Student_Stress_FactorsBayesian_Analysis_project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8i2ubZljjgHd",
        "outputId": "e356cd81-1f31-4c56-9590-6550017834c6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow-probability in /usr/local/lib/python3.10/dist-packages (0.23.0)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.10/dist-packages (from tensorflow-probability) (1.4.0)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow-probability) (1.16.0)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow-probability) (1.25.2)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from tensorflow-probability) (4.4.2)\n",
            "Requirement already satisfied: cloudpickle>=1.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow-probability) (2.2.1)\n",
            "Requirement already satisfied: gast>=0.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow-probability) (0.5.4)\n",
            "Requirement already satisfied: dm-tree in /usr/local/lib/python3.10/dist-packages (from tensorflow-probability) (0.1.8)\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow-probability\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_probability as tfp\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "J3CEk_HVaOB0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tfd = tfp.distributions\n",
        "data = pd.read_csv('/content/Student Stress Factors (1).csv')\n",
        "COLUMN_NAMES = [\n",
        "    'Kindly Rate your Sleep Quality 😴',\n",
        "    'How many times a week do you suffer headaches 🤕?',\n",
        "    'How would you rate you academic performance 👩‍🎓?',\n",
        "    'how would you rate your study load?',\n",
        "    'How many times a week you practice extracurricular activities 🎾?',\n",
        "    'How would you rate your stress levels?'\n",
        "]"
      ],
      "metadata": {
        "id": "tluWySasaXTX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def convert_and_cast(column):\n",
        "    return tf.convert_to_tensor(data[column].values, dtype=tf.float32)\n",
        "\n",
        "sleep_quality = convert_and_cast(COLUMN_NAMES[0])\n",
        "headache_frequency = convert_and_cast(COLUMN_NAMES[1])\n",
        "academic_performance = convert_and_cast(COLUMN_NAMES[2])\n",
        "study_load = convert_and_cast(COLUMN_NAMES[3])\n",
        "extracurricular_activity = convert_and_cast(COLUMN_NAMES[4])\n",
        "stress_levels = convert_and_cast(COLUMN_NAMES[5])"
      ],
      "metadata": {
        "id": "_m2J5D5Iab-V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def joint_log_prob(sleep_quality, headache_frequency, academic_performance, study_load, extracurricular_activity, stress_levels, intercept, sleep_coef, headache_coef, academic_coef, study_coef, extracurricular_coef, sigma):\n",
        "    rv_intercept = tfd.Normal(loc=0., scale=10.)\n",
        "    rv_sleep_coef = tfd.Normal(loc=0., scale=10.)\n",
        "    rv_headache_coef = tfd.Normal(loc=0., scale=10.)\n",
        "    rv_academic_coef = tfd.Normal(loc=0., scale=10.)\n",
        "    rv_study_coef = tfd.Normal(loc=0., scale=10.)\n",
        "    rv_extracurricular_coef = tfd.Normal(loc=0., scale=10.)\n",
        "    rv_sigma = tfd.Exponential(rate=1.0)\n",
        "\n",
        "    mu = (intercept + sleep_coef * sleep_quality +\n",
        "          headache_coef * headache_frequency +\n",
        "          academic_coef * academic_performance +\n",
        "          study_coef * study_load +\n",
        "          extracurricular_coef * extracurricular_activity)\n",
        "\n",
        "    rv_stress_levels = tfd.Normal(loc=mu, scale=sigma)\n",
        "\n",
        "    return (\n",
        "        rv_intercept.log_prob(intercept) +\n",
        "        rv_sleep_coef.log_prob(sleep_coef) +\n",
        "        rv_headache_coef.log_prob(headache_coef) +\n",
        "        rv_academic_coef.log_prob(academic_coef) +\n",
        "        rv_study_coef.log_prob(study_coef) +\n",
        "        rv_extracurricular_coef.log_prob(extracurricular_coef) +\n",
        "        rv_sigma.log_prob(sigma) +\n",
        "        tf.reduce_sum(rv_stress_levels.log_prob(stress_levels))\n",
        "    )\n",
        "initial_state = [\n",
        "    tf.zeros([], name='init_intercept', dtype=tf.float32),\n",
        "    tf.zeros([], name='init_sleep_coef', dtype=tf.float32),\n",
        "    tf.zeros([], name='init_headache_coef', dtype=tf.float32),\n",
        "    tf.zeros([], name='init_academic_coef', dtype=tf.float32),\n",
        "    tf.zeros([], name='init_study_coef', dtype=tf.float32),\n",
        "    tf.zeros([], name='init_extracurricular_coef', dtype=tf.float32),\n",
        "    tf.ones([], name='init_sigma', dtype=tf.float32),\n",
        "]\n",
        "hmc_kernel = tfp.mcmc.HamiltonianMonteCarlo(\n",
        "    target_log_prob_fn=lambda intercept, sleep_coef, headache_coef, academic_coef, study_coef, extracurricular_coef, sigma: joint_log_prob(\n",
        "        sleep_quality, headache_frequency, academic_performance, study_load, extracurricular_activity, stress_levels,\n",
        "        intercept, sleep_coef, headache_coef, academic_coef, study_coef, extracurricular_coef, sigma),\n",
        "    step_size=0.01,\n",
        "    num_leapfrog_steps=3)\n",
        "num_results = 1000\n",
        "num_burnin_steps = 500\n"
      ],
      "metadata": {
        "id": "B2d0Y7DeamLA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def run_chain():\n",
        "    return tfp.mcmc.sample_chain(\n",
        "        num_results=num_results,\n",
        "        num_burnin_steps=num_burnin_steps,\n",
        "        current_state=initial_state,\n",
        "        kernel=hmc_kernel,\n",
        "        trace_fn=lambda _, pkr: pkr.is_accepted)\n",
        "\n",
        "states, is_accepted = run_chain()\n"
      ],
      "metadata": {
        "id": "FnFdHKoATE92"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "GMxWOSOrars-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "intercept_samples = states[0].numpy()\n",
        "sleep_coef_samples = states[1].numpy()\n",
        "headache_coef_samples = states[2].numpy()\n",
        "academic_coef_samples = states[3].numpy()\n",
        "study_coef_samples = states[4].numpy()\n",
        "extracurricular_coef_samples = states[5].numpy()\n",
        "sigma_samples = states[6].numpy()\n",
        "acceptance_rate = tf.reduce_mean(tf.cast(is_accepted, dtype=tf.float32)).numpy()\n",
        "print('Acceptance rate:', acceptance_rate)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D32jaf98av84",
        "outputId": "680986b5-5d25-4a8a-f3ad-d21e245bcdd1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acceptance rate: 0.989\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def summarize_and_plot(parameter_samples, parameter_name):\n",
        "    print(f'{parameter_name}: Mean = {np.mean(parameter_samples):.4f}, Standard deviation = {np.std(parameter_samples):.4f}')\n",
        "    plt.hist(parameter_samples, bins=30, density=True)\n",
        "    plt.title(f'Posterior distribution of {parameter_name}')\n",
        "    plt.xlabel(parameter_name)\n",
        "    plt.ylabel('Density')\n",
        "    plt.show()\n",
        "summarize_and_plot(intercept_samples, 'Intercept')\n",
        "summarize_and_plot(sleep_coef_samples, 'Sleep Coefficient')\n",
        "summarize_and_plot(headache_coef_samples, 'Headache Coefficient')\n",
        "summarize_and_plot(academic_coef_samples, 'Academic Performance Coefficient')\n",
        "summarize_and_plot(study_coef_samples, 'Study Load Coefficient')\n",
        "summarize_and_plot(extracurricular_coef_samples, 'Extracurricular Activities Coefficient')\n",
        "summarize_and_plot(sigma_samples, 'Sigma (Standard deviation of stress levels)')\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "4xwJbyJrjnlz",
        "outputId": "e5205ae1-c3d0-4922-826d-b3300cf4672a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intercept: Mean = -0.2358, Standard deviation = 0.4623\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sleep Coefficient: Mean = 0.4477, Standard deviation = 0.1262\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Headache Coefficient: Mean = 0.0067, Standard deviation = 0.1040\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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Vq1Z1WpbVKbYMMTExOnPmjHr06OFUX4kSJdSyZcss68vq7+Lq5wG5x2mpW8i4ceN05513qmTJkvL391ft2rUdb3yHDx+Wm5ubatas6bROQECAypcvr8OHD0uSOnTooEceeUQjRozQRx99pI4dOyo8PFxPPPHEda8S2L9/v4wx+te//qV//etfWfZJSkpSlSpVHPerV69+3f3KqK127dpO7e7u7rrjjjscyzNUqVIlV1caZaxXq1atTMtq167t9GGSlffff18REREKDAxU8+bNdf/996tnz5664447rvvYGXKz/xkqV66c6XLnO++8U9KV8/WtWrXK9bbyIrvxl64c/l+8eLFjMniGaz8kKlSoIOnKh7WXl1eeH6dOnTpavXp1/nbg/2vRooVjwv21tV19umrfvn0yxmT5upDkdAo0Li5Ow4YN048//qjTp0879UtOTpaU99fZ/Pnz9e9//1tbt251mg939QftgQMH5ObmlqsQfu1zIV3Z56vr3bdvn3777TfHnI9rZUzgzkrG83n27Nnr1iJdGY+WLVtmaq9bt65jeYMGDbRv3z7t3r07XzXlRUa4yOvXAezbt0/S/+Y2Xuva17mHh0emfbn2eUDuEW5uIdm9eV8tp/+JZCyfNWuWfv31V/3f//2fFi9erGeeeUZjxozRr7/+muO8jIyJh0OGDFFoaGiWfa4NV7k9apEXN2KbWenevbvatWunuXPnasmSJfrggw/03nvvac6cOerSpUuutlHYtWb3/GZMWiwq2R2NM8YUaR35kZ6eLpvNpp9++inL/cj4G0hLS1OnTp106tQpvfbaa6pTp448PT117Ngx9erV67oTcbPyyy+/6MEHH1T79u31+eefq1KlSipVqpQmT56sadOm5Wt/cvNcpKenq1OnTnr11Vez7JsRorNSp04dSdL27dsdX55XGNLT09WwYUN9+OGHWS4PDAwstMfKj4znd+rUqQoICMi0/Nqrw7jSqnARbiDpyneXpKena9++fY7/IUlXJvedOXNG1apVc+rfqlUrtWrVSu+8846mTZumJ598UtOnT1ffvn2z/QDNOGJRqlSpQv3ukYza9u7d63RU5NKlSzp48GC+Hytjuxn/A7va3r17c7WNSpUq6YUXXtALL7ygpKQkNWvWTO+8844j3FwvTObFn3/+mekIye+//y5JjomLGUdIrr2K7NqjW3mp7erxv9aePXtUsWLFbL9ALy+ufpxr/ze8d+/eTK/RG6VGjRoyxqh69eo5fqhv375dv//+u6ZMmaKePXs62mNiYpz65eV1Nnv2bHl4eGjx4sVOR0onT56cqcb09HTt2rVLTZo0yfW+ZadGjRo6d+5cvv6W2rZtqwoVKug///mPXn/99et+iFerVi3b11LG8oyatm3bpnvvvbdQ/46ulfGesmPHjjytlzGJ3c/Pr9De727kfloNc24gSbr//vslKdNVDxn/K8q4CuP06dOZ/ned8eaZcYg84ztjrv0A9fPzU8eOHfXFF18oPj4+Uw1ZXaWQGyEhIXJ3d9cnn3ziVNvXX3+t5ORkpytI8qJSpUpq0qSJpkyZ4jiFIF35cMo475+dtLQ0p3WkK/tfuXJlp1MJnp6emfrl119//aUvvvjCcf/SpUv64osv5Ovrq+bNm0v63xvu1XMk0tLSNHHixEzby21tV4/T1c/5jh07tGTJEsdrq6Duuusu+fn5acKECU5j+NNPP2n37t35fp7z6uGHH1aJEiU0YsSITH8LxhidPHlS0v/+J351H2OMxo4d67ROXl5nJUqUkM1mczrSdujQIc2bN8+pX3h4uNzc3PT2229nOkKUn6Nj3bt3V2xsrBYvXpxp2ZkzZ/TXX39lu26ZMmX02muvaffu3XrttdeyfPxvv/1W69evl3TlvWj9+vWKjY11LD9//rwmTpyooKAgx6m27t2769ixY/ryyy8zbe/ixYs5Xl2ZF76+vmrfvr0mTZqkuLg4p2U5jWVoaKi8vLz07rvvZjk/MT/vd9m9tyIzjtxAktS4cWNFRERo4sSJOnPmjDp06KD169drypQpCg8P1z333CNJmjJlij7//HM99NBDqlGjhs6ePasvv/xSXl5ejg+x0qVLq169evr+++915513ysfHRw0aNFCDBg00btw4tW3bVg0bNlS/fv10xx13KDExUbGxsTp69Ki2bduW59p9fX0VGRmpESNG6L777tODDz6ovXv36vPPP9fdd9+tp556Kt/jEhUVpbCwMLVt21bPPPOMTp06pU8//VT169fXuXPnsl3v7Nmzuv3229WtWzc1btxYZcuW1dKlS7VhwwaNGTPG0a958+b6/vvvNXjwYN19990qW7asHnjggXzVWrlyZb333ns6dOiQ7rzzTn3//ffaunWrJk6c6JgHUr9+fbVq1UqRkZE6deqUfHx8NH369Cw/nPJS2wcffKAuXbooODhYffr0cVwK7u3tXWjfw1GqVCm999576t27tzp06KAePXo4LgUPCgpyfKX+jVajRg39+9//VmRkpA4dOqTw8HCVK1dOBw8e1Ny5c/Xss89qyJAhqlOnjmrUqKEhQ4bo2LFj8vLy0uzZs7OcQ5Hb11lYWJg+/PBD3XfffXriiSeUlJSkcePGqWbNmvrtt98c/WrWrKk33nhDI0eOVLt27fTwww/Lbrdrw4YNqly5suOS6Nx65ZVX9OOPP+rvf/+74zLx8+fPa/v27Zo1a5YOHTqkihUr5rj+zp07NWbMGK1YsULdunVTQECAEhISNG/ePK1fv15r166VJA0dOlT/+c9/1KVLF7344ovy8fHRlClTdPDgQc2ePdsxT/Dpp5/WjBkz9Pzzz2vFihVq06aN0tLStGfPHs2YMUOLFy++7mn43Prkk0/Utm1bNWvWTM8++6yqV6+uQ4cOacGCBdq6dWuW63h5eWn8+PF6+umn1axZMz3++OPy9fVVXFycFixYoDZt2uizzz7LUx05vbfiGi64QgtFLLeXxV6+fNmMGDHCVK9e3ZQqVcoEBgaayMhIp8u2N2/ebHr06GGqVq1q7Ha78fPzM3//+9/Nxo0bnba1du1a07x5c+Pu7p7p0sUDBw6Ynj17moCAAFOqVClTpUoV8/e//93MmjUrVzVndznzZ599ZurUqWNKlSpl/P39Tf/+/c3p06ed+mR3SWhOZs+eberWrWvsdrupV6+emTNnjomIiMjxUvDU1FTzyiuvmMaNG5ty5coZT09P07hxY/P55587rXPu3DnzxBNPmPLlyztd9ptxafbMmTMz1ZPdpeD169c3GzduNMHBwcbDw8NUq1bNfPbZZ5nWP3DggAkJCTF2u934+/ub119/3cTExGTaZna1ZXWprDHGLF261LRp08aULl3aeHl5mQceeMDs2rXLqU/GpeDHjx93as/uOc3K999/b5o2bWrsdrvx8fExTz75pDl69GiW28vLpeDZ9c3uNTN79mzTtm1b4+npaTw9PU2dOnXMgAEDzN69ex19du3aZUJCQkzZsmVNxYoVTb9+/cy2bduyHL/cvs6+/vprU6tWLWO3202dOnXM5MmTHeN6rUmTJjnGqkKFCqZDhw4mJibGsTyrS6Ez9rlDhw5ObWfPnjWRkZGmZs2axt3d3VSsWNG0bt3ajB492vFVA9cza9Ys07lzZ+Pj42NKlixpKlWqZB577DGzcuVKp34HDhww3bp1M+XLlzceHh6mRYsWZv78+Zm2d+nSJfPee++Z+vXrO/axefPmZsSIESY5OdlpfwpyKbgxxuzYscM89NBDjppq165t/vWvfzmW5/Q1C6Ghocbb29t4eHiYGjVqmF69ejm9Z0ZERBhPT89M9WX1vOb03or/sRlzE8zgAwAAyCXm3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEu55b7ELz09XX/++afKlSvHV1kDAHCTMMbo7Nmzqly5cqZfu7/WLRdu/vzzT5f/oBoAAMifI0eO6Pbbb8+xzy0XbsqVKyfpyuBc+5PzAACgeEpJSVFgYKDjczwnt1y4yTgV5eXlRbgBAOAmk5spJUwoBgAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAlkK4AQAAllLS1QUAQGEKGrog3+seGhVWiJUAcBWO3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEspNuFm1KhRstlsGjRoUI79Zs6cqTp16sjDw0MNGzbUwoULi6ZAAABwUygW4WbDhg364osv1KhRoxz7rV27Vj169FCfPn20ZcsWhYeHKzw8XDt27CiiSgEAQHHn8nBz7tw5Pfnkk/ryyy9VoUKFHPuOHTtW9913n1555RXVrVtXI0eOVLNmzfTZZ58VUbUAAKC4c3m4GTBggMLCwhQSEnLdvrGxsZn6hYaGKjY2Ntt1UlNTlZKS4nQDAADWVdKVDz59+nRt3rxZGzZsyFX/hIQE+fv7O7X5+/srISEh23WioqI0YsSIAtUJAABuHi47cnPkyBG99NJL+u677+Th4XHDHicyMlLJycmO25EjR27YYwEAANdz2ZGbTZs2KSkpSc2aNXO0paWladWqVfrss8+UmpqqEiVKOK0TEBCgxMREp7bExEQFBARk+zh2u112u71wiwcAAMWWy47c3Hvvvdq+fbu2bt3quN1111168skntXXr1kzBRpKCg4O1bNkyp7aYmBgFBwcXVdkAAKCYc9mRm3LlyqlBgwZObZ6enrrtttsc7T179lSVKlUUFRUlSXrppZfUoUMHjRkzRmFhYZo+fbo2btyoiRMnFnn9AACgeHL51VI5iYuLU3x8vON+69atNW3aNE2cOFGNGzfWrFmzNG/evEwhCQAA3Lpsxhjj6iKKUkpKiry9vZWcnCwvLy9XlwOgkAUNXZDvdQ+NCivESgAUprx8fhfrIzcAAAB5RbgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACWQrgBAACW4rKfXwCA7BTki/gAgCM3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUkq6ugAA1hQ0dIGrS8izgtR8aFRYIVYCoCA4cgMAACyFcAMAACyFcAMAACzFpeFm/PjxatSokby8vOTl5aXg4GD99NNP2faPjo6WzWZzunl4eBRhxQAAoLhz6YTi22+/XaNGjVKtWrVkjNGUKVPUtWtXbdmyRfXr189yHS8vL+3du9dx32azFVW5AADgJuDScPPAAw843X/nnXc0fvx4/frrr9mGG5vNpoCAgKIoDwAA3ISKzZybtLQ0TZ8+XefPn1dwcHC2/c6dO6dq1aopMDBQXbt21c6dO3PcbmpqqlJSUpxuAADAulwebrZv366yZcvKbrfr+eef19y5c1WvXr0s+9auXVuTJk3SDz/8oG+//Vbp6elq3bq1jh49mu32o6Ki5O3t7bgFBgbeqF0BAADFgM0YY1xZwKVLlxQXF6fk5GTNmjVLX331lX7++edsA87VLl++rLp166pHjx4aOXJkln1SU1OVmprquJ+SkqLAwEAlJyfLy8ur0PYDgLOb8Uv8CoIv8QNurJSUFHl7e+fq89vl31Ds7u6umjVrSpKaN2+uDRs2aOzYsfriiy+uu26pUqXUtGlT7d+/P9s+drtddru90OoFAADFm8tPS10rPT3d6UhLTtLS0rR9+3ZVqlTpBlcFAABuFi49chMZGakuXbqoatWqOnv2rKZNm6aVK1dq8eLFkqSePXuqSpUqioqKkiS9/fbbatWqlWrWrKkzZ87ogw8+0OHDh9W3b19X7gYAAChGXBpukpKS1LNnT8XHx8vb21uNGjXS4sWL1alTJ0lSXFyc3Nz+d3Dp9OnT6tevnxISElShQgU1b95ca9euzdX8HAAAcGtw+YTiopaXCUkA8o8JxQAKU14+v4vdnBsAAICCINwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLKenqAgAUX0FDF7i6BADIM47cAAAASyHcAAAASyHcAAAASyHcAAAASyHcAAAASyHcAAAASyHcAAAASyHcAAAAS+FL/ACgEBTkCw8PjQorxEoAuPTIzfjx49WoUSN5eXnJy8tLwcHB+umnn3JcZ+bMmapTp448PDzUsGFDLVy4sIiqBQAANwOXhpvbb79do0aN0qZNm7Rx40b97W9/U9euXbVz584s+69du1Y9evRQnz59tGXLFoWHhys8PFw7duwo4soBAEBxZTPGGFcXcTUfHx998MEH6tOnT6Zljz32mM6fP6/58+c72lq1aqUmTZpowoQJudp+SkqKvL29lZycLC8vr0KrG7AifluqaHBaCri+vHx+F5sJxWlpaZo+fbrOnz+v4ODgLPvExsYqJCTEqS00NFSxsbHZbjc1NVUpKSlONwAAYF0uDzfbt29X2bJlZbfb9fzzz2vu3LmqV69eln0TEhLk7+/v1Obv76+EhIRstx8VFSVvb2/HLTAwsFDrBwAAxYvLw03t2rW1detWrVu3Tv3791dERIR27dpVaNuPjIxUcnKy43bkyJFC2zYAACh+XH4puLu7u2rWrClJat68uTZs2KCxY8fqiy++yNQ3ICBAiYmJTm2JiYkKCAjIdvt2u112u71wiwYAAMWWy4/cXCs9PV2pqalZLgsODtayZcuc2mJiYrKdowMAAG49Lj1yExkZqS5duqhq1ao6e/aspk2bppUrV2rx4sWSpJ49e6pKlSqKioqSJL300kvq0KGDxowZo7CwME2fPl0bN27UxIkTXbkbAACgGHFpuElKSlLPnj0VHx8vb29vNWrUSIsXL1anTp0kSXFxcXJz+9/BpdatW2vatGl688039frrr6tWrVqaN2+eGjRo4KpdAAAAxUyx+56bG43vuQFyj++5KRp8zw1wfTfl99wAAAAUBpdfLQXg+vhRRgDIPY7cAAAASyHcAAAASyHcAAAASyHcAAAAS2FCMWBxXM4N4FbDkRsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGAphBsAAGApfM8NUET4vhkAKBocuQEAAJZCuAEAAJZCuAEAAJZCuAEAAJbChGIAcLGCTDY/NCqsECsBrIEjNwAAwFIINwAAwFIINwAAwFKYcwPkAV/EBwDFH0duAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApbg03ERFRenuu+9WuXLl5Ofnp/DwcO3duzfHdaKjo2Wz2ZxuHh4eRVQxAAAo7lwabn7++WcNGDBAv/76q2JiYnT58mV17txZ58+fz3E9Ly8vxcfHO26HDx8uoooBAEBx59Iv8Vu0aJHT/ejoaPn5+WnTpk1q3759tuvZbDYFBATc6PIAAMBNqFjNuUlOTpYk+fj45Njv3LlzqlatmgIDA9W1a1ft3Lkz276pqalKSUlxugEAAOvKV7j5448/CrsOpaena9CgQWrTpo0aNGiQbb/atWtr0qRJ+uGHH/Ttt98qPT1drVu31tGjR7PsHxUVJW9vb8ctMDCw0GsHAADFh80YY/K6kpubmzp06KA+ffqoW7duhTKht3///vrpp5+0evVq3X777ble7/Lly6pbt6569OihkSNHZlqempqq1NRUx/2UlBQFBgYqOTlZXl5eBa4btxZ+WwrFzaFRYa4uASgSKSkp8vb2ztXnd76O3GzevFmNGjXS4MGDFRAQoOeee07r16/PV7GSNHDgQM2fP18rVqzIU7CRpFKlSqlp06bav39/lsvtdru8vLycbgAAwLryFW6aNGmisWPH6s8//9SkSZMUHx+vtm3bqkGDBvrwww91/PjxXG3HGKOBAwdq7ty5Wr58uapXr57nWtLS0rR9+3ZVqlQpz+sCAADrKdCE4pIlS+rhhx/WzJkz9d5772n//v0aMmSIAgMD1bNnT8XHx+e4/oABA/Ttt99q2rRpKleunBISEpSQkKCLFy86+vTs2VORkZGO+2+//baWLFmiP/74Q5s3b9ZTTz2lw4cPq2/fvgXZFQAAYBEFCjcbN27UCy+8oEqVKunDDz/UkCFDdODAAcXExOjPP/9U165dc1x//PjxSk5OVseOHVWpUiXH7fvvv3f0iYuLcwpJp0+fVr9+/VS3bl3df//9SklJ0dq1a1WvXr2C7AoAALCIfE0o/vDDDzV58mTt3btX999/v/r27av7779fbm7/y0pHjx5VUFCQ/vrrr0ItuKDyMiEJuBYTilHcMKEYt4q8fH7n60v8xo8fr2eeeUa9evXKdq6Ln5+fvv766/xsHgAAIN/yFW5iYmJUtWpVpyM10pUJwkeOHFHVqlXl7u6uiIiIQikSAAAgt/I156ZGjRo6ceJEpvZTp07l64onAACAwpKvcJPdNJ1z587xC90AAMCl8nRaavDgwZKu/HDlsGHDVKZMGceytLQ0rVu3Tk2aNCnUAgEAAPIiT+Fmy5Ytkq4cudm+fbvc3d0dy9zd3dW4cWMNGTKkcCsEChlXPMFKCvJ65korWFWews2KFSskSb1799bYsWO5lBoAABQ7+bpaavLkyYVdBwAAQKHIdbh5+OGHFR0dLS8vLz388MM59p0zZ06BCwMAAMiPXIcbb29v2Ww2x78BAACKo1yHm6tPRXFaCgAAFFf5+p6bixcv6sKFC477hw8f1scff6wlS5YUWmEAAAD5ka9w07VrV33zzTeSpDNnzqhFixYaM2aMunbtqvHjxxdqgQAAAHmRr3CzefNmtWvXTpI0a9YsBQQE6PDhw/rmm2/0ySefFGqBAAAAeZGvcHPhwgWVK1dOkrRkyRI9/PDDcnNzU6tWrXT48OFCLRAAACAv8hVuatasqXnz5unIkSNavHixOnfuLElKSkrii/0AAIBL5SvcDBs2TEOGDFFQUJBatmyp4OBgSVeO4jRt2rRQCwQAAMiLfH1Dcbdu3dS2bVvFx8ercePGjvZ7771XDz30UKEVBwAAkFf5CjeSFBAQoICAAKe2Fi1aFLggAACAgshXuDl//rxGjRqlZcuWKSkpSenp6U7L//jjj0IpDgAAIK/yFW769u2rn3/+WU8//bQqVark+FkGAAAAV8tXuPnpp5+0YMECtWnTprDrAQAAKJB8XS1VoUIF+fj4FHYtAAAABZavcDNy5EgNGzbM6felAAAAioN8nZYaM2aMDhw4IH9/fwUFBalUqVJOyzdv3lwoxQEAAORVvsJNeHh4IZcBAABQOPIVboYPH17YdQAAABSKfM25kaQzZ87oq6++UmRkpE6dOiXpyumoY8eOFVpxAAAAeZWvIze//fabQkJC5O3trUOHDqlfv37y8fHRnDlzFBcXp2+++aaw6wQAAMiVfB25GTx4sHr16qV9+/bJw8PD0X7//fdr1apVhVYcAABAXuUr3GzYsEHPPfdcpvYqVaooISGhwEUBAADkV77Cjd1uV0pKSqb233//Xb6+vrneTlRUlO6++26VK1dOfn5+Cg8P1969e6+73syZM1WnTh15eHioYcOGWrhwYZ7qBwAA1pWvcPPggw/q7bff1uXLlyVJNptNcXFxeu211/TII4/kejs///yzBgwYoF9//VUxMTG6fPmyOnfurPPnz2e7ztq1a9WjRw/16dNHW7ZsUXh4uMLDw7Vjx4787AoAALAYmzHG5HWl5ORkdevWTRs2bNC5c+dUuXJlJSQkKDg4WAsXLpSnp2e+ijl+/Lj8/Pz0888/q3379ln2eeyxx3T+/HnNnz/f0daqVSs1adJEEyZMuO5jpKSkyNvbW8nJyfLy8spXnbi5BQ1d4OoSgGLh0KgwV5cA5FpePr/zdbWUt7e3YmJitGbNGm3btk3nzp1Ts2bNFBISkq+CMyQnJ0tSjr9bFRsbq8GDBzu1hYaGat68eVn2T01NVWpqquN+VqfTAACAdeQ53KSnpys6Olpz5szRoUOHZLPZVL16dQUEBMgYI5vNlq9C0tPTNWjQILVp00YNGjTItl9CQoL8/f2d2vz9/bOdyBwVFaURI0bkqyYAAHDzyVO4McbowQcf1MKFC9W4cWM1bNhQxhjt3r1bvXr10pw5c7I9gnI9AwYM0I4dO7R69ep8rZ+dyMhIpyM9KSkpCgwMLNTHAICbUUFO0XJKC8VZnsJNdHS0Vq1apWXLlumee+5xWrZ8+XKFh4frm2++Uc+ePfNUxMCBAzV//nytWrVKt99+e459AwIClJiY6NSWmJiogICALPvb7XbZ7fY81QMAAG5eebpa6j//+Y9ef/31TMFGkv72t79p6NCh+u6773K9PWOMBg4cqLlz52r58uWqXr36ddcJDg7WsmXLnNpiYmIUHByc68cFAADWladw89tvv+m+++7LdnmXLl20bdu2XG9vwIAB+vbbbzVt2jSVK1dOCQkJSkhI0MWLFx19evbsqcjISMf9l156SYsWLdKYMWO0Z88evfXWW9q4caMGDhyYl10BAAAWladwc+rUqUyTea/m7++v06dP53p748ePV3Jysjp27KhKlSo5bt9//72jT1xcnOLj4x33W7durWnTpmnixIlq3LixZs2apXnz5uU4CRkAANw68jTnJi0tTSVLZr9KiRIl9Ndff+V6e7n5ip2VK1dmanv00Uf16KOP5vpxAADArSPPV0v16tUr2wm6V3+fDAAAgCvkKdxERERct09er5QCAAAoTHkKN5MnT75RdQAAABSKfP1wJgAAQHFFuAEAAJZCuAEAAJZCuAEAAJaS518FBwoLP9oHALgROHIDAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshXADAAAshS/xAwDkGV/CieKMIzcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSmFCMm1JBJjMCAKyNIzcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSXBpuVq1apQceeECVK1eWzWbTvHnzcuy/cuVK2Wy2TLeEhISiKRgAABR7Lg0358+fV+PGjTVu3Lg8rbd3717Fx8c7bn5+fjeoQgAAcLNx6c8vdOnSRV26dMnzen5+fipfvnzhFwQAAG56N+WcmyZNmqhSpUrq1KmT1qxZk2Pf1NRUpaSkON0AAIB13VThplKlSpowYYJmz56t2bNnKzAwUB07dtTmzZuzXScqKkre3t6OW2BgYBFWDAAAiprNGGNcXYQk2Ww2zZ07V+Hh4Xlar0OHDqpataqmTp2a5fLU1FSlpqY67qekpCgwMFDJycny8vIqSMkoIH7ZG7g1HRoV5uoScBNKSUmRt7d3rj6/XTrnpjC0aNFCq1evzna53W6X3W4vwooAAIAr3VSnpbKydetWVapUydVlAACAYsKlR27OnTun/fv3O+4fPHhQW7dulY+Pj6pWrarIyEgdO3ZM33zzjSTp448/VvXq1VW/fn3997//1VdffaXly5dryZIlrtoFAABQzLg03GzcuFH33HOP4/7gwYMlSREREYqOjlZ8fLzi4uIcyy9duqSXX35Zx44dU5kyZdSoUSMtXbrUaRsAAODWVmwmFBeVvExIwo3FhGLg1sSEYuRHXj6/b/o5NwAAAFcj3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsh3AAAAEsp6eoCAAC3lqChCwq0/qFRYYVUCayKIzcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBSCDcAAMBS+FVwFEhBf90XAIDC5tIjN6tWrdIDDzygypUry2azad68edddZ+XKlWrWrJnsdrtq1qyp6OjoG14nAAC4ebg03Jw/f16NGzfWuHHjctX/4MGDCgsL0z333KOtW7dq0KBB6tu3rxYvXnyDKwUAADcLl56W6tKli7p06ZLr/hMmTFD16tU1ZswYSVLdunW1evVqffTRRwoNDb1RZQIAgJvITTWhODY2ViEhIU5toaGhio2NdVFFAACguLmpJhQnJCTI39/fqc3f318pKSm6ePGiSpcunWmd1NRUpaamOu6npKTc8DoBAIDr3FRHbvIjKipK3t7ejltgYKCrSwIAADfQTRVuAgIClJiY6NSWmJgoLy+vLI/aSFJkZKSSk5MdtyNHjhRFqQAAwEVuqtNSwcHBWrhwoVNbTEyMgoODs13HbrfLbrff6NIAAEAx4dIjN+fOndPWrVu1detWSVcu9d66davi4uIkXTnq0rNnT0f/559/Xn/88YdeffVV7dmzR59//rlmzJihf/7zn64oHwAAFEMuDTcbN25U06ZN1bRpU0nS4MGD1bRpUw0bNkySFB8f7wg6klS9enUtWLBAMTExaty4scaMGaOvvvqKy8ABAICDzRhjXF1EUUpJSZG3t7eSk5Pl5eXl6nJuevz8AoCidmhUmKtLgAvk5fP7pppQDAAAcD2EGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCk31Zf4AQBQkKs0udLq1sCRGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCklXV0AXC9o6AJXlwAAQKHhyA0AALAUwg0AALCUYhFuxo0bp6CgIHl4eKhly5Zav359tn2jo6Nls9mcbh4eHkVYLQAAKM5cHm6+//57DR48WMOHD9fmzZvVuHFjhYaGKikpKdt1vLy8FB8f77gdPny4CCsGAADFmcvDzYcffqh+/fqpd+/eqlevniZMmKAyZcpo0qRJ2a5js9kUEBDguPn7+xdhxQAAoDhzabi5dOmSNm3apJCQEEebm5ubQkJCFBsbm+16586dU7Vq1RQYGKiuXbtq586d2fZNTU1VSkqK0w0AAFiXS8PNiRMnlJaWlunIi7+/vxISErJcp3bt2po0aZJ++OEHffvtt0pPT1fr1q119OjRLPtHRUXJ29vbcQsMDCz0/QAAAMWHy09L5VVwcLB69uypJk2aqEOHDpozZ458fX31xRdfZNk/MjJSycnJjtuRI0eKuGIAAFCUXPolfhUrVlSJEiWUmJjo1J6YmKiAgIBcbaNUqVJq2rSp9u/fn+Vyu90uu91e4FoBAMDNwaVHbtzd3dW8eXMtW7bM0Zaenq5ly5YpODg4V9tIS0vT9u3bValSpRtVJgAAuIm4/OcXBg8erIiICN11111q0aKFPv74Y50/f169e/eWJPXs2VNVqlRRVFSUJOntt99Wq1atVLNmTZ05c0YffPCBDh8+rL59+7pyNwAAQDHh8nDz2GOP6fjx4xo2bJgSEhLUpEkTLVq0yDHJOC4uTm5u/zvAdPr0afXr108JCQmqUKGCmjdvrrVr16pevXqu2gUAAFCM2IwxxtVFFKWUlBR5e3srOTlZXl5eri6nWOCHMwHcKg6NCnN1CcinvHx+33RXSwEAAOSEcAMAACyFcAMAACyFcAMAACzF5VdLWU1BJucy0Q0ArInPhqLFkRsAAGAphBsAAGAphBsAAGApzLmxCL6IDwCuj/fKWwNHbgAAgKUQbgAAgKUQbgAAgKUw56YY4VwwAKAwuepzxdXfzcORGwAAYCmEGwAAYCmEGwAAYCmEGwAAYClMKAYAoBjjYpO848gNAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwlGIRbsaNG6egoCB5eHioZcuWWr9+fY79Z86cqTp16sjDw0MNGzbUwoULi6hSAABQ3Lk83Hz//fcaPHiwhg8frs2bN6tx48YKDQ1VUlJSlv3Xrl2rHj16qE+fPtqyZYvCw8MVHh6uHTt2FHHlAACgOLIZY4wrC2jZsqXuvvtuffbZZ5Kk9PR0BQYG6h//+IeGDh2aqf9jjz2m8+fPa/78+Y62Vq1aqUmTJpowYcJ1Hy8lJUXe3t5KTk6Wl5dX4e3I/8dP0wMAbnWHRoUV+jbz8vnt0iM3ly5d0qZNmxQSEuJoc3NzU0hIiGJjY7NcJzY21qm/JIWGhmbbHwAA3FpKuvLBT5w4obS0NPn7+zu1+/v7a8+ePVmuk5CQkGX/hISELPunpqYqNTXVcT85OVnSlQR4I6SnXrgh2wUA4GZxIz5jM7aZmxNOLg03RSEqKkojRozI1B4YGOiCagAAsD7vj2/cts+ePStvb+8c+7g03FSsWFElSpRQYmKiU3tiYqICAgKyXCcgICBP/SMjIzV48GDH/fT0dJ06dUq33XabbDZbAffg5pWSkqLAwEAdOXLkhsw9uhUwhgXD+BUcY1hwjGHBFdUYGmN09uxZVa5c+bp9XRpu3N3d1bx5cy1btkzh4eGSroSPZcuWaeDAgVmuExwcrGXLlmnQoEGOtpiYGAUHB2fZ3263y263O7WVL1++MMq3BC8vL/6gC4gxLBjGr+AYw4JjDAuuKMbwekdsMrj8tNTgwYMVERGhu+66Sy1atNDHH3+s8+fPq3fv3pKknj17qkqVKoqKipIkvfTSS+rQoYPGjBmjsLAwTZ8+XRs3btTEiRNduRsAAKCYcHm4eeyxx3T8+HENGzZMCQkJatKkiRYtWuSYNBwXFyc3t/9d1NW6dWtNmzZNb775pl5//XXVqlVL8+bNU4MGDVy1CwAAoBhxebiRpIEDB2Z7GmrlypWZ2h599FE9+uijN7gqa7Pb7Ro+fHimU3bIPcawYBi/gmMMC44xLLjiOIYu/xI/AACAwuTyn18AAAAoTIQbAABgKYQbAABgKYQbAABgKYSbW8ipU6f05JNPysvLS+XLl1efPn107ty5HPv/4x//UO3atVW6dGlVrVpVL774ouP3uW41eR0/SZo4caI6duwoLy8v2Ww2nTlzpmiKLSbGjRunoKAgeXh4qGXLllq/fn2O/WfOnKk6derIw8NDDRs21MKFC4uo0uIrL2O4c+dOPfLIIwoKCpLNZtPHH39cdIUWY3kZwy+//FLt2rVThQoVVKFCBYWEhFz3dWt1eRm/OXPm6K677lL58uXl6empJk2aaOrUqUVY7RWEm1vIk08+qZ07dyomJkbz58/XqlWr9Oyzz2bb/88//9Sff/6p0aNHa8eOHYqOjtaiRYvUp0+fIqy6+Mjr+EnShQsXdN999+n1118voiqLj++//16DBw/W8OHDtXnzZjVu3FihoaFKSkrKsv/atWvVo0cP9enTR1u2bFF4eLjCw8O1Y8eOIq68+MjrGF64cEF33HGHRo0ale1P0txq8jqGK1euVI8ePbRixQrFxsYqMDBQnTt31rFjx4q48uIhr+Pn4+OjN954Q7Gxsfrtt9/Uu3dv9e7dW4sXLy7awg1uCbt27TKSzIYNGxxtP/30k7HZbObYsWO53s6MGTOMu7u7uXz58o0os9gq6PitWLHCSDKnT5++gVUWLy1atDADBgxw3E9LSzOVK1c2UVFRWfbv3r27CQsLc2pr2bKlee65525oncVZXsfwatWqVTMfffTRDazu5lCQMTTGmL/++suUK1fOTJky5UaVWKwVdPyMMaZp06bmzTffvBHlZYsjN7eI2NhYlS9fXnfddZejLSQkRG5ublq3bl2ut5OcnCwvLy+VLFksvv+xyBTW+N0qLl26pE2bNikkJMTR5ubmppCQEMXGxma5TmxsrFN/SQoNDc22v9XlZwzhrDDG8MKFC7p8+bJ8fHxuVJnFVkHHzxijZcuWae/evWrfvv2NLDUTws0tIiEhQX5+fk5tJUuWlI+PjxISEnK1jRMnTmjkyJHXPRVjRYUxfreSEydOKC0tzfEzKhn8/f2zHa+EhIQ89be6/IwhnBXGGL722muqXLlypuB9K8jv+CUnJ6ts2bJyd3dXWFiYPv30U3Xq1OlGl+uEcHOTGzp0qGw2W463PXv2FPhxUlJSFBYWpnr16umtt94qeOHFRFGNH4Cbz6hRozR9+nTNnTtXHh4eri7nplGuXDlt3bpVGzZs0DvvvKPBgwdn+VNKN9KtdW7Bgl5++WX16tUrxz533HGHAgICMk0A++uvv3Tq1KnrTjw8e/as7rvvPpUrV05z585VqVKlClp2sVEU43crqlixokqUKKHExESn9sTExGzHKyAgIE/9rS4/YwhnBRnD0aNHa9SoUVq6dKkaNWp0I8sstvI7fm5ubqpZs6YkqUmTJtq9e7eioqLUsWPHG1mucw1F9ki4IXx9fVWnTp0cb+7u7goODtaZM2e0adMmx7rLly9Xenq6WrZsme32U1JS1LlzZ7m7u+vHH3+03P9ebvT43arc3d3VvHlzLVu2zNGWnp6uZcuWKTg4OMt1goODnfpLUkxMTLb9rS4/Ywhn+R3D999/XyNHjtSiRYuc5tndagrrNZienq7U1NQbUWL2inT6MlzqvvvuM02bNjXr1q0zq1evNrVq1TI9evRwLD969KipXbu2WbdunTHGmOTkZNOyZUvTsGFDs3//fhMfH++4/fXXX67aDZfJ6/gZY0x8fLzZsmWL+fLLL40ks2rVKrNlyxZz8uRJV+xCkZo+fbqx2+0mOjra7Nq1yzz77LOmfPnyJiEhwRhjzNNPP22GDh3q6L9mzRpTsmRJM3r0aLN7924zfPhwU6pUKbN9+3ZX7YLL5XUMU1NTzZYtW8yWLVtMpUqVzJAhQ8yWLVvMvn37XLULLpfXMRw1apRxd3c3s2bNcnrPO3v2rKt2waXyOn7vvvuuWbJkiTlw4IDZtWuXGT16tClZsqT58ssvi7Ruws0t5OTJk6ZHjx6mbNmyxsvLy/Tu3dvpD/bgwYNGklmxYoUx5n+XL2d1O3jwoGt2woXyOn7GGDN8+PAsx2/y5MlFvwMu8Omnn5qqVasad3d306JFC/Prr786lnXo0MFEREQ49Z8xY4a58847jbu7u6lfv75ZsGBBEVdc/ORlDDNeg9feOnToUPSFFyN5GcNq1aplOYbDhw8v+sKLibyM3xtvvGFq1qxpPDw8TIUKFUxwcLCZPn16kddsM8aYojtOBAAAcGMx5wYAAFgK4QYAAFgK4QYAAFgK4QYAAFgK4QYAAFgK4QYAAFgK4QYAAFgK4QZArq1cuVI2m01nzpwplO0dOnRINptNW7duLZTtuYIxRs8++6x8fHwc+5JVW8eOHTVo0KBcbbOwxxm41RBugJtIr169FB4enqmdD8PMLl26pPfff1+NGzdWmTJlVLFiRbVp00aTJ0/W5cuXC+1xFi1apOjoaM2fP1/x8fFq0KBBlm1z5szRyJEjc7XN1q1bKz4+Xt7e3oVWpxWCJJBb/Co4AMu5dOmSQkNDtW3bNo0cOVJt2rSRl5eXfv31V40ePVpNmzZVkyZNCuWxDhw4oEqVKql169Y5tvn4+OR6m+7u7vzyN1AAHLkBLGr16tVq166dSpcurcDAQL344os6f/68Y/nUqVN11113qVy5cgoICNATTzyhpKQkp20sXLhQd955p0qXLq177rlHhw4dclp+8uRJ9ejRQ1WqVFGZMmXUsGFD/ec//3Hqk56ervfff181a9aU3W5X1apV9c477zj1+eOPP3TPPfeoTJkyaty4sWJjY/O0L9f6+OOPtWrVKi1btkwDBgxQkyZNdMcdd+iJJ57QunXrVKtWLUlSamqqXnzxRfn5+cnDw0Nt27bVhg0bnLa1Y8cOdenSRWXLlpW/v7+efvppnThxQtKVI2n/+Mc/FBcXJ5vNpqCgoCzbJGU6LZWamqrXXntNgYGBstvtqlmzpr7++mtJWR+Ju94YBAUF6d1339UzzzyjcuXKqWrVqpo4caJjefXq1SVJTZs2lc1mU8eOHbMdP+CmV+S/ZgUg3yIiIkzXrl0ztWf8yOnp06eNMcbs37/feHp6mo8++sj8/vvvZs2aNaZp06amV69ejnW+/vprs3DhQnPgwAETGxtrgoODTZcuXRzL4+LijN1uN4MHDzZ79uwx3377rfH393d6nKNHj5oPPvjAbNmyxRw4cMB88sknpkSJEk6/jP7qq6+aChUqmOjoaLN//37zyy+/OH4hOOOHHuvUqWPmz59v9u7da7p162aqVatmLl++nOt9uVajRo1M586drzueL774oqlcubJZuHCh2blzp4mIiDAVKlRw/Gr76dOnja+vr4mMjDS7d+82mzdvNp06dTL33HOPMcaYM2fOmLffftvcfvvtJj4+3iQlJWXZZsyVHxh86aWXHI/dvXt3ExgYaObMmWMOHDhgli5d6viBwfw8n9WqVTM+Pj5m3LhxZt++fSYqKsq4ubmZPXv2GGOMWb9+vZFkli5dauLj42+JX6bHrYtwA9xEIiIiTIkSJYynp6fTzcPDw+nDsE+fPubZZ591WveXX34xbm5u5uLFi1lue8OGDUaS45fOIyMjTb169Zz6vPbaa06Pk5WwsDDz8ssvG2OMSUlJMXa73RFmrpURbr766itH286dO40ks3v37nzvS+nSpc2LL76YbY3GGHPu3DlTqlQp89133znaLl26ZCpXrmzef/99Y4wxI0eOzBSSjhw5YiSZvXv3GmOM+eijj0y1atWc+mTVdnW42bt3r5FkYmJisqzt2nCTmzGoVq2aeeqppxzL09PTjZ+fnxk/frwx5n9jvWXLlhzHBbAC5twAN5l77rlH48ePd2pbt26dnnrqKcf9bdu26bffftN3333naDPGKD09XQcPHlTdunW1adMmvfXWW9q2bZtOnz6t9PR0SVJcXJzq1aun3bt3q2XLlk6PExwc7HQ/LS1N7777rmbMmKFjx47p0qVLSk1NVZkyZSRJu3fvVmpqqu69994c96lRo0aOf1eqVEmSlJSUpDp16uRqX65ljMnx8aQr82IuX76sNm3aONpKlSqlFi1aaPfu3ZKujOOKFStUtmzZLNe/8847r/s4Wdm6datKlCihDh065Kp/bsfg6nG02WwKCAjIdKoRuBUQboCbjKenp2rWrOnUdvToUaf7586d03PPPacXX3wx0/pVq1bV+fPnFRoaqtDQUH333Xfy9fVVXFycQkNDdenSpVzX8sEHH2js2LH6+OOP1bBhQ3l6emrQoEGObZQuXTpX2ylVqpTj3zabTZIcYet6+5KVO++8U3v27Mn1fmTn3LlzeuCBB/Tee+9lWpYRwvIjt+NydR25GYOrx1G6MpYZ4wjcSgg3gAU1a9ZMu3btyhSCMmzfvl0nT57UqFGjFBgYKEnauHGjU5+6devqxx9/dGr79ddfne6vWbNGXbt2dRw1Sk9P1++//6569epJkmrVqqXSpUtr2bJl6tu37w3Zl6w88cQTev3117VlyxY1bdrUadnly5d16dIl1ahRQ+7u7lqzZo2qVavmWLZhwwbHxN9mzZpp9uzZCgoKUsmShfd22bBhQ6Wnp+vnn39WSEjIdfvnZwyu5e7uLunK0TbA6rhaCrCg1157TWvXrtXAgQO1detW7du3Tz/88IMGDhwo6cr/9t3d3fXpp5/qjz/+0I8//pjpO1ief/557du3T6+88or27t2radOmKTo62qlPrVq1FBMTo7Vr12r37t167rnnlJiY6Fju4eGh1157Ta+++qq++eYbHThwQL/++qvjqqDC2JesDBo0SG3atNG9996rcePGadu2bfrjjz80Y8YMtWrVSvv27ZOnp6f69++vV155RYsWLdKuXbvUr18/XbhwQX369JEkDRgwQKdOnVKPHj20YcMGHThwQIsXL1bv3r0LFBKCgoIUERGhZ555RvPmzdPBgwe1cuVKzZgxo9DG4Fp+fn4qXbq0Fi1apMTERCUnJ+e7fqC4I9wAFtSoUSP9/PPP+v3339WuXTs1bdpUw4YNU+XKlSVJvr6+io6O1syZM1WvXj2NGjVKo0ePdtpG1apVNXv2bM2bN0+NGzfWhAkT9O677zr1efPNN9WsWTOFhoaqY8eOCggIyPQlg//617/08ssva9iwYapbt64ee+yxPM0Dud6+ZMVutysmJkavvvqqvvjiC7Vq1Up33323PvnkE7344otq0KCBJGnUqFF65JFH9PTTT6tZs2bav3+/Fi9erAoVKkiSKleurDVr1igtLU2dO3dWw4YNNWjQIJUvX15ubgV7+xw/fry6deumF154QXXq1FG/fv2yvbw9P2NwrZIlS+qTTz7RF198ocqVK6tr164Fqh8ozmwmNzPvAAAAbhIcuQEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJby/wA0Hx/gehz9nwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Academic Performance Coefficient: Mean = -0.0311, Standard deviation = 0.1280\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Study Load Coefficient: Mean = 0.3948, Standard deviation = 0.1161\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracurricular Activities Coefficient: Mean = 0.1887, Standard deviation = 0.1038\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sigma (Standard deviation of stress levels): Mean = 1.3049, Standard deviation = 0.1187\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Results & Conclusions Summary:\n",
        "1. Intercept: The average starting point for stress level is estimated to be below zero (mean = -0.4243), suggesting that when all other factors are not taken into account, the base stress level starts from a lower point. This high acceptance rate for this parameter's estimates indicates a high degree of confidence in this model's predictions for the intercept.\n",
        "\n",
        "2. Sleep Coefficient: More sleep correlates with a positive change in stress levels (mean = 0.4491), implying that increased sleep potentially leads to better stress outcomes. The relatively small standard deviation suggests that this positive relationship is consistent across the data set.\n",
        "\n",
        "3. Headache Coefficient: The influence of headaches on stress levels appears to be minimal but slightly positive (mean = 0.0217), indicating that headaches might increase stress levels, but the effect is likely small.\n",
        "\n",
        "4. Academic Performance Coefficient: The association between academic performance and stress levels is slightly negative (mean = -0.0271), suggesting that better performance could be associated with a slight reduction in stress, although the effect is not pronounced.\n",
        "\n",
        "5. Study Load Coefficient: A heavier study load seems to correlate with higher stress levels (mean = 0.3711), pointing towards the conclusion that more academic work may lead to increased stress.\n",
        "\n",
        "6. Sigma (Standard Deviation of Stress Levels): The variability in stress levels among individuals (mean = 1.3154) is moderately high, indicating a significant spread of stress levels in the population studied. The model is fairly certain about this estimate of variability, as shown by the small standard deviation around the mean.\n",
        "\n",
        "Each graph represents the uncertainty and estimation of the effects of different factors on stress levels, with the posterior distributions providing a probabilistic understanding of these effects. The consistent and narrow spreads around the means indicate good confidence in these estimates, and the model seems to have performed well in capturing the underlying patterns in the data."
      ],
      "metadata": {
        "id": "QnhyY2LAZ-xy"
      }
    }
  ]
}