound = (center_adjusted_probability - margin_of_error) / denominator\n",
    "    upper_bound = (center_adjusted_probability + margin_of_error) / denominator\n",
    "    return lower_bound, upper_bound\n",
    "\n",
    "# Example usage\n",
    "successes = 70\n",
    "trials = 150\n",
    "confidence = 0.95\n",
    "\n",
    "wald_interval = proportion_confidence_interval_wald(successes, trials, confidence)\n",
    "binomial_interval = proportion_confidence_interval_binomial(successes, trials, confidence)\n",
    "wilson_interval = proportion_confidence_interval_wilson(successes, trials, confidence)\n",
    "\n",
    "print(\"Wald Interval:\")\n",
    "print(f\"Lower Bound: {wald_interval[0]}\")\n",
    "print(f\"Upper Bound: {wald_interval[1]}\")\n",
    "print()\n",
    "print(\"Binomial Interval:\")\n",
    "print(f\"Lower Bound: {binomial_interval[0]}\")\n",
    "print(f\"Upper Bound: {binomial_interval[1]}\")\n",
    "print()\n",
    "print(\"Wilson Interval:\")\n",
    "print(f\"Lower Bound: {wilson_interval[0]}\")\n",
    "print(f\"Upper Bound: {wilson_interval[1]}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5716ee9a",
   "metadata": {},
   "source": [
    "# Proportion Confidence Interval Calculator\n",
    "\n",
    "https://www.statskingdom.com/proportion-confidence-interval-calculator.html"
   ]
  },
  {
   "cell_type": "raw",
   "id": "d82c6620",
   "metadata": {},
   "source": [
    "calculate the sample proportion for a given sample size and confidence interval when the total population is unknown\n",
    "\n",
    "we can use the formula:\n",
    "\n",
    "Sample Proportion = Margin of Error + Confidence Interval\n",
    "\n",
    "The margin of error is based on the desired confidence level and the sample size. For a 95% confidence level, the margin of error is approximately ±1.96, which corresponds to a confidence interval of 0.95.\n",
    "\n",
    "Given a sample size of 150 and a 95% confidence interval, we can calculate the sample proportion as follows:\n",
    "\n",
    "Sample Proportion = Margin of Error + Confidence Interval\n",
    "= 1.96 / √150 + 0.95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "68689cbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample Proportion: 1.1100333298618343\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "sample_size = 150\n",
    "confidence_interval = 0.95\n",
    "margin_of_error = 1.96 / math.sqrt(sample_size)\n",
    "\n",
    "sample_proportion = margin_of_error + confidence_interval\n",
    "\n",
    "print(\"Sample Proportion:\", sample_proportion)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9954bd58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wald Interval:\n",
      "Lower Bound: -0.006315286765525559\n",
      "Upper Bound: 0.02111573116368335\n",
      "\n",
      "Binomial Interval:\n",
      "Lower Bound: -0.011363564621200974\n",
      "Upper Bound: 0.026164009019358767\n",
      "\n",
      "Wilson Interval:\n",
      "Lower Bound: 0.0014053085483211455\n",
      "Upper Bound: 0.03799580950453512\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import norm\n",
    "\n",
    "def proportion_confidence_interval_wald(successes, trials, confidence=0.95):\n",
    "    p_hat = successes / trials\n",
    "    z = norm.ppf(1 - (1 - confidence) / 2)\n",
    "    margin_of_error = z * math.sqrt((p_hat * (1 - p_hat)) / trials)\n",
    "    lower_bound = p_hat - margin_of_error\n",
    "    upper_bound = p_hat + margin_of_error\n",
    "    return lower_bound, upper_bound\n",
    "\n",
    "def proportion_confidence_interval_binomial(successes, trials, confidence=0.95):\n",
    "    p_hat = successes / trials\n",
    "    z = norm.ppf(1 - (1 - confidence) / 2)\n",
    "    margin_of_error = z * math.sqrt((p_hat * (1 - p_hat)) / trials + (z**2) / (4 * trials**2))\n",
    "    lower_bound = p_hat - margin_of_error\n",
    "    upper_bound = p_hat + margin_of_error\n",
    "    return lower_bound, upper_bound\n",
    "\n",
    "def proportion_confidence_interval_wilson(successes, trials, confidence=0.95):\n",
    "    p_hat = successes / trials\n",
    "    z = norm.ppf(1 - (1 - confidence) / 2)\n",
    "    denominator = 1 + (z**2) / trials\n",
    "    center_adjusted_probability = p_hat + (z**2) / (2 * trials)\n",
    "    margin_of_error = z * math.sqrt((p_hat * (1 - p_hat) + (z**2) / (4 * trials)) / trials)\n",
    "    lower_bound = (center_adjusted_probability - margin_of_error) / denominator\n",
    "    upper_bound = (center_adjusted_probability + margin_of_error) / denominator\n",
    "    return lower_bound, upper_bound\n",
    "\n",
    "# Example usage\n",
    "#successes = #70 \n",
    "successes = 1.1100333298618343 #Sample Proportion: 1.1100333298618343\n",
    "\n",
    "trials = 150\n",
    "confidence = 0.95\n",
    "\n",
    "wald_interval = proportion_confidence_interval_wald(successes, trials, confidence)\n",
    "binomial_interval = proportion_confidence_interval_binomial(successes, trials, confidence)\n",
    "wilson_interval = proportion_confidence_interval_wilson(successes, trials, confidence)\n",
    "\n",
    "print(\"Wald Interval:\")\n",
    "print(f\"Lower Bound: {wald_interval[0]}\")\n",
    "print(f\"Upper Bound: {wald_interval[1]}\")\n",
    "print()\n",
    "print(\"Binomial Interval:\")\n",
    "print(f\"Lower Bound: {binomial_interval[0]}\")\n",
    "print(f\"Upper Bound: {binomial_interval[1]}\")\n",
    "print()\n",
    "print(\"Wilson Interval:\")\n",
    "print(f\"Lower Bound: {wilson_interval[0]}\")\n",
    "print(f\"Upper Bound: {wilson_interval[1]}\")\n",
    "\n",
    "# Plotting the recommended graph\n",
    "x = np.linspace(successes - 30, successes + 30, 100)\n",
    "y_wald = norm.pdf(x, successes / trials, math.sqrt((successes / trials) * (1 - (successes / trials)) / trials))\n",
    "y_binomial = norm.pdf(x, successes / trials, math.sqrt((successes / trials) * (1 - (successes / trials)) / trials + (norm.ppf(1 - (1 - confidence) / 2)**2) / (4 * trials**2)))\n",
    "y_wilson = norm.pdf(x, successes / trials, math.sqrt((successes / trials) * (1 - (successes / trials))) + (norm.ppf(1 - (1 - confidence) / 2)**2) / (4 * trials))\n",
    "\n",
    "plt.plot(x, y_wald, label='Wald Interval')\n",
    "plt.plot(x, y_binomial, label='Binomial Interval')\n",
    "plt.plot(x, y_wilson, label='Wilson Interval')\n",
    "plt.xlabel('Proportion')\n",
    "plt.ylabel('Density')\n",
    "plt.legend()\n",
    "plt.title('Recommended Confidence Intervals')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bbfe3f2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wald Interval:\n",
      "Lower Bound: 0.4199848053940782\n",
      "Upper Bound: 0.5800151946059219\n",
      "\n",
      "Binomial Interval:\n",
      "Lower Bound: 0.41896669895961103\n",
      "Upper Bound: 0.581033301040389\n",
      "\n",
      "Wilson Interval:\n",
      "Lower Bound: 0.42099012028854144\n",
      "Upper Bound: 0.5790098797114586\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import norm\n",
    "\n",
    "def proportion_confidence_interval_wald(successes, trials, confidence=0.95):\n",
    "    p_hat = successes / trials\n",
    "    z = norm.ppf(1 - (1 - confidence) / 2)\n",
    "    margin_of_error = z * math.sqrt((p_hat * (1 - p_hat)) / trials)\n",
    "    lower_bound = p_hat - margin_of_error\n",
    "    upper_bound = p_hat + margin_of_error\n",
    "    return lower_bound, upper_bound\n",
    "\n",
    "def proportion_confidence_interval_binomial(successes, trials, confidence=0.95):\n",
    "    p_hat = successes / trials\n",
    "    z = norm.ppf(1 - (1 - confidence) / 2)\n",
    "    margin_of_error = z * math.sqrt((p_hat * (1 - p_hat)) / trials + (z**2) / (4 * trials**2))\n",
    "    lower_bound = p_hat - margin_of_error\n",
    "    upper_bound = p_hat + margin_of_error\n",
    "    return lower_bound, upper_bound\n",
    "\n",
    "def proportion_confidence_interval_wilson(successes, trials, confidence=0.95):\n",
    "    p_hat = successes / trials\n",
    "    z = norm.ppf(1 - (1 - confidence) / 2)\n",
    "    denominator = 1 + (z**2) / trials\n",
    "    center_adjusted_probability = p_hat + (z**2) / (2 * trials)\n",
    "    margin_of_error = z * math.sqrt((p_hat * (1 - p_hat) + (z**2) / (4 * trials)) / trials)\n",
    "    lower_bound = (center_adjusted_probability - margin_of_error) / denominator\n",
    "    upper_bound = (center_adjusted_probability + margin_of_error) / denominator\n",
    "    return lower_bound, upper_bound\n",
    "\n",
    "# Example usage\n",
    "successes = 75 \n",
    "trials = 150\n",
    "confidence = 0.95\n",
    "\n",
    "wald_interval = proportion_confidence_interval_wald(successes, trials, confidence)\n",
    "binomial_interval = proportion_confidence_interval_binomial(successes, trials, confidence)\n",
    "wilson_interval = proportion_confidence_interval_wilson(successes, trials, confidence)\n",
    "\n",
    "print(\"Wald Interval:\")\n",
    "print(f\"Lower Bound: {wald_interval[0]}\")\n",
    "print(f\"Upper Bound: {wald_interval[1]}\")\n",
    "print()\n",
    "print(\"Binomial Interval:\")\n",
    "print(f\"Lower Bound: {binomial_interval[0]}\")\n",
    "print(f\"Upper Bound: {binomial_interval[1]}\")\n",
    "print()\n",
    "print(\"Wilson Interval:\")\n",
    "print(f\"Lower Bound: {wilson_interval[0]}\")\n",
    "print(f\"Upper Bound: {wilson_interval[1]}\")\n",
    "\n",
    "# Plotting the recommended graph\n",
    "x = np.linspace(successes - 30, successes + 30, 100)\n",
    "y_wald = norm.pdf(x, successes / trials, math.sqrt((successes / trials) * (1 - (successes / trials)) / trials))\n",
    "y_binomial = norm.pdf(x, successes / trials, math.sqrt((successes / trials) * (1 - (successes / trials)) / trials + (norm.ppf(1 - (1 - confidence) / 2)**2) / (4 * trials**2)))\n",
    "y_wilson = norm.pdf(x, successes / trials, math.sqrt((successes / trials) * (1 - (successes / trials))) + (norm.ppf(1 - (1 - confidence) / 2)**2) / (4 * trials))\n",
    "\n",
    "plt.plot(x, y_wald, label='Wald Interval')\n",
    "plt.plot(x, y_binomial, label='Binomial Interval')\n",
    "plt.plot(x, y_wilson, label='Wilson Interval')\n",
    "plt.xlabel('Proportion')\n",
    "plt.ylabel('Density')\n",
    "plt.legend()\n",
    "plt.title('Recommended Confidence Intervals')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ddc37ca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4263c23d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores and Results:\n",
      "Number of successes (x): 75\n",
      "Total number of trials (n): 150\n",
      "Significance level (alpha): 0.05\n",
      "\n",
      "Confidence Intervals:\n",
      "Wald Interval: (0.4199848053940781, 0.5800151946059219)\n",
      "Binomial Interval: (0.42, 0.58)\n",
      "Wilson Interval: (0.49099135021876655, 0.5090086497812334)\n",
      "Clopper-Pearson Interval: (0.42, 0.58)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The recommended method is: Wilson\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import binom, norm\n",
    "\n",
    "def wald_interval(x, n, alpha):\n",
    "    p_hat = x / n\n",
    "    z = np.abs(norm.ppf(alpha/2))\n",
    "    lower = p_hat - z * np.sqrt(p_hat*(1-p_hat)/n)\n",
    "    upper = p_hat + z * np.sqrt(p_hat*(1-p_hat)/n)\n",
    "    return lower, upper\n",
    "\n",
    "def binomial_interval(x, n, alpha):\n",
    "    lower = binom.ppf(alpha/2, n, x/n)\n",
    "    upper = binom.ppf(1-alpha/2, n, x/n)\n",
    "    return lower/n, upper/n\n",
    "\n",
    "def wilson_interval(x, n, alpha):\n",
    "    p_hat = x / n\n",
    "    z = np.abs(norm.ppf(alpha/2))\n",
    "    denominator = 1 + z**2 / n\n",
    "    center_adjusted = p_hat + z**2 / (2 * n)\n",
    "    adjusted_sqrt = np.sqrt(z**2 / n * p_hat * (1 - p_hat) / n + z**2 / (4 * n**2))\n",
    "    lower = (center_adjusted - adjusted_sqrt) / denominator\n",
    "    upper = (center_adjusted + adjusted_sqrt) / denominator\n",
    "    return lower, upper\n",
    "\n",
    "def clopper_pearson_interval(x, n, alpha):\n",
    "    lower = binom.ppf(alpha/2, n, x/n)\n",
    "    upper = binom.ppf(1-alpha/2, n, x/n+1e-9)  # Add a small value to avoid upper bound being exactly 1\n",
    "    return lower/n, upper/n\n",
    "\n",
    "def plot_intervals(intervals, labels):\n",
    "    x = np.arange(len(intervals))\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    for i, interval in enumerate(intervals):\n",
    "        plt.errorbar(x[i], interval[0], yerr=[[interval[0]], [interval[1]]], marker='o', label=labels[i])\n",
    "    plt.xticks(x, labels)\n",
    "    plt.ylim(0, 1)\n",
    "    plt.xlabel('Interval Method')\n",
    "    plt.ylabel('Proportion')\n",
    "    plt.title('Proportion Confidence Intervals')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "# Parameters\n",
    "x = 75  # Number of successes\n",
    "n = 150  # Total number of trials\n",
    "alpha = 0.05  # Significance level\n",
    "\n",
    "# Calculate confidence intervals\n",
    "wald = wald_interval(x, n, alpha)\n",
    "binomial = binomial_interval(x, n, alpha)\n",
    "wilson = wilson_interval(x, n, alpha)\n",
    "clopper_pearson = clopper_pearson_interval(x, n, alpha)\n",
    "\n",
    "# Display the scores and results\n",
    "print(\"Scores and Results:\")\n",
    "print(f\"Number of successes (x): {x}\")\n",
    "print(f\"Total number of trials (n): {n}\")\n",
    "print(f\"Significance level (alpha): {alpha}\")\n",
    "\n",
    "print(\"\\nConfidence Intervals:\")\n",
    "print(\"Wald Interval:\", wald)\n",
    "print(\"Binomial Interval:\", binomial)\n",
    "print(\"Wilson Interval:\", wilson)\n",
    "print(\"Clopper-Pearson Interval:\", clopper_pearson)\n",
    "\n",
    "\n",
    "# Plot the intervals\n",
    "intervals = [wald, binomial, wilson, clopper_pearson]\n",
    "labels = ['Wald', 'Binomial', 'Wilson', 'Clopper-Pearson']\n",
    "plot_intervals(intervals, labels)\n",
    "\n",
    "# Recommendation based on interval width\n",
    "widths = [interval[1] - interval[0] for interval in intervals]\n",
    "best_method = labels[np.argmin(widths)]\n",
    "print(f\"\\nThe recommended method is: {best_method}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ed7e7248",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample Size: 3182.0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import t\n",
    "\n",
    "def sample_size_ttest(alpha, power, effect_size, sigma):\n",
    "    z_alpha = t.ppf(1 - alpha/2, df=150-1)  # Two-tailed test\n",
    "    z_beta = t.ppf(power, df=150-1)  # One-tailed test\n",
    "    n = ((z_alpha + z_beta) * sigma / effect_size) ** 2\n",
    "    return np.ceil(n)\n",
    "\n",
    "# Parameters\n",
    "alpha = 0.05  # Significance level\n",
    "power = 0.8  # Desired power\n",
    "effect_size = 0.5  # Desired effect size\n",
    "sigma = 10  # Standard deviation of the population\n",
    "\n",
    "# Calculate sample size\n",
    "sample_size = sample_size_ttest(alpha, power, effect_size, sigma)\n",
    "print(\"Sample Size:\", sample_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f942469d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample Mean: 75.5\n",
      "Sample Standard Deviation: 106.77312395916867\n",
      "Population Mean Estimate: 75.5\n",
      "Population Standard Deviation Estimate: 151.0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Sample data\n",
    "sample_data = np.array([0, 151])  # Replace [...] with your actual sample data\n",
    "\n",
    "\n",
    "# Calculate sample mean and sample standard deviation\n",
    "sample_mean = np.mean(sample_data)\n",
    "sample_std = np.std(sample_data, ddof=1)  # ddof=1 for sample standard deviation\n",
    "\n",
    "# Population mean estimate (assuming sample is representative)\n",
    "population_mean_estimate = sample_mean\n",
    "\n",
    "# Population standard deviation estimate (assuming sample is representative)\n",
    "population_std_estimate = sample_std * np.sqrt(len(sample_data))  \n",
    "\n",
    "# Print the results\n",
    "print(\"Sample Mean:\", sample_mean)\n",
    "print(\"Sample Standard Deviation:\", sample_std)\n",
    "print(\"Population Mean Estimate:\", population_mean_estimate)\n",
    "print(\"Population Standard Deviation Estimate:\", population_std_estimate)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bb894a48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z-test Results:\n",
      "Sample Mean: 5.2\n",
      "Population Mean: 5.0\n",
      "Population Standard Deviation: 1.5\n",
      "Sample Size: 150\n",
      "Alternative Hypothesis: two-sided\n",
      "Z-score: 1.6329931618554534\n",
      "P-value: 0.10247043485974916\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "\n",
    "def z_test(sample_mean, population_mean, population_std, sample_size, alternative='two-sided'):\n",
    "    # Calculate the standard error\n",
    "    std_error = population_std / np.sqrt(sample_size)\n",
    "\n",
    "    # Calculate the test statistic\n",
    "    z_score = (sample_mean - population_mean) / std_error\n",
    "\n",
    "    # Calculate the p-value\n",
    "    if alternative == 'two-sided':\n",
    "        p_value = 2 * (1 - norm.cdf(np.abs(z_score)))\n",
    "    elif alternative == 'greater':\n",
    "        p_value = 1 - norm.cdf(z_score)\n",
    "    elif alternative == 'less':\n",
    "        p_value = norm.cdf(z_score)\n",
    "\n",
    "    # Return the test statistic and p-value\n",
    "    return z_score, p_value\n",
    "\n",
    "# Parameters\n",
    "sample_mean = 5.2  # Sample mean\n",
    "population_mean = 5.0  # Population mean\n",
    "population_std = 1.5  # Population standard deviation\n",
    "sample_size = 150  # Sample size\n",
    "alternative = 'two-sided'  # Alternative hypothesis ('two-sided', 'greater', 'less')\n",
    "\n",
    "# Perform the Z-test\n",
    "z_score, p_value = z_test(sample_mean, population_mean, population_std, sample_size, alternative)\n",
    "\n",
    "# Display the results\n",
    "print(\"Z-test Results:\")\n",
    "print(f\"Sample Mean: {sample_mean}\")\n",
    "print(f\"Population Mean: {population_mean}\")\n",
    "print(f\"Population Standard Deviation: {population_std}\")\n",
    "print(f\"Sample Size: {sample_size}\")\n",
    "print(f\"Alternative Hypothesis: {alternative}\")\n",
    "print(f\"Z-score: {z_score}\")\n",
    "print(f\"P-value: {p_value}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3f372452",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z-test Results:\n",
      "Sample Mean: 5.2\n",
      "Population Mean: 5.0\n",
      "Population Standard Deviation: 1.5\n",
      "Sample Size: 150\n",
      "Alternative Hypothesis: two-sided\n",
      "Z-score: 1.6329931618554534\n",
      "P-value: 0.10247043485974916\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import norm\n",
    "\n",
    "def z_test(sample_mean, population_mean, population_std, sample_size, alternative='two-sided'):\n",
    "    # Calculate the standard error\n",
    "    std_error = population_std / np.sqrt(sample_size)\n",
    "\n",
    "    # Calculate the test statistic\n",
    "    z_score = (sample_mean - population_mean) / std_error\n",
    "\n",
    "    # Calculate the p-value\n",
    "    if alternative == 'two-sided':\n",
    "        p_value = 2 * (1 - norm.cdf(np.abs(z_score)))\n",
    "    elif alternative == 'greater':\n",
    "        p_value = 1 - norm.cdf(z_score)\n",
    "    elif alternative == 'less':\n",
    "        p_value = norm.cdf(z_score)\n",
    "\n",
    "    # Return the test statistic and p-value\n",
    "    return z_score, p_value\n",
    "\n",
    "# Parameters\n",
    "sample_mean = 5.2  # Sample mean\n",
    "population_mean = 5.0  # Population mean\n",
    "population_std = 1.5  # Population standard deviation\n",
    "sample_size = 150  # Sample size\n",
    "alternative = 'two-sided'  # Alternative hypothesis ('two-sided', 'greater', 'less')\n",
    "\n",
    "# Perform the Z-test\n",
    "z_score, p_value = z_test(sample_mean, population_mean, population_std, sample_size, alternative)\n",
    "\n",
    "# Display the results\n",
    "print(\"Z-test Results:\")\n",
    "print(f\"Sample Mean: {sample_mean}\")\n",
    "print(f\"Population Mean: {population_mean}\")\n",
    "print(f\"Population Standard Deviation: {population_std}\")\n",
    "print(f\"Sample Size: {sample_size}\")\n",
    "print(f\"Alternative Hypothesis: {alternative}\")\n",
    "print(f\"Z-score: {z_score}\")\n",
    "print(f\"P-value: {p_value}\")\n",
    "\n",
    "# Plot the results\n",
    "results = [z_score, p_value]\n",
    "labels = ['Z-score', 'P-value']\n",
    "plt.bar(labels, results)\n",
    "plt.ylabel('Value')\n",
    "plt.title('Z-test Results')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ba15604a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores and Results:\n",
      "Number of successes (x): 75\n",
      "Sample size (n): 150\n",
      "Assumed population proportion (p): 0.5\n",
      "Significance level (alpha): 0.05\n",
      "\n",
      "Sample Size Z-Test:\n",
      "Lower Bound: 0.4199848053940782\n",
      "Upper Bound: 0.5800151946059219\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import norm\n",
    "\n",
    "def sample_size_z_test(x, n, p, alpha):\n",
    "    p_hat = x / n\n",
    "    z_critical = norm.ppf(1 - alpha/2)\n",
    "    standard_error = np.sqrt(p * (1 - p) / n)\n",
    "    margin_of_error = z_critical * standard_error\n",
    "    lower_bound = p_hat - margin_of_error\n",
    "    upper_bound = p_hat + margin_of_error\n",
    "    return lower_bound, upper_bound\n",
    "\n",
    "# Parameters\n",
    "x = 75  # Number of successes\n",
    "n = 150  # Sample size\n",
    "p = 0.5  # Assumed population proportion\n",
    "alpha = 0.05  # Significance level\n",
    "\n",
    "# Perform sample size Z-test\n",
    "lower_bound, upper_bound = sample_size_z_test(x, n, p, alpha)\n",
    "\n",
    "# Print the scores and results\n",
    "print(\"Scores and Results:\")\n",
    "print(f\"Number of successes (x): {x}\")\n",
    "print(f\"Sample size (n): {n}\")\n",
    "print(f\"Assumed population proportion (p): {p}\")\n",
    "print(f\"Significance level (alpha): {alpha}\")\n",
    "print(\"\")\n",
    "\n",
    "print(\"Sample Size Z-Test:\")\n",
    "print(\"Lower Bound:\", lower_bound)\n",
    "print(\"Upper Bound:\", upper_bound)\n",
    "\n",
    "# Plot the scores\n",
    "scores = [lower_bound, upper_bound]\n",
    "labels = [\"Lower Bound\", \"Upper Bound\"]\n",
    "plt.bar(labels, scores)\n",
    "plt.ylim(0, 1)\n",
    "plt.xlabel('Interval')\n",
    "plt.ylabel('Proportion')\n",
    "plt.title('Sample Size Z-Test')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c2359b7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample Size Z-test:\n",
      "Required Sample Size: 15.365835282776498\n",
      "\n",
      "Two-sample Z-test:\n",
      "Test Statistic: 3.464101615137755\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import norm\n",
    "\n",
    "def sample_size_z_test(sample_mean, population_mean, population_std, alpha):\n",
    "    z = norm.ppf(1 - alpha/2)\n",
    "    sample_size = (z * population_std / (sample_mean - population_mean))**2\n",
    "    return sample_size\n",
    "\n",
    "def two_sample_z_test(sample_mean1, sample_mean2, population_std1, population_std2, sample_size1, sample_size2, alpha):\n",
    "    z = norm.ppf(1 - alpha/2)\n",
    "    pooled_std = np.sqrt((population_std1**2/sample_size1) + (population_std2**2/sample_size2))\n",
    "    test_stat = (sample_mean1 - sample_mean2) / pooled_std\n",
    "    return test_stat\n",
    "\n",
    "# Parameters\n",
    "sample_mean = 100\n",
    "population_mean = 95\n",
    "population_std = 10\n",
    "alpha = 0.05\n",
    "sample_size1 = 150\n",
    "sample_size2 = 150\n",
    "sample_mean1 = 102\n",
    "sample_mean2 = 98\n",
    "\n",
    "# Sample size Z-test\n",
    "sample_size_z = sample_size_z_test(sample_mean, population_mean, population_std, alpha)\n",
    "print(\"Sample Size Z-test:\")\n",
    "print(\"Required Sample Size:\", sample_size_z)\n",
    "\n",
    "# Two-sample Z-test\n",
    "test_statistic = two_sample_z_test(sample_mean1, sample_mean2, population_std, population_std, sample_size1, sample_size2, alpha)\n",
    "print(\"\\nTwo-sample Z-test:\")\n",
    "print(\"Test Statistic:\", test_statistic)\n",
    "\n",
    "# Plot scores\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(sample_mean, 0, 'ro', label='Sample Mean')\n",
    "plt.axvline(population_mean, color='b', linestyle='--', label='Population Mean')\n",
    "plt.xlabel('Value')\n",
    "plt.title('Sample Size Z-test')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(sample_mean1, 0, 'ro', label='Sample 1 Mean')\n",
    "plt.plot(sample_mean2, 0, 'bo', label='Sample 2 Mean')\n",
    "plt.xlabel('Value')\n",
    "plt.title('Two-sample Z-test')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4a964945",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regression Equation: y = 0.0006x + -0.0339\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Hypothetical dataset (Sample sizes and Regression values)\n",
    "sample_sizes = np.arange(100, 201)\n",
    "regression_values = np.random.randn(len(sample_sizes))\n",
    "\n",
    "# Perform linear regression\n",
    "coefficients = np.polyfit(sample_sizes, regression_values, 1)\n",
    "regression_line = np.polyval(coefficients, sample_sizes)\n",
    "\n",
    "# Plot the data and regression line\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(sample_sizes, regression_values, color='b', label='Data')\n",
    "plt.plot(sample_sizes, regression_line, color='r', label='Regression Line')\n",
    "plt.xlabel('Sample Size')\n",
    "plt.ylabel('Regression Value')\n",
    "plt.title('Regression of Sample Size')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Print the regression equation\n",
    "slope = coefficients[0]\n",
    "intercept = coefficients[1]\n",
    "print(\"Regression Equation: y = {:.4f}x + {:.4f}\".format(slope, intercept))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "01b06201",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regression Equation for Village A: y = -0.0037x + 0.6305\n",
      "Regression Equation for Village B: y = -0.0026x + 0.3399\n",
      "Regression Equation for Village C: y = 0.0037x + -0.5553\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Hypothetical dataset (Sample sizes and Regression values for each village)\n",
    "sample_sizes_A = np.random.randint(100, 200, size=150)\n",
    "regression_values_A = np.random.randn(150)\n",
    "\n",
    "sample_sizes_B = np.random.randint(100, 200, size=150)\n",
    "regression_values_B = np.random.randn(150)\n",
    "\n",
    "sample_sizes_C = np.random.randint(100, 200, size=150)\n",
    "regression_values_C = np.random.randn(150)\n",
    "\n",
    "# Perform linear regression for each village\n",
    "coefficients_A = np.polyfit(sample_sizes_A, regression_values_A, 1)\n",
    "regression_line_A = np.polyval(coefficients_A, sample_sizes_A)\n",
    "\n",
    "coefficients_B = np.polyfit(sample_sizes_B, regression_values_B, 1)\n",
    "regression_line_B = np.polyval(coefficients_B, sample_sizes_B)\n",
    "\n",
    "coefficients_C = np.polyfit(sample_sizes_C, regression_values_C, 1)\n",
    "regression_line_C = np.polyval(coefficients_C, sample_sizes_C)\n",
    "\n",
    "# Plot the data and regression lines for each village\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(sample_sizes_A, regression_values_A, color='b', label='Village A')\n",
    "plt.plot(sample_sizes_A, regression_line_A, color='r', label='Regression Line A')\n",
    "\n",
    "plt.scatter(sample_sizes_B, regression_values_B, color='g', label='Village B')\n",
    "plt.plot(sample_sizes_B, regression_line_B, color='m', label='Regression Line B')\n",
    "\n",
    "plt.scatter(sample_sizes_C, regression_values_C, color='y', label='Village C')\n",
    "plt.plot(sample_sizes_C, regression_line_C, color='k', label='Regression Line C')\n",
    "\n",
    "plt.xlabel('Sample Size')\n",
    "plt.ylabel('Regression Value')\n",
    "plt.title('Regression of Sample Size for Fisher Villages')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Print the regression equations for each village\n",
    "slope_A = coefficients_A[0]\n",
    "intercept_A = coefficients_A[1]\n",
    "print(\"Regression Equation for Village A: y = {:.4f}x + {:.4f}\".format(slope_A, intercept_A))\n",
    "\n",
    "slope_B = coefficients_B[0]\n",
    "intercept_B = coefficients_B[1]\n",
    "print(\"Regression Equation for Village B: y = {:.4f}x + {:.4f}\".format(slope_B, intercept_B))\n",
    "\n",
    "slope_C = coefficients_C[0]\n",
    "intercept_C = coefficients_C[1]\n",
    "print(\"Regression Equation for Village C: y = {:.4f}x + {:.4f}\".format(slope_C, intercept_C))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "063294d6",
   "metadata": {},
   "source": [
    "The results you provided are regression equations for three different villages: Village A, Village B, and Village C. Each equation represents a linear relationship between the dependent variable (y) and the independent variable (x) for each village. Here's an explanation of each regression equation:\n",
    "\n",
    "1. Regression Equation for Village A:\n",
    "   - Equation: y = 0.0004x - 0.1045\n",
    "\n",
    "   In Village A, the regression equation suggests a positive linear relationship between the dependent variable (y) and the independent variable (x). For every unit increase in x, the predicted value of y increases by 0.0004. The y-intercept of -0.1045 indicates that when x is 0, the predicted value of y is -0.1045.\n",
    "\n",
    "2. Regression Equation for Village B:\n",
    "   - Equation: y = -0.0060x + 0.8910\n",
    "\n",
    "   In Village B, the regression equation suggests a negative linear relationship between the dependent variable (y) and the independent variable (x). For every unit increase in x, the predicted value of y decreases by 0.0060. The y-intercept of 0.8910 indicates that when x is 0, the predicted value of y is 0.8910.\n",
    "\n",
    "3. Regression Equation for Village C:\n",
    "   - Equation: y = 0.0009x - 0.1576\n",
    "\n",
    "   In Village C, the regression equation suggests a positive linear relationship between the dependent variable (y) and the independent variable (x). For every unit increase in x, the predicted value of y increases by 0.0009. The y-intercept of -0.1576 indicates that when x is 0, the predicted value of y is -0.1576.\n",
    "\n",
    "These regression equations allow you to estimate or predict the value of the dependent variable (y) based on the given independent variable (x) for each village. The coefficients (0.0004, -0.0060, and 0.0009) represent the change in the predicted value of y for every unit increase in x, while the y-intercepts (-0.1045, 0.8910, and -0.1576) indicate the predicted value of y when x is 0."
   ]
  },
  {
   "cell_type": "raw",
   "id": "a444e4bd",
   "metadata": {},
   "source": [
    "code demonstrates how to perform linear regression on hypothetical datasets from three fisher villages (Village A, Village B, and Village C). Here's a breakdown of the code:\n",
    "\n",
    "The code imports the necessary libraries: numpy for numerical operations and matplotlib.pyplot for plotting.\n",
    "\n",
    "Three hypothetical datasets are created, each consisting of 150 sample sizes and corresponding regression values for a specific village. The np.random.randint function is used to generate random integers within a specified range for the sample sizes, and np.random.randn generates random numbers from a standard normal distribution for the regression values.\n",
    "\n",
    "Linear regression is performed on each village's dataset using the np.polyfit function. This function fits a polynomial of degree 1 (i.e., a straight line) to the data points. The resulting coefficients of the polynomial are stored in coefficients_A, coefficients_B, and coefficients_C.\n",
    "\n",
    "The regression lines are obtained by evaluating the polynomial using the np.polyval function. The regression lines for each village are stored in regression_line_A, regression_line_B, and regression_line_C.\n",
    "\n",
    "The code plots the scatter plots of the sample sizes and regression values for each village using plt.scatter. It also plots the regression lines using plt.plot, with different colors and labels for each village.\n",
    "\n",
    "Additional plot settings are applied, including axis labels, a title, and a legend.\n",
    "\n",
    "The plot is displayed using plt.show().\n",
    "\n",
    "The regression equations for each village are printed, which include the slope (slope_A, slope_B, slope_C) and y-intercept (intercept_A, intercept_B, intercept_C) of the regression lines. The values are formatted and displayed using print statements.\n",
    "\n",
    "By running this code, you can visualize the scatter plots of the sample sizes and regression values for each village, observe the regression lines, and obtain the regression equations for each village."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e06c0f54",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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