{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eGA6rB8XG_lc"
      },
      "source": [
        "### Phúc Lâm "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MJ1taZkHG9H2"
      },
      "source": [
        "# Lab 06 - Bài Tập"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pph-tf7hG9H4"
      },
      "source": [
        "### Bài 1: Tạo quần thể mô phỏng\n",
        "\n",
        "Bạn hãy tạo một mẫu gồm 10000 phần tử mô phỏng chiều cao của nam thanh niên có giá trị từ 120cm - 200cm. Bạn hãy lưu kết quả vào biến **POP**.\n",
        "\n",
        "Tính:\n",
        "a. Chiều cao trung bình (kỳ vọng) của quần thể và độ lệch chuẩn về chiều cao của quần thể.\n",
        "\n",
        "b. Tính tỷ lệ người cao trong quần thể, biết rằng thanh niên có chiều cao từ 180 trở lên được gọi là cao.\n",
        "\n",
        "c. Vẽ histogram về chiều cao của quần thể. Theo bạn quần thể có phân phối chuẩn hay không?\n",
        "\n",
        "d. Thử vẽ histogram và đồ thị hàm mật độ của phân phối chuẩn sử dụng tham số loc và scale bằng với kỳ vọng và độ lệch chuẩn của quần thể."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CkmmaxXPG9H6"
      },
      "outputs": [],
      "source": [
        "# Bạn hãy tạo một mẫu gồm 10000 phần tử mô phỏng chiều cao của nam thanh niên có giá trị từ 120cm - 200cm. Bạn hãy lưu kết quả vào biến POP.\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import scipy.stats as stats\n",
        "POP = np.random.uniform(120, 200, 10000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GW3_03wWHuHM",
        "outputId": "65f6083f-f457-4652-fd47-7178084d70ad"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Chiều cao trung bình của quần thể:  159.71671132960716\n",
            "Độ lệch chuẩn về chiều cao của quần thể:  23.167288275187346\n"
          ]
        }
      ],
      "source": [
        "# Tính: a. Chiều cao trung bình (kỳ vọng) của quần thể và độ lệch chuẩn về chiều cao của quần thể.\n",
        "print(\"Chiều cao trung bình của quần thể: \", np.mean(POP))\n",
        "print(\"Độ lệch chuẩn về chiều cao của quần thể: \", np.std(POP))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "di2cHaRNH7pH",
        "outputId": "edb005be-b7eb-495b-ab3d-560add0b751f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Tỷ lệ người cao trong quần thể:  24.83 %\n"
          ]
        }
      ],
      "source": [
        "# b. Tính tỷ lệ người cao trong quần thể, biết rằng thanh niên có chiều cao từ 180 trở lên được gọi là cao.\n",
        "CountOfTallMan =  np.sum(POP >= 180)\n",
        "ratioOfTallMan = CountOfTallMan / len(POP) * 100\n",
        "print(\"Tỷ lệ người cao trong quần thể: \", ratioOfTallMan,\"%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "qzqfZpCSIG75",
        "outputId": "d75c0852-d30b-4fbf-bf3d-7a6a511002a3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# c. Vẽ histogram về chiều cao của quần thể. Theo bạn quần thể có phân phối chuẩn hay không?\n",
        "plt.figure(figsize=(8,5))\n",
        "plt.hist(POP, bins=30, color='pink', edgecolor='black', alpha=0.7)\n",
        "plt.title('Histogram về chiều cao của quần thể')\n",
        "plt.xlabel('Chiều cao (cm)')\n",
        "plt.ylabel('Số lượng')\n",
        "plt.grid(axis='y', alpha=0.75)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 566
        },
        "id": "9gzaTQb6LAFZ",
        "outputId": "5fa366d5-0f2f-44d8-9dd9-e81df05e1b58"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# d. Thử vẽ histogram và đồ thị hàm mật độ của phân phối chuẩn sử dụng tham số loc và scale bằng với kỳ vọng và độ lệch chuẩn của quần thể.\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.histplot(POP, bins=30, kde=False, color='skyblue', edgecolor='black', stat='density', label='Histogram')\n",
        "\n",
        "std_dev_height = np.std(POP)\n",
        "mean_height = np.mean(POP)\n",
        "\n",
        "# Tính toán hàm mật độ của phân phối chuẩn\n",
        "x = np.linspace(120, 200, 1000)\n",
        "pdf = (1 / (std_dev_height * np.sqrt(2 * np.pi))) * np.exp(-0.5 * ((x - mean_height) / std_dev_height) ** 2)\n",
        "\n",
        "# Vẽ đồ thị hàm mật độ\n",
        "plt.plot(x, pdf, color='red', label='Hàm mật độ phân phối chuẩn')\n",
        "plt.title('Histogram và hàm mật độ của phân phối chuẩn')\n",
        "plt.xlabel('Chiều cao (cm)')\n",
        "plt.ylabel('Mật độ')\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fdxEVEuIG9H6"
      },
      "source": [
        "### Bài 2: Lấy mẫu cỡ 20\n",
        "\n",
        "Bạn hãy lấy một mẫu kích thước 20 phần từ. Tính:\n",
        "\n",
        "a. Trung bình mẫu, và độ lệch chuẩn của mẫu\n",
        "\n",
        "b. Tỷ lệ người cao của mẫu\n",
        "\n",
        "c. Vẽ histogram của mẫu\n",
        "\n",
        "d. Thử vẽ đồ thị hàm mật độ của phân phối chuẩn với tham số loc và scale bằng với giá trị trung bình và độ lệch chuẩn và chiều cao của mẫu. Bạn có nhận xét gì không?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZbheXc_RID-N",
        "outputId": "3645212c-308a-4903-b9a4-0173f5bec823"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[136.06594212 125.06760201 136.50843555 147.72454818 196.68755572\n",
            " 180.67029231 159.07077778 146.01801128 179.30666303 127.95743009\n",
            " 132.77918429 145.95737564 181.37680528 167.12786873 181.93869515\n",
            " 173.45182305 155.7931426  153.34253979 177.06912476 131.77981381]\n"
          ]
        }
      ],
      "source": [
        "sample_size = 20\n",
        "sample = np.random.choice(POP, size = sample_size, replace = False)\n",
        "print(sample)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aQ3z37STG9H7",
        "outputId": "8a7dbfcc-8ad5-4fcd-b179-cd88923ed08e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Trung bình mẫu:  156.7846815591258\n",
            "Độ lệch chuẩn của mẫu:  21.1138707977766\n"
          ]
        }
      ],
      "source": [
        "# a. Trung bình mẫu, và độ lệch chuẩn của mẫu\n",
        "SampleMean = np.mean(sample)\n",
        "SampleStd = np.std(sample)\n",
        "\n",
        "print(\"Trung bình mẫu: \", SampleMean)\n",
        "print(\"Độ lệch chuẩn của mẫu: \", SampleStd)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TAQf8hzcMtvr",
        "outputId": "0144c848-e772-494e-f167-11188d7db2cc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Tỷ lệ người cao của mẫu:  20.0 %\n"
          ]
        }
      ],
      "source": [
        "# b. Tỷ lệ người cao của mẫu\n",
        "SampleCountTallMan = np.sum(sample >= 180)\n",
        "SampleRatioTallMan = SampleCountTallMan / len(sample) * 100\n",
        "print(\"Tỷ lệ người cao của mẫu: \", SampleRatioTallMan,\"%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "sIlqDn12NQX5",
        "outputId": "c3d8b852-1006-420e-d35c-9cd90aa1ff18"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# c. Vẽ histogram của mẫu\n",
        "plt.figure(figsize=(8,5))\n",
        "plt.hist(sample, bins=10, color='skyblue', edgecolor='black', alpha=1)\n",
        "plt.title('Histogram của mẫu')\n",
        "plt.xlabel('Chiều cao (cm)')\n",
        "plt.ylabel('Số lượng')\n",
        "plt.grid(axis='y', alpha=0.75)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "0br5UdTlN9Pe",
        "outputId": "c60c8379-92f2-4aa2-b432-adb9993d80c4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# d. Thử vẽ đồ thị hàm mật độ của phân phối chuẩn với tham số loc và scale\n",
        "plt.figure(figsize=(8,5))\n",
        "sns.histplot(sample, bins=10, color='skyblue',stat='density',label='Histogram')\n",
        "\n",
        "\n",
        "# Tính toán hàm mật độ của phân phối chuẩn\n",
        "x = np.linspace(min(sample) - 1, max(sample) + 1, 1000)\n",
        "pdf = (1 / (SampleStd * np.sqrt(2 * np.pi))) * np.exp(-0.5 * ((x - SampleMean) / SampleStd) ** 2)\n",
        "# Vẽ đồ thị hàm mật độ\n",
        "plt.plot(x, pdf, color='red', label='Hàm mật độ phân phối chuẩn')\n",
        "plt.title('Histogram và hàm mật độ của phân phối chuẩn cho mẫu')\n",
        "plt.xlabel('Chiều cao (cm)')\n",
        "plt.ylabel('Mật độ')\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8TXgJM_DG9H7"
      },
      "source": [
        "### Bài 3: Lấy mẫu cỡ 100\n",
        "\n",
        "Lặp lại thí nghiệm bài 2 với cỡ mẫu là 100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EwaRcy5UG9H7"
      },
      "outputs": [],
      "source": [
        "# Lấy mẫu kích thước 100\n",
        "sample_size = 100\n",
        "sample = np.random.choice(POP, size=sample_size, replace=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BhwB3vhNRORx"
      },
      "outputs": [],
      "source": [
        "# Tính toán trung bình mẫu và độ lệch chuẩn mẫu\n",
        "SampleMean = np.mean(sample)\n",
        "SampleStd = np.std(sample)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "hu-gLI-HRUqv",
        "outputId": "3b918808-ef7a-4d07-99ce-c79e8cb44dfa"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Vẽ histogram của mẫu\n",
        "plt.figure(figsize=(8, 5))\n",
        "sns.histplot(sample, bins=50, color='skyblue', stat='density', edgecolor='black')\n",
        "\n",
        "# Tính toán hàm mật độ của phân phối chuẩn\n",
        "x = np.linspace(min(sample) - 1, max(sample) + 1, 1000)\n",
        "pdf = (1 / (SampleStd * np.sqrt(2 * np.pi))) * np.exp(-0.5 * ((x - SampleMean) / SampleStd) ** 2)\n",
        "\n",
        "# Vẽ đồ thị hàm mật độ\n",
        "plt.plot(x, pdf, color='red', label='Hàm mật độ phân phối chuẩn')\n",
        "plt.title('Histogram và hàm mật độ của phân phối chuẩn cho mẫu')\n",
        "plt.xlabel('Chiều cao (cm)')\n",
        "plt.ylabel('Mật độ')\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kjcKcnBaG9H7"
      },
      "source": [
        "### Bài 4: Khảo sát phân phối của trung bình mẫu\n",
        "\n",
        "a. Thực hiện bài 2 100 lần, mỗi lần bạn tính được trung bình mẫu. Vẽ đồ thị histogram cho 100 trung bình mẫu bạn tính được.\n",
        "\n",
        "b. Thực hiện bài 3 100 lần, mỗi lần bạn tính được trung bình mẫu. Vẽ đồ thị histogram cho 100 trung bình mẫu bạn tính được.\n",
        "\n",
        "c. Vẽ histogram của hai câu a, b trong cùng một hình để so sánh. Theo bạn hình dạng của histogram thay đổi nói lên điều gì?\n",
        "\n",
        "Gợi ý: tạo một danh sách rỗng để chứa các trung bình mẫu tính được, sử dụng hàm append() để thêm giá trị trung bình vào danh sách sau mỗi lần tính."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "-Kz7DahcG9H7",
        "outputId": "f5b6b997-937a-4c15-ae45-8cf7ba49b1d9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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60vdmlChRwuHTCXJz/ZxmBo8///xT0l/73PX7VokSJXK9dNv16tatm6WtXr16SklJ0enTp+Xv7+9wjZl1ZtaYW9/rx5ObzPNe/87X11dVq1bN8keYr6+v3ToLa//LyaFDhxQQEKBy5crl2CdzLuvXr2/X7u7urlq1ajn8R4EknTx5Uj169JCvr68++eQTubq62u67mX3f+N9pVjfzxy6QiSO8QCHq0KGDDh06pEWLFqlJkyZ69913dccdd+jdd991al3ZhauHH35Y77zzjoYNG6b/+7//05o1a2xHj7O7oP3fX9Rya5OU5UN2+e36o4Ivv/yyIiMj1aFDBy1evFhff/21YmNj1bhx45u6OH9OKleurJMnT2YZ34kTJyTJdq5m5cqV7dqv71uuXLlsj4A56u9HFP8up8CQ01FUZ81fXuSlxlsZT07LOrJOR/e/vM7P9cqXL6/09HRduHDBof4F4fz587rvvvt07tw5rV69Osv5yTez72f+8XD9H89AXhB4gUJWrlw5hYeHKzo6WkePHlWzZs3srjSQ04teYGCgjh8/nuXFbN++fbb7M/+1Wq06fPiwXb/sPl2fkz///FNxcXF6/vnnNXnyZPXq1UtdunQpsG+Gul7t2rVltVq1Z8+eXPuVLVtW586ds2u7evVqlhfTTz75RPfcc4/t6127du2qzp07Z1n2VjVv3lwpKSlZPpWe+SGv5s2bS7r27WEVKlTI9ktMtmzZYuuXk5s90pXd4yUpT0fv/i5zn7t+30pPT89ypYHcZHeKz4EDB+Tt7Z3lqOrtyNH9L/OI8/Xtjs5PgwYNJCnLc7927do6fvy4zp49m+OymXO5f/9+u/arV6/q8OHDtvtzc/nyZT3wwAM6cOCAvvzyy2zfobmZff/w4cPy8/Mzxb4A5yHwAoXo+rfyS5UqpTp16ti9vVeyZElJWV/0unfvroyMDM2bN8+u/bXXXpPFYtF9990nSQoJCZEkvfnmm3b93njjDYfrzDxqdf2Rrzlz5ji8jlsRGhoqFxcXTZkyJcsR2L/XVLt27SyXr8r8Vq6/c3V1zTKWFStW6NixY/la94MPPig3Nze7x94wDC1YsEBVqlRRu3btbO29e/fWl19+qaNHj9ra4uLidODAAfXp0yfX7WRedSGvgb127dr64Ycf7L6i9/oa8qJVq1YqX7683nnnHaWnp9vaP/74Y4dOE8gUHx9vdy7r0aNH9dlnn6lr1645HkG9nTi6/9WuXVuS7PbpjIwMLVy40KHtBAcHS1KWMNm7d28ZhqHJkydnWSazri5dusjd3V2vv/66Xa3vvfeezp8/n+tVXjLr7Nu3r+Lj47VixQpbLdnJ676/ffv2XNcHOIJzeIFC1KhRI3Xq1EktW7ZUuXLltG3bNn3yySeKiIiw9cn88M6IESMUEhIiV1dXPfLII3rggQd0zz336IUXXtCRI0cUFBSkNWvW6LPPPtOoUaNsL5YtW7ZU7969NWfOHJ05c8Z2WbLMr/h05Oigj4+P7fJLaWlpqlKlitasWZPlyFFBqVOnjl544QVNnTpVd999tx566CF5eHho69atCggIsH3V6OOPP65hw4apd+/e6tKli3bt2qWvv/46y1uf999/v6ZMmaLw8HC1a9dOP/30kz7++GOHj1jv3r1bn3/+uaRrRzPPnz+vadOmSZKCgoL0wAMPSJKqVq2qUaNG6ZVXXlFaWppat26tmJgYbdy4UR9//LFdeBs3bpxWrFihe+65RyNHjtTFixf1yiuvqGnTpgoPD8+1Hi8vLzVq1EjLli1TvXr1VK5cOTVp0uSGl/B6/PHH9cknn6hbt256+OGHdejQIS1evNi27+SVu7u7Jk2apOHDh+vee+/Vww8/rCNHjuj9999X7dq1HT4S3aRJE4WEhNhdlkxStgHtduTo/te4cWPdeeedioqK0tmzZ1WuXDktXbrU7o+J3NSqVUtNmjTRN998o8cee8zWfs8992jgwIF6/fXXdfDgQXXr1k1Wq1UbN27UPffco4iICPn5+WnChAmaMGGCunXrpp49e2r//v1688031bp1aw0YMCDXbT/zzDP6/PPP9cADD+js2bNavHix3f1/Xz4v+/6pU6e0e/duPf300w49BkCOCv26EMBt6ma+ae36y0BNmzbNaNOmjVGmTBnDy8vLaNCggfHSSy/Zff1wenq6MXz4cKNChQqGxWKxu0TZhQsXjNGjRxsBAQGGm5ubUbduXeOVV16xu9SSYVy7DNbTTz9tlCtXzihVqpQRGhpq7N+/35Bkd5mwzEuKnT59Ost4/vjjD6NXr15GmTJlDF9fX6NPnz7G8ePHc7y02fXrCAsLM0qWLOnQ45STRYsWGS1atDA8PDyMsmXLGh07djRiY2Nt92dkZBjPPfec4efnZ3h7exshISHGr7/+mu1lyZ555hmjcuXKhpeXl3HXXXcZ8fHxWS7HlZPcLkl3/WW+MjIyjJdfftkIDAw03N3djcaNGxuLFy/Odr0///yz0bVrV8Pb29soU6aM0b9/f+PkyZMOPTabNm0yWrZsabi7u9vNSU6Pe6ZZs2YZVapUMTw8PIy77rrL2LZtW46XJVuxYoXdsocPHzYkGf/5z3/s2l9//XUjMDDQ8PDwMNq0aWN8//33RsuWLY1u3brdcBz63zetLV682Khbt67h4eFhtGjRIssluHK6LFl2l/m7fjw5PXcdvdxXXvfl6+vKy/536NAho3PnzoaHh4dRqVIlY9y4cbZvIbtRnYZhGLNnzzZKlSqV5bKC6enpxiuvvGI0aNDAcHd3NypUqGDcd999xvbt2+36vfnmm0bDhg0Nd3d3o1KlSsaTTz5p/PnnnzfcbseOHXO9ZOP1HN3333rrLb5aGPnCYhhF6NMHAArMjz/+qBYtWmjx4sW2b94CCoLValWFChX00EMP6Z133nF2OcXK+fPnVatWLc2cOdPukoC3qxYtWqhTp052X6wD3AzO4QVMKLtruM6ZM0cuLi43/IYzIC8uX76c5fzUDz/8UGfPns3TVwsjf/j6+mrs2LF65ZVX8vUKJM6wevVqHTx4UFFRUc4uBSbAEV7AhCZPnqzt27frnnvuUYkSJbRq1SqtWrVKTzzxhN5++21nlwcTWbdunUaPHq0+ffqofPny2rFjh9577z01bNhQ27dvt/viCgBwFgIvYEKxsbGaPHmy9uzZo4sXL6p69eoaOHCgXnjhBbtvxAJu1ZEjRzRixAht2bLF9kGr7t27a8aMGapYsaKzywMASQReAAAAmBzn8AIAAMDUCLwAAAAwNU7my4bVatXx48dVunTpm/4KTwAAABQcwzB04cIFBQQEyMUl92O4BN5sHD9+XNWqVXN2GQAAALiBo0ePqmrVqrn2IfBmo3Tp0pKuPYA+Pj5OrqZwpaWlac2aNeratavc3NycXQ4KEHNdPDDPxQdzXTwwz39JTk5WtWrVbLktNwTebGSexuDj41MsA6+3t7d8fHyK/RPJ7Jjr4oF5Lj6Y6+KBec7KkdNP+dAaAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMr4ewCAACFIyEhQUlJSc4uo8D4+fmpevXqzi4DQBFE4AWAYuCPP/5Q4yZNlJqS4uxSCoyXt7f27d1L6AWQBYEXAIqBM2fOKDUlRU+9ulABtes5u5x8d/zQAb357BNKSkoi8ALIgsALAMVIQO16qtm4ubPLAIBCxYfWAAAAYGoEXgAAAJgagRcAAACmRuAFAACAqRF4AQAAYGoEXgAAAJgagRcAAACmRuAFAACAqTk98M6fP181atSQp6en2rZtqy1btuTY95dfflHv3r1Vo0YNWSwWzZkz55bXCQAAAHNzauBdtmyZIiMjNXHiRO3YsUNBQUEKCQnRqVOnsu2fkpKiWrVqacaMGfL398+XdQIAAMDcnBp4Z8+eraFDhyo8PFyNGjXSggUL5O3trUWLFmXbv3Xr1nrllVf0yCOPyMPDI1/WCQAAAHMr4awNX716Vdu3b1dUVJStzcXFRZ07d1Z8fHyhrvPKlSu6cuWK7XZycrIkKS0tTWlpaTdVy+0qc7zFbdzFEXNdPGTOr9VqlZeXl1xkSBnpTq4q/7nIkJeXl6xWa7Hdp3lOFw/M81/y8hg4LfAmJSUpIyNDlSpVsmuvVKmS9u3bV6jrnD59uiZPnpylfc2aNfL29r6pWm53sbGxzi4BhYS5Lh5OnDih6OhoSeel/d85u5x818giRUdH69ixYzp27Jizy3EqntPFA/N87VRXRzkt8BYlUVFRioyMtN1OTk5WtWrV1LVrV/n4+DixssKXlpam2NhYdenSRW5ubs4uBwWIuS4eMue5cuXK6tSpkyYsWanABk2dXVa++33fT5r6aHdt2LBBQUFBzi7HKXhOFw/M818y35F3hNMCr5+fn1xdXZWYmGjXnpiYmOMH0gpqnR4eHtmeE+zm5lZsd6biPPbihrkuHlxcXJSamiqrLJKr+Y51WGVRamqqXFxciv3+zHO6eGCelafxO+1Da+7u7mrZsqXi4uJsbVarVXFxcQoODi4y6wQAAMDtzal/5kdGRiosLEytWrVSmzZtNGfOHF26dEnh4eGSpEGDBqlKlSqaPn26pGsfStuzZ4/t/8eOHdOPP/6oUqVKqU6dOg6tEwAAAMWLUwNv3759dfr0ab344os6efKkmjdvrtWrV9s+dJaQkCAXl78OQh8/flwtWrSw3X711Vf16quvqmPHjlq3bp1D6wQAAEDx4vQTuSIiIhQREZHtfZkhNlONGjVkGMYtrRMAAADFi9O/WhgAAAAoSAReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpOT3wzp8/XzVq1JCnp6fatm2rLVu25Np/xYoVatCggTw9PdW0aVOtXLnS7v6LFy8qIiJCVatWlZeXlxo1aqQFCxYU5BAAAABQhDk18C5btkyRkZGaOHGiduzYoaCgIIWEhOjUqVPZ9t+0aZP69eunIUOGaOfOnQoNDVVoaKh+/vlnW5/IyEitXr1aixcv1t69ezVq1ChFRETo888/L6xhAQAAoAhxauCdPXu2hg4dqvDwcNuRWG9vby1atCjb/nPnzlW3bt00ZswYNWzYUFOnTtUdd9yhefPm2fps2rRJYWFh6tSpk2rUqKEnnnhCQUFBNzxyDAAAAHMq4awNX716Vdu3b1dUVJStzcXFRZ07d1Z8fHy2y8THxysyMtKuLSQkRDExMbbb7dq10+eff67HHntMAQEBWrdunQ4cOKDXXnstx1quXLmiK1eu2G4nJydLktLS0pSWlnYzw7ttZY63uI27OGKui4fM+bVarfLy8pKLDCkj3clV5T8XGfLy8pLVai22+zTP6eKBef5LXh4DpwXepKQkZWRkqFKlSnbtlSpV0r59+7Jd5uTJk9n2P3nypO32G2+8oSeeeEJVq1ZViRIl5OLionfeeUcdOnTIsZbp06dr8uTJWdrXrFkjb2/vvAzLNGJjY51dAgoJc108nDhxQtHR0ZLOS/u/c3Y5+a6RRYqOjtaxY8d07NgxZ5fjVDyniwfmWUpJSXG4r9MCb0F544039MMPP+jzzz9XYGCgNmzYoKeffloBAQHq3LlztstERUXZHTlOTk5WtWrV1LVrV/n4+BRW6UVCWlqaYmNj1aVLF7m5uTm7HBQg5rp4yJznypUrq1OnTpqwZKUCGzR1dln57vd9P2nqo921YcMGBQUFObscp+A5XTwwz3/JfEfeEU4LvH5+fnJ1dVViYqJde2Jiovz9/bNdxt/fP9f+qampGjdunD799FP16NFDktSsWTP9+OOPevXVV3MMvB4eHvLw8MjS7ubmVmx3puI89uKGuS4eXFxclJqaKqsskqvpjnXIKotSU1Pl4uJS7PdnntPFA/OsPI3faR9ac3d3V8uWLRUXF2drs1qtiouLU3BwcLbLBAcH2/WXrh3Sz+yfec6ti4v9sFxdXWW1WvN5BAAAALgdOPXP/MjISIWFhalVq1Zq06aN5syZo0uXLik8PFySNGjQIFWpUkXTp0+XJI0cOVIdO3bUrFmz1KNHDy1dulTbtm3TwoULJUk+Pj7q2LGjxowZIy8vLwUGBmr9+vX68MMPNXv2bKeNEwAAAM7j1MDbt29fnT59Wi+++KJOnjyp5s2ba/Xq1bYPpiUkJNgdrW3Xrp2WLFmi8ePHa9y4capbt65iYmLUpEkTW5+lS5cqKipK/fv319mzZxUYGKiXXnpJw4YNK/TxAQAAwPmcfiJXRESEIiIisr1v3bp1Wdr69OmjPn365Lg+f39//ec//8mv8gAAAHCbc/pXCwMAAAAFicALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNRuKfAahiHDMG6pgPnz56tGjRry9PRU27ZttWXLllz7r1ixQg0aNJCnp6eaNm2qlStXZumzd+9e9ezZU76+vipZsqRat26thISEW6oTAAAAt6ebCrwffvihmjZtKi8vL3l5ealZs2b66KOP8ryeZcuWKTIyUhMnTtSOHTsUFBSkkJAQnTp1Ktv+mzZtUr9+/TRkyBDt3LlToaGhCg0N1c8//2zrc+jQIbVv314NGjTQunXrtHv3bk2YMEGenp43M1QAAADc5hwOvPPnz5ckzZ49W08++aS6d++u5cuXa/ny5erWrZuGDRum1157LU8bnz17toYOHarw8HA1atRICxYskLe3txYtWpRt/7lz56pbt24aM2aMGjZsqKlTp+qOO+7QvHnzbH1eeOEFde/eXTNnzlSLFi1Uu3Zt9ezZUxUrVsxTbQAAADCHEjfqcPDgQQ0ZMkR33XWXJOmNN97QW2+9pUGDBtn69OzZU40bN9akSZM0evRozZgxQ8OGDVOZMmVyXO/Vq1e1fft2RUVF2dpcXFzUuXNnxcfHZ7tMfHy8IiMj7dpCQkIUExMjSbJarfrqq680duxYhYSEaOfOnapZs6aioqIUGhqaYy1XrlzRlStXbLeTk5MlSWlpaUpLS8txOTPKHG9xG3dxxFwXD5nza7Va5eXlJRcZUka6k6vKfy4y5OXlJavVWmz3aZ7TxQPz/Je8PAY3DLxLly6Vr6+vpk+fLkk6ceKE2rVrl6Vfu3btdOLECUnSuHHjdMcdd6hr1645rjcpKUkZGRmqVKmSXXulSpW0b9++bJc5efJktv1PnjwpSTp16pQuXryoGTNmaNq0afr3v/+t1atX66GHHtLatWvVsWPHbNc7ffp0TZ48OUv7mjVr5O3tneMYzCw2NtbZJaCQMNfFw4kTJxQdHS3pvLT/O2eXk+8aWaTo6GgdO3ZMx44dc3Y5TsVzunhgnqWUlBSH+94w8EZGRioqKkohISH6+uuvVadOHS1fvlzjxo2z67ds2TLVrVtXkvT7778rICAgj2XfOqvVKkl68MEHNXr0aElS8+bNtWnTJi1YsCDHwBsVFWV35Dg5OVnVqlVT165d5ePjU/CFFyFpaWmKjY1Vly5d5Obm5uxyUICY6+Ihc54rV66sTp06acKSlQps0NTZZeW73/f9pKmPdteGDRsUFBTk7HKcgud08cA8/yXzHXlH3DDwlixZUq+//rrtNIPJkyerb9++2rBhg+00h++//15xcXFavny5JKlatWo33LCfn59cXV2VmJho156YmCh/f/9sl/H398+1v5+fn0qUKKFGjRrZ9WnYsKG++y7nIxoeHh7y8PDI0u7m5lZsd6biPPbihrkuHlxcXJSamiqrLJLrDX/133assig1NVUuLi7Ffn/mOV08MM/K0/gd/tBacHCwJKl3797avHmz/Pz8FBMTo5iYGPn5+WnLli3q1auXwxt2d3dXy5YtFRcXZ2uzWq2Ki4uzbSu7Gv7eX7p2SD+zv7u7u1q3bq39+/fb9Tlw4IACAwMdrg0AAADm4fCf+Znnz1osFrVs2VKLFy++5Y1HRkYqLCxMrVq1Ups2bTRnzhxdunRJ4eHhkqRBgwapSpUqtvOHR44cqY4dO2rWrFnq0aOHli5dqm3btmnhwoW2dY4ZM0Z9+/ZVhw4ddM8992j16tX64osvtG7duluuFwAAALcfhwLv+fPnVa9ePX333XeqUaNGrn3zcs5r3759dfr0ab344os6efKkmjdvrtWrV9s+mJaQkCAXl78OQrdr105LlizR+PHjNW7cONWtW1cxMTFq0qSJrU+vXr20YMECTZ8+XSNGjFD9+vX13//+V+3bt3e4LgAAAJiHQ4G3bNmykqQWLVrcsG9GRkaeCoiIiFBERES292V3VLZPnz7q06dPrut87LHH9Nhjj+WpDgAAAJiTQ4H322+/VY8ePfTOO+8oLS1Nzz//vAYPHmw7dzY+Pl4ffPCB7dQDAAAAoKhwKPB26tRJLi4uuvPOOzV06FDNnj1b/fr1s93fs2dPNW3aVAsXLlRYWFiBFQsAAADklcNXaZg9e7b8/PwUHx+vVq1aZbm/VatW2rJlS74WBwAAANwqhwPv0KFD5ePjo2rVqumdd97Jcv+7777r0PV3AQAAgMKU56uPv/baa+rdu7dWrVqltm3bSpK2bNmigwcP6r///W++FwgAhSUhIUFJSUnOLiNfZX4D5fXXJweA4iTPgbd79+46ePCg3nrrLe3du1eS9MADD2jYsGEc4QVw20pISFCDhg2VmofvZr8deHl5KTo6WkOHDpUkXb1y1ckVAUDhu6nvl6xatapeeuml/K4FAJwmKSlJqSkpeurVhQqoXc/Z5eQbFxmSziv0qWcVPWuq0tPTnV0SABQ6832hOgDcgoDa9VSzcXNnl5F/MtKl/d/JL6C6sysBAKdx+ENrAAAAwO2IwAsAAABTI/ACAADA1PIceO+9916dO3cuS3tycrLuvffe/KgJAAAAyDd5Drzr1q3T1atZL2tz+fJlbdy4MV+KAgAAAPKLw1dp2L17t+3/e/bs0cmTJ223MzIytHr1alWpUiV/qwMAAABukcOBt3nz5rJYLLJYLNmeuuDl5aU33ngjX4sDAAAAbpXDgffw4cMyDEO1atXSli1bVKFCBdt97u7uqlixolxdXQukSAAAAOBmORx4AwMDJf31vewAAADA7eCmv2ltz549SkhIyPIBtp49e95yUQAAAEB+cTjwXr16Ve7u7vrtt9/Uq1cv/fTTT7JYLDIMQ5JksVgkXfsAGwAAAFBUOBR4N23apMjISP3www8aOXKkatasqbi4ONWsWVNbtmzRmTNn9Mwzz+jVV18t6HoBOElCQoKSkpKcXUaB2bt3r7NLQD4w+zz6+fmpevXqzi4DuO3cMPB+9NFHev3117Vs2TJJUnx8vL799lv5+fnJxcVFLi4uat++vaZPn64RI0Zo586dio6OVs+ePVWyZMkCHwCAgpeQkKAGDRsqNSXF2aUUuKtXsl5nHEXfudOJslgsGjBggLNLKVBe3t7at3cvoRfIoxsG3jJlyujChQs6deqUatWqpYyMDJUuXVrStb80jx8/rvr16yswMFD79++XJI0dO1bt2rUj8AImkZSUpNSUFD316kIF1K7n7HIKxK71sVox5yWlp6c7uxTchJTk8zIMQ+FTX1ftJs2cXU6BOH7ogN589gklJSUReIE8umHgfeCBB9S0aVONGjVKMTExatKkiXbt2qWaNWuqbdu2mjlzptzd3bVw4ULVqlVLknT06NECLxxA4QuoXU81Gzd3dhkF4vihA84uAfmgcs06pt1HAdw8h87hrVGjhmJiYiRJ48eP16VLlyRJU6ZM0f3336+7775b5cuXt532AAAAABQVeb4sWUhIiO3/derU0b59+3T27FmVLVvWdqUGAAAAoKi46evw/l25cuXyYzUAAABAvnM48D722GMO9Vu0aNFNFwMAAADkN4cD7/vvv6/AwEC1aNHC9mUTAAAAQFHncOB98sknFR0drcOHDys8PFwDBgzgVAYAAAAUeS6Odpw/f75OnDihsWPH6osvvlC1atX08MMP6+uvv+aILwAAAIoshwOvJHl4eKhfv36KjY3Vnj171LhxYz311FOqUaOGLl68WFA1AgAAADctT4HXbkEXF1ksFhmGoYyMjPysCQAAAMg3eQq8V65cUXR0tLp06aJ69erpp59+0rx585SQkKBSpUoVVI0AAADATXP4Q2tPPfWUli5dqmrVqumxxx5TdHS0/Pz8CrI2AAAA4JY5HHgXLFig6tWrq1atWlq/fr3Wr1+fbb//+7//y7fiAAAAgFvlcOAdNGgQXx0MAACA206evngCAAAAuN3c9FUaAAAAgNsBgRcAAACmRuAFAACAqRF4AQAAYGoEXgAAAJgagRcAAACmRuAFAACAqRF4AQAAYGoEXgAAAJgagRcAAACmRuAFAACAqRF4AQAAYGoEXgAAAJhaCWcXAJhFQkKCkpKSnF2Gw6xWqyRp165dcnHJ/W/fvXv3FkZJAAAUCAIvkA8SEhLUoGFDpaakOLsUh3l5eSk6OlodOnRQamqqQ8tcvXK1gKsCACD/EXiBfJCUlKTUlBQ99epCBdSu5+xyHOIiQ9J5TViyUlZZcu27a32sVsx5Senp6YVTHAAA+YjAC+SjgNr1VLNxc2eX4ZiMdGn/dwps0FRyzf1XwfFDBwqpKAAA8h8fWgMAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpFYnAO3/+fNWoUUOenp5q27attmzZkmv/FStWqEGDBvL09FTTpk21cuXKHPsOGzZMFotFc+bMyeeqAQAAcDtweuBdtmyZIiMjNXHiRO3YsUNBQUEKCQnRqVOnsu2/adMm9evXT0OGDNHOnTsVGhqq0NBQ/fzzz1n6fvrpp/rhhx8UEBBQ0MMAAABAEeX0wDt79mwNHTpU4eHhatSokRYsWCBvb28tWrQo2/5z585Vt27dNGbMGDVs2FBTp07VHXfcoXnz5tn1O3bsmIYPH66PP/5Ybm5uhTEUAAAAFEFO/eKJq1evavv27YqKirK1ubi4qHPnzoqPj892mfj4eEVGRtq1hYSEKCYmxnbbarVq4MCBGjNmjBo3bnzDOq5cuaIrV67YbicnJ0uS0tLSlJaWlpch3fYyx1vcxn2rrFarvLy8rn17WcZt8m1kmXU6UK+rxXL7jS+PTDvG/43FtOP7H7OPT7r27YheXl6yWq3Z/o7m93fxwDz/JS+PgcUwDKMAa8nV8ePHVaVKFW3atEnBwcG29rFjx2r9+vXavHlzlmXc3d31wQcfqF+/fra2N998U5MnT1ZiYqIkafr06Vq7dq2+/vprWSwW1ahRQ6NGjdKoUaOyrWPSpEmaPHlylvYlS5bI29v7FkcJAACA/JaSkqJHH31U58+fl4+PT659TffVwtu3b9fcuXO1Y8cOWSwWh5aJioqyO2qcnJysatWqqWvXrjd8AM0mLS1NsbGx6tKlC6eC5MGuXbvUoUMHTViy8tpX9d4OMtJV8tcfdKnOnTf8auEfVn6qd8eP0DPvfKKGLdsWUoGFy7Rj/N88r/stSW+/MNx84/sf087f3/y+7ydNfbS7NmzYoKCgoCz38/u7eGCe/5L5jrwjnBp4/fz85OrqajsymykxMVH+/v7ZLuPv759r/40bN+rUqVOqXr267f6MjAw988wzmjNnjo4cOZJlnR4eHvLw8MjS7ubmVmx3puI89pvh4uKi1NRUWWW5YXgsclxL3LDmDMO4fcfnILOPkfHd/qyyKDU1Vfv375eLS9aP4FitVknSnj17sr3/duDn52f3+o2c8TqtPI3fqb8V3N3d1bJlS8XFxSk0NFTStSdsXFycIiIisl0mODhYcXFxdqcnxMbG2k6JGDhwoDp37my3TEhIiAYOHKjw8PACGQcAAAXt3OlEWSwWDRgwINv7vby8FB0drQ4dOig1NbWQq8sfXt7e2rd3L6EX+c7pfwZHRkYqLCxMrVq1Ups2bTRnzhxdunTJFk4HDRqkKlWqaPr06ZKkkSNHqmPHjpo1a5Z69OihpUuXatu2bVq4cKEkqXz58ipfvrzdNtzc3OTv76/69esX7uAAAMgnKcnnZRiGwqe+rtpNmmW530WGpPOasGTltSPdt5njhw7ozWefUFJSEoEX+c7pgbdv3746ffq0XnzxRZ08eVLNmzfX6tWrValSJUlSQkKC3Vsz7dq105IlSzR+/HiNGzdOdevWVUxMjJo0aeKsIQAAUGgq16yjmo2bZ70jI13a/921zxGY9LQO4GYViWdEREREjqcwrFu3Lktbnz591KdPH4fXn915uwAAACgebs+z2gEAAAAHEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgaiWcXQCKj4SEBCUlJTm7jAKxd+9eZ5cAAAByQOBFoUhISFCDhg2VmpLi7FIK1NUrV51dAgAAuA6BF4UiKSlJqSkpeurVhQqoXc/Z5eS7XetjtWLOS0pPT3d2KQAA4DoEXhSqgNr1VLNxc2eXke+OHzrg7BIAAEAO+NAaAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMr4ewCcE1CQoKSkpKcXYasVqskadeuXXJxyb+/h/bu3Ztv6wIAmJeZXy/8/PxUvXp1Z5dRLBF4i4CEhAQ1aNhQqSkpzi5FXl5eio6OVocOHZSamprv67965Wq+rxMAcPs7dzpRFotFAwYMcHYpBcbL21v79u4l9DoBgbcISEpKUmpKip56daECatdzai0uMiSd14QlK2WVJd/Wu2t9rFbMeUnp6en5tk4AgHmkJJ+XYRgKn/q6ajdp5uxy8t3xQwf05rNPKCkpicDrBATeIiSgdj3VbNzcuUVkpEv7v1Ngg6aSa/7tHscPHci3dQEAzKtyzTrOfy2E6RSJD63Nnz9fNWrUkKenp9q2bastW7bk2n/FihVq0KCBPD091bRpU61cudJ2X1pamp577jk1bdpUJUuWVEBAgAYNGqTjx48X9DAAAABQBDk98C5btkyRkZGaOHGiduzYoaCgIIWEhOjUqVPZ9t+0aZP69eunIUOGaOfOnQoNDVVoaKh+/vlnSVJKSop27NihCRMmaMeOHfq///s/7d+/Xz179izMYQEAAKCIcPopDbNnz9bQoUMVHh4uSVqwYIG++uorLVq0SM8//3yW/nPnzlW3bt00ZswYSdLUqVMVGxurefPmacGCBfL19VVsbKzdMvPmzVObNm2UkJCQ7XkzV65c0ZUrV2y3k5OTJV07WpyWlpZvY82J1WqVl5fXtfNnM5x8jmvm9vO5DleLpeiMsQDcluPLw1zfluPLI9OO8X9jMe34/sfs45McGGMB/f4uLGafQxcZ8vLyktVqvaVskblsYeSToi4vj4HFMAyjAGvJ1dWrV+Xt7a1PPvlEoaGhtvawsDCdO3dOn332WZZlqlevrsjISI0aNcrWNnHiRMXExGjXrl3Zbuebb75R165dde7cOfn4+GS5f9KkSZo8eXKW9iVLlsjb2zvvAwMAAECBSklJ0aOPPqrz589nm+/+zqlHeJOSkpSRkaFKlSrZtVeqVEn79u3LdpmTJ09m2//kyZPZ9r98+bKee+459evXL8cHIyoqSpGRkbbbycnJqlatmrp27XrDBzA/7Nq1Sx06dNCEJSuvfVjMmTLSVfLXH3Spzp35+qG1H1Z+qnfHj9Az73yihi3b5tt6i4rbcnx5mOvbcnx5ZNox/m+e1/2WpLdfGG6+8f2Paefvb244xgL6/V1YzD6Hv+/7SVMf7a4NGzYoKCjopteTlpam2NhYdenSRW5ubvlY4e0n8x15R9x+z4g8SEtL08MPPyzDMPTWW2/l2M/Dw0MeHh5Z2t3c3AplZ3JxcVFqauq1y4AVlV9SriXytZYMwyh6Y8xHt/X4HJjr23p8DjL7GBnf7c/hMebz7+/CYvY5tMqi1NRUubi45Eu2KKyMUpTlZfxO3aP8/Pzk6uqqxMREu/bExET5+/tnu4y/v79D/TPD7u+//65vv/22UI7UAgAAoOhx6lUa3N3d1bJlS8XFxdnarFar4uLiFBwcnO0ywcHBdv0lKTY21q5/Ztg9ePCgvvnmG5UvX75gBgAAAIAiz+nvGURGRiosLEytWrVSmzZtNGfOHF26dMl21YZBgwapSpUqmj59uiRp5MiR6tixo2bNmqUePXpo6dKl2rZtmxYuXCjpWtj95z//qR07dujLL79URkaG7fzecuXKyd3d3TkDBQAAgFM4PfD27dtXp0+f1osvvqiTJ0+qefPmWr16te2DaQkJCXJx+etAdLt27bRkyRKNHz9e48aNU926dRUTE6MmTZpIko4dO6bPP/9cktS8eXO7ba1du1adOnUqlHEBAACgaHB64JWkiIgIRUREZHvfunXrsrT16dNHffr0ybZ/jRo15MQrrQEAAKCIcfo3rQEAAAAFicALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADC1Es4uAAAAoLjYu3fvLS1vtVolSbt27ZKLS9E7bunn56fq1as7u4wsCLwAAAAF7NzpRFksFg0YMOCW1uPl5aXo6Gh16NBBqamp+VRd/vHy9ta+vXuLXOgl8AIAABSwlOTzMgxD4VNfV+0mzW56PS4yJJ3XhCUrZZUl/wrMB8cPHdCbzz6hpKQkAi8AAEBxVblmHdVs3PzmV5CRLu3/ToENmkquxDhHFb2TPwAAAIB8ROAFAACAqRF4AQAAYGoEXgAAAJgagRcAAACmRuAFAACAqRF4AQAAYGoEXgAAAJgagRcAAACmRuAFAACAqRF4AQAAYGoEXgAAAJgagRcAAACmRuAFAACAqRF4AQAAYGoEXgAAAJgagRcAAACmRuAFAACAqRF4AQAAYGoEXgAAAJgagRcAAACmRuAFAACAqRF4AQAAYGoEXgAAAJgagRcAAACmRuAFAACAqRF4AQAAYGoEXgAAAJgagRcAAACmRuAFAACAqRF4AQAAYGoEXgAAAJgagRcAAACmRuAFAACAqRF4AQAAYGoEXgAAAJgagRcAAACmRuAFAACAqRF4AQAAYGpFIvDOnz9fNWrUkKenp9q2bastW7bk2n/FihVq0KCBPD091bRpU61cudLufsMw9OKLL6py5cry8vJS586ddfDgwYIcAgAAAIoopwfeZcuWKTIyUhMnTtSOHTsUFBSkkJAQnTp1Ktv+mzZtUr9+/TRkyBDt3LlToaGhCg0N1c8//2zrM3PmTL3++utasGCBNm/erJIlSyokJESXL18urGEBAACgiHB64J09e7aGDh2q8PBwNWrUSAsWLJC3t7cWLVqUbf+5c+eqW7duGjNmjBo2bKipU6fqjjvu0Lx58yRdO7o7Z84cjR8/Xg8++KCaNWumDz/8UMePH1dMTEwhjgwAAABFQQlnbvzq1avavn27oqKibG0uLi7q3Lmz4uPjs10mPj5ekZGRdm0hISG2MHv48GGdPHlSnTt3tt3v6+urtm3bKj4+Xo888kiWdV65ckVXrlyx3T5//rwk6ezZs0pLS7vp8TkqOTlZnp6eOrpnt66mXCzw7eXGRYbqWVJ0cEe8rLLk23pPHflVnp6eOrb/Z5WwWPNtvUXF7Ti+vMz17Ti+vDLrGDPn+VTCIVOOL5NZ5+/vbjTGgvr9XVjMPof5Nb6iPM+JR36Tp6enkpOTdebMmQLf3oULFyRdO9h5Q4YTHTt2zJBkbNq0ya59zJgxRps2bbJdxs3NzViyZIld2/z5842KFSsahmEY33//vSHJOH78uF2fPn36GA8//HC265w4caIhiR9++OGHH3744Yef2+zn6NGjN8ycTj3CW1RERUXZHTW2Wq06e/asypcvL4ulaP31VNCSk5NVrVo1HT16VD4+Ps4uBwWIuS4emOfig7kuHpjnvxiGoQsXLiggIOCGfZ0aeP38/OTq6qrExES79sTERPn7+2e7jL+/f679M/9NTExU5cqV7fo0b94823V6eHjIw8PDrq1MmTJ5GYrp+Pj4FPsnUnHBXBcPzHPxwVwXD8zzNb6+vg71c+qH1tzd3dWyZUvFxcXZ2qxWq+Li4hQcHJztMsHBwXb9JSk2NtbWv2bNmvL397frk5ycrM2bN+e4TgAAAJiX009piIyMVFhYmFq1aqU2bdpozpw5unTpksLDwyVJgwYNUpUqVTR9+nRJ0siRI9WxY0fNmjVLPXr00NKlS7Vt2zYtXLhQkmSxWDRq1ChNmzZNdevWVc2aNTVhwgQFBAQoNDTUWcMEAACAkzg98Pbt21enT5/Wiy++qJMnT6p58+ZavXq1KlWqJElKSEiQi8tfB6LbtWunJUuWaPz48Ro3bpzq1q2rmJgYNWnSxNZn7NixunTpkp544gmdO3dO7du31+rVq+Xp6Vno47vdeHh4aOLEiVlO8YD5MNfFA/NcfDDXxQPzfHMshuHItRwAAACA25PTv3gCAAAAKEgEXgAAAJgagRcAAACmRuAFAACAqRF4i4kNGzbogQceUEBAgCwWi2JiYuzuHzx4sCwWi91Pt27d7PqcPXtW/fv3l4+Pj8qUKaMhQ4bo4sWLhTgK3Eh+zHONGjWy9JkxY0YhjgKOuNFcS9LevXvVs2dP+fr6qmTJkmrdurUSEhJs91++fFlPP/20ypcvr1KlSql3795ZvtgHzpUf89ypU6csz+lhw4YV4ijgiBvN9fVzmPnzyiuv2PrwOp0zAm8xcenSJQUFBWn+/Pk59unWrZtOnDhh+4mOjra7v3///vrll18UGxurL7/8Uhs2bNATTzxR0KUjD/JjniVpypQpdn2GDx9ekGXjJtxorg8dOqT27durQYMGWrdunXbv3q0JEybYXZ5x9OjR+uKLL7RixQqtX79ex48f10MPPVRYQ4AD8mOeJWno0KF2z+mZM2cWRvnIgxvN9d/n78SJE1q0aJEsFot69+5t68PrdC4MFDuSjE8//dSuLSwszHjwwQdzXGbPnj2GJGPr1q22tlWrVhkWi8U4duxYAVWKW3Ez82wYhhEYGGi89tprBVYX8l92c923b19jwIABOS5z7tw5w83NzVixYoWtbe/evYYkIz4+vqBKxS24mXk2DMPo2LGjMXLkyIIrDPkuu7m+3oMPPmjce++9ttu8TueOI7ywWbdunSpWrKj69evrySef1JkzZ2z3xcfHq0yZMmrVqpWtrXPnznJxcdHmzZudUS5uUm7znGnGjBkqX768WrRooVdeeUXp6elOqBQ3y2q16quvvlK9evUUEhKiihUrqm3btnZvkW7fvl1paWnq3Lmzra1BgwaqXr264uPjnVA18sqRec708ccfy8/PT02aNFFUVJRSUlIKv2Dkm8TERH311VcaMmSIrY3X6dwReCHp2tvcH374oeLi4vTvf/9b69ev13333aeMjAxJ0smTJ1WxYkW7ZUqUKKFy5crp5MmTzigZN+FG8yxJI0aM0NKlS7V27Vr961//0ssvv6yxY8c6sWrk1alTp3Tx4kXNmDFD3bp105o1a9SrVy899NBDWr9+vaRrz2l3d3eVKVPGbtlKlSrxnL5NODLPkvToo49q8eLFWrt2raKiovTRRx9pwIABTqwct+qDDz5Q6dKl7U5B4nU6d07/amEUDY888ojt/02bNlWzZs1Uu3ZtrVu3Tv/4xz+cWBnykyPzHBkZaevTrFkzubu761//+pemT5/OV1neJqxWqyTpwQcf1OjRoyVJzZs316ZNm7RgwQJ17NjRmeUhnzg6z38/h7Np06aqXLmy/vGPf+jQoUOqXbt24ReOW7Zo0SL1798/y7nayBlHeJGtWrVqyc/PT7/++qskyd/fX6dOnbLrk56errNnz8rf398ZJSIfXD/P2Wnbtq3S09N15MiRwisMt8TPz08lSpRQo0aN7NobNmxo+/S+v7+/rl69qnPnztn1SUxM5Dl9m3BknrPTtm1bScr1eY+ia+PGjdq/f78ef/xxu3Zep3NH4EW2/vjjD505c0aVK1eWJAUHB+vcuXPavn27rc+3334rq9Vq++WJ28/185ydH3/8US4uLlneKkPR5e7urtatW2v//v127QcOHFBgYKAkqWXLlnJzc1NcXJzt/v379yshIUHBwcGFWi9ujiPznJ0ff/xRknJ93qPoeu+999SyZUsFBQXZtfM6nTtOaSgmLl68aPfX/OHDh/Xjjz+qXLlyKleunCZPnqzevXvL399fhw4d0tixY1WnTh2FhIRIunbEoFu3bho6dKgWLFigtLQ0RURE6JFHHlFAQICzhoXr3Oo8x8fHa/PmzbrnnntUunRpxcfHa/To0RowYIDKli3rrGEhG7nNdfXq1TVmzBj17dtXHTp00D333KPVq1friy++0Lp16yRJvr6+GjJkiCIjI1WuXDn5+Pho+PDhCg4O1p133umkUeF6tzrPhw4d0pIlS9S9e3eVL19eu3fv1ujRo9WhQwc1a9bMSaNCdm4015KUnJysFStWaNasWVmW53X6Bpx9mQgUjrVr1xqSsvyEhYUZKSkpRteuXY0KFSoYbm5uRmBgoDF06FDj5MmTdus4c+aM0a9fP6NUqVKGj4+PER4ebly4cMFJI0J2bnWet2/fbrRt29bw9fU1PD09jYYNGxovv/yycfnyZSeOCtnJba4zvffee0adOnUMT09PIygoyIiJibFbR2pqqvHUU08ZZcuWNby9vY1evXoZJ06cKOSRIDe3Os8JCQlGhw4djHLlyhkeHh5GnTp1jDFjxhjnz593wmiQG0fm+u233za8vLyMc+fOZbsOXqdzZjEMwyi0dA0AAAAUMs7hBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQDctClTpqhWrVpatWqVpk2bpqVLlzq7JADIgsALAEXQL7/8opkzZyo9Pd3ZpeTqiy++0Hvvvae33npLq1evVteuXZ1dEgBkQeAFACfp1KmTRo0ale19jRo10vbt2zV27Ng8rXPdunWyWCw6d+7crRfogC1btuill17S1q1b9eabb6pcuXL5st79+/fL399fFy5cyJf1ZWfPnj2qWrWqLl26VGDbAFA0EHgBmIbFYsn1Z9KkSc4u0WEWi0Uffvihdu7cqSVLlji7nBy99dZbaty4sb766isNGzYs345IR0VFafjw4SpdunS+rC87jRo10p133qnZs2cX2DYAFA0lnF0AAOSXEydO2P6/bNkyvfjii9q/f7+trVSpUrb/G4ahjIwMlShRdH8Nenh4aO3atc4uI1ePPPKIfH195erqqq+++ipf1pmQkKAvv/xSb7zxRr6sLzfh4eEaOnSooqKiivS+AODWcIQXgGn4+/vbfnx9fWWxWGy39+3bp9KlS2vVqlVq2bKlPDw89N1332nw4MEKDQ21W8+oUaPUqVMn2+1OnTppxIgRGjt2rMqVKyd/f/8sR4v37dun9u3by9PTU40aNdI333wji8WimJiYXGtOT09XRESEfH195efnpwkTJsgwDNv9NWrU0Jw5c2y3LRaL3n33XfXq1Uve3t6qW7euPv/88yzr3b59u1q1aiVvb2+1a9fOLvhf78iRI7JYLFq+fLnuvvtueXl5qXXr1jpw4IC2bt2qVq1aqVSpUrrvvvt0+vRp23Jbt25V3759ValSJfn6+io0NFS7d+/Ost4ff/zR1nbu3DlZLBatW7cux3qWL1+uoKAgValSxa79+++/V6dOneTt7a2yZcsqJCREf/75p6RrczR8+HCNGjVKZcuWVaVKlfTOO+/o0qVLCg8PV+nSpVWnTh2tWrXKbp1dunTR2bNntX79+hzrAXD7I/ACKFaef/55zZgxQ3v37lWzZs0cXu6DDz5QyZIltXnzZs2cOVNTpkxRbGysJCkjI0OhoaHy9vbW5s2btXDhQr3wwgsOr7dEiRLasmWL5s6dq9mzZ+vdd9/NdZnJkyfr4Ycf1u7du9W9e3f1799fZ8+etevzwgsvaNasWdq2bZtKlCihxx577Ia1TJw4UePHj9eOHTtUokQJPfrooxo7dqzmzp2rjRs36tdff9WLL75o63/hwgWFhYXpu+++0w8//KC6deuqe/fut3ze7caNG9WqVSu7th9//FH/+Mc/1KhRI8XHx+u7777TAw88oIyMDFufDz74QH5+ftqyZYuGDx+uJ598Un369FG7du20Y8cOde3aVQMHDlRKSoptGXd3dzVv3lwbN268pZoBFHEGAJjQf/7zH8PX19d2e+3atYYkIyYmxq5fWFiY8eCDD9q1jRw50ujYsaPtdseOHY327dvb9WndurXx3HPPGYZhGKtWrTJKlChhnDhxwnZ/bGysIcn49NNPc6yxY8eORsOGDQ2r1Wpre+6554yGDRvabgcGBhqvvfaa7bYkY/z48bbbFy9eNCQZq1atshvnN998Y+vz1VdfGZKM1NTUbOs4fPiwIcl49913bW3R0dGGJCMuLs7WNn36dKN+/fo5jicjI8MoXbq08cUXX9itd+fOnbY+f/75pyHJWLt2bY7rCQoKMqZMmWLX1q9fP+Ouu+7KcZnr5yg9Pd0oWbKkMXDgQFvbiRMnDElGfHy83bK9evUyBg8enOO6Adz+OMILoFi5/siho64/Gly5cmWdOnVK0rUrClSrVk3+/v62+9u0aePQeu+8805ZLBbb7eDgYB08eNDuyGVutZQsWVI+Pj62WrLrU7lyZUnK0ie39VaqVEmS1LRpU7u2v68jMTFRQ4cOVd26deXr6ysfHx9dvHhRCQkJuW7nRlJTU+Xp6WnXlnmE19H6XV1dVb58+Sz1S1kfBy8vL7ujvgDMhzP0ARQrJUuWtLvt4uJid86sJKWlpWVZzs3Nze62xWKR1WrN/wId4Egtf++TGahvVG92y1zf9vd1hIWF6cyZM5o7d64CAwPl4eGh4OBgXb16VdK1x1aS3eOb3WN7PT8/P9u5uZm8vLxuuFx2j4sjj8PZs2dVu3btG64fwO2LI7wAirUKFSrYXd1Bkt2HrBxRv359HT16VImJiba2rVu3OrTs5s2b7W5nngvr6uqapxqc4fvvv9eIESPUvXt3NW7cWB4eHkpKSrLdX6FCBUn2V89w5LFt0aKF9uzZY9fWrFkzxcXF5U/h1/n555/VokWLAlk3gKKBwAugWLv33nu1bds2ffjhhzp48KAmTpyon3/+OU/r6NKli2rXrq2wsDDt3r1b33//vcaPHy9JdqcrZCchIUGRkZHav3+/oqOj9cYbb2jkyJE3PZ7CVLduXX300Ufau3evNm/erP79+9sdifXy8tKdd95p+5Dg+vXrbY9LbkJCQhQfH293WkdUVJS2bt2qp556Srt379a+ffv01ltv2QXsm3HkyBEdO3ZMnTt3vqX1ACjaCLwAirWQkBBNmDBBY8eOVevWrXXhwgUNGjQoT+twdXVVTEyMLl68qNatW+vxxx+3XaXh+nNRrzdo0CClpqaqTZs2evrppzVy5Eg98cQTNz2ewvTee+/pzz//1B133KGBAwdqxIgRqlixol2fRYsWKT09XS1bttSoUaM0bdq0G673vvvuU4kSJfTNN9/Y2urVq6c1a9Zo165datOmjYKDg/XZZ5/d8rVzo6Oj1bVrVwUGBt7SegAUbRbj+pPXAAC37Pvvv1f79u3166+/cn7oTZg/f74+//xzff311wW2jatXr6pu3bpasmSJ7rrrrgLbDgDn40NrAJAPPv30U5UqVUp169bVr7/+qpEjR+quu+4i7N6kf/3rXzp37pwuXLhQYF8vnJCQoHHjxhF2gWKAI7wAkA8+/PBDTZs2TQkJCfLz81Pnzp01a9YslS9f3tmlAUCxR+AFAACAqfGhNQAAAJgagRcAAACmRuAFAACAqRF4AQAAYGoEXgAAAJgagRcAAACmRuAFAACAqRF4AQAAYGr/D9ycE5iT3n1DAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# a. Thực hiện bài 2 100 lần, mỗi lần bạn tính được trung bình mẫu. Vẽ đồ thị histogram cho 100 trung bình mẫu bạn tính được.\n",
        "\n",
        "def PPMEAN(data, sample_size, num_samples):\n",
        "  sample_means = []\n",
        "  for _ in range(num_samples):\n",
        "    sample = np.random.choice(data, size=sample_size, replace=False)\n",
        "    sample_mean = np.mean(sample)\n",
        "    sample_means.append(sample_mean)\n",
        "  return sample_means\n",
        "\n",
        "\n",
        "# Sử dụng hàm để lấy 100 trung bình mẫu với kích thước mẫu là 20\n",
        "sample_means_20 = PPMEAN(data=POP, sample_size = 20, num_samples=100)\n",
        "\n",
        "# Vẽ histogram cho 100 trung bình mẫu\n",
        "plt.figure(figsize=(8, 5))\n",
        "sns.histplot(sample_means_20, bins=10, color='skyblue', stat='density', edgecolor='black')\n",
        "plt.title('Histogram của 100 trung bình mẫu (cỡ 20)')\n",
        "plt.xlabel('Trung bình mẫu (cm)')\n",
        "plt.ylabel('Mật độ')\n",
        "plt.grid()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "ni7mdb3hUWoU",
        "outputId": "21464435-c9de-40f0-e303-88d27a73eaa0"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#  Thực hiện bài 3 100 lần, mỗi lần bạn tính được trung bình mẫu. Vẽ đồ thị histogram cho 100 trung bình mẫu bạn tính được.\n",
        "\n",
        "# Sử dụng hàm để lấy 100 trung bình mẫu với kích thước mẫu là 20\n",
        "sample_means_100 = PPMEAN(data=POP, sample_size = 100, num_samples=100)\n",
        "\n",
        "# Vẽ histogram cho 100 trung bình mẫu\n",
        "plt.figure(figsize=(8, 5))\n",
        "sns.histplot(sample_means_100, bins=20, color='skyblue', stat='density', edgecolor='black')\n",
        "plt.title('Histogram của 100 trung bình mẫu (cỡ 100)')\n",
        "plt.xlabel('Trung bình mẫu (cm)')\n",
        "plt.ylabel('Mật độ')\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 568
        },
        "id": "rLjMqs9dU4N5",
        "outputId": "3256c9e9-9bef-4bcd-c947-7c14b6ce19a8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# c. Vẽ histogram của hai câu a, b trong cùng một hình để so sánh. Theo bạn hình dạng của histogram thay đổi nói lên điều gì?\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "# Vẽ histogram cho trung bình mẫu kích thước 20\n",
        "sns.histplot(sample_means_20, bins=20, color='skyblue', stat='density', edgecolor='black', label='Cỡ 20', alpha=0.6)\n",
        "\n",
        "# Vẽ histogram cho trung bình mẫu kích thước 100\n",
        "sns.histplot(sample_means_100, bins=20, color='lightcoral', stat='density', edgecolor='black', label='Cỡ 100', alpha=0.6)\n",
        "\n",
        "plt.title('So sánh histogram của trung bình mẫu cỡ 20 và cỡ 100')\n",
        "plt.xlabel('Trung bình mẫu (cm)')\n",
        "plt.ylabel('Mật độ')\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JU6uXVKrG9H7"
      },
      "source": [
        "### Bài 5: Khảo sát tỷ lệ mẫu\n",
        "\n",
        "a. Bạn hãy mô phỏng 1000 lần lấy mẫu có kích cỡ 100. Vẽ histogram tỷ lệ người cao của các mẫu\n",
        "\n",
        "b. Bạn hãy mô phỏng 1000 lần lấy mẫu có kích cỡ 500. Vẽ histogram tỷ lệ người cao của các mẫu"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "MJtK1ftKG9H8",
        "outputId": "4ee7fd23-c1e3-4129-fb39-c75fa825ccf9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# a. Bạn hãy mô phỏng 1000 lần lấy mẫu có kích cỡ 100. Vẽ histogram tỷ lệ người cao của các mẫu\n",
        "POP = np.random.uniform(120,200,10000)\n",
        "\n",
        "def KSMAU(data, sample_size, num_samples):\n",
        "  sample_ratios = []\n",
        "  for _ in range(num_samples):\n",
        "    sample = np.random.choice(data, size=sample_size, replace=False)\n",
        "    count_tall = np.sum(sample >= 180)\n",
        "    ratio_tall = count_tall / sample_size\n",
        "    sample_ratios.append(ratio_tall)\n",
        "  return sample_ratios\n",
        "\n",
        "# Mô phỏng 1000 lần với kích thước mẫu 100\n",
        "sample_size_100 = 100\n",
        "num_samples = 1000\n",
        "tall_ratios_100 = KSMAU(POP,sample_size_100, num_samples)\n",
        "\n",
        "# Vẽ histogram tỷ lệ người cao\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.histplot(tall_ratios_100, bins=20, color='skyblue', stat='density', edgecolor='black')\n",
        "plt.title('Histogram tỷ lệ người cao (kích thước mẫu 100)')\n",
        "plt.xlabel('Tỷ lệ người cao (%)')\n",
        "plt.ylabel('Mật độ')\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "YdlkHeF3Yu5X",
        "outputId": "9e037d45-890c-47d7-aef3-7749cf88eb94"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# b. Bạn hãy mô phỏng 1000 lần lấy mẫu có kích cỡ 500. Vẽ histogram tỷ lệ người cao của các mẫu\n",
        "# Mô phỏng 1000 lần với kích thước mẫu 500\n",
        "sample_size_500 = 500\n",
        "tall_ratios_500 = KSMAU(POP,sample_size_500, num_samples)\n",
        "\n",
        "# Vẽ histogram tỷ lệ người cao\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.histplot(tall_ratios_500, bins=20, color='lightcoral', stat='density', edgecolor='black')\n",
        "plt.title('Histogram tỷ lệ người cao (kích thước mẫu 500)')\n",
        "plt.xlabel('Tỷ lệ người cao (%)')\n",
        "plt.ylabel('Mật độ')\n",
        "plt.grid()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hCdij5sOG9H8"
      },
      "source": [
        "### Bài 6: Khảo sát phương sai mẫu\n",
        "\n",
        "Bạn hãy mô phỏng 1000 lần lấy mẫu có kích cỡ 100. Vẽ histogram đại lượng phương sai của các mẫu (Phương sai bằng bình phương độ lệch chuẩn)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "bwviINKXG9H8",
        "outputId": "07eeb159-796e-47c0-8835-73d128bb005d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "POP = np.random.uniform(120,200,10000)\n",
        "\n",
        "PhuongSai =[]\n",
        "for _ in range(1000):\n",
        "  sample = np.random.choice(POP, size=100, replace=False)\n",
        "  sample_variance = np.var(sample)\n",
        "  PhuongSai.append(sample_variance)\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.histplot(PhuongSai, bins=20, color='skyblue', stat='density', edgecolor='black')\n",
        "plt.xlabel('Phương sai')\n",
        "plt.ylabel('Mật độ')\n",
        "plt.grid()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P-HuH-izG9H8"
      },
      "source": [
        "### Bài 7:  Bài Tập Ước Lượng\n",
        "\n",
        "Khảo sát cân nặng (kg) của gà khi xuất chuồng, người ta cân một số con và có kết quả như sau:\n",
        "\n",
        "> 2.1, 1.8, 2.0, 2.3, 1.7, 1.5, 2.0, 2.2, 1.8\n",
        "\n",
        "Giả sử cân nặng của gà là biến ngẫu nhiên có phân phối chuẩn. Hãy lập khoảng tin cậy cho cân nặng trung bình của  khi xuất chuồng với độ tin cậy là 95%. Trong 2 trường hợp:\n",
        "\n",
        "a, Biết $\\sigma = 0.3$\n",
        "\n",
        "b, Không biết $\\sigma$\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VOBX75oDG9H8",
        "outputId": "07be4343-45da-45c0-fc37-b444e088db27"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Khoảng tin cậy 95% cho cân nặng trung bình (σ đã biết): (1.7373369348793277, 2.1293297317873385)\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import scipy.stats as stats\n",
        "\n",
        "# Dữ liệu\n",
        "weights = np.array([2.1, 1.8, 2.0, 2.3, 1.7, 1.5, 2.0, 2.2, 1.8])\n",
        "\n",
        "# a, Biết  σ=0.3\n",
        "\n",
        "# Tính toán các thông số\n",
        "n = len(weights)  # Kích thước mẫu\n",
        "mean_weight = np.mean(weights)  # Trung bình mẫu\n",
        "sigma = 0.3  # Độ lệch chuẩn đã biết\n",
        "alpha = 0.05  # Mức ý nghĩa\n",
        "\n",
        "# Tính khoảng tin cậy\n",
        "z_score = stats.norm.ppf(1 - alpha / 2)  # Giá trị z cho 95% khoảng tin cậy\n",
        "margin_of_error = z_score * (sigma / np.sqrt(n))  # Lỗi biên\n",
        "\n",
        "confidence_interval = (mean_weight - margin_of_error, mean_weight + margin_of_error)\n",
        "print(\"Khoảng tin cậy 95% cho cân nặng trung bình (σ đã biết):\", confidence_interval)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LQ7FQiWfbQ-b",
        "outputId": "61443466-abed-4d28-a663-00fb29e8d0dd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Khoảng tin cậy 95% cho cân nặng trung bình (σ không biết): (1.7373606652694715, 2.129306001397195)\n"
          ]
        }
      ],
      "source": [
        "# b, Không biết  σ\n",
        "# Tính toán độ lệch chuẩn mẫu\n",
        "sample_std = np.std(weights, ddof=1)  # Độ lệch chuẩn mẫu\n",
        "\n",
        "# Tính khoảng tin cậy\n",
        "t_score = stats.t.ppf(1 - alpha / 2, n - 1)  # Giá trị t cho 95% khoảng tin cậy\n",
        "margin_of_error_t = t_score * (sample_std / np.sqrt(n))  # Lỗi biên\n",
        "\n",
        "confidence_interval_t = (mean_weight - margin_of_error_t, mean_weight + margin_of_error_t)\n",
        "print(\"Khoảng tin cậy 95% cho cân nặng trung bình (σ không biết):\", confidence_interval_t)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4u7N_zoRG9H8"
      },
      "source": [
        "### Bài 8\n",
        "\n",
        "Bộ dữ liệu `SAT-lard.csv` chứa điểm thi SAT của 1000 học sinh.\n",
        "\n",
        "a. Giả sử bộ dữ liệu là kết quả của quá trình lấy mẫu ngẫu nhiên từ các học sinh. Bạn hãy xây dựng khoảng tin cậy cho trung bình điểm thi của tất cả các sinh viên với độ tin cậy 99% (`Đáp án: 105.2 ± 3.10`)\n",
        "\n",
        "b. Giả sử bộ dữ liệu là kết quả của quá trình lấy mẫu ngẫu nhiên từ các học sinh. Bạn hãy xây dựng khoảng tin cậy cho trung bình điểm thi của tất cả các sinh viên với độ tin cậy (`Đáp án: 105.2 ± 1.86`)\n",
        "\n",
        "c. Giả sử bộ dữ liệu là toàn bộ kết quả điểm thi SAT của học sinh một trường phố thông. Tính điểm SAT trung bình của trường phổ thông đấy. (`Đáp án:  μ = 1528.74`)\n",
        "\n",
        "d. Giả sử bộ dữ liệu là toàn bộ kết quả điểm thi SAT của học sinh một trường phố thông. Sử dụng mẫu là điểm thi của 36 học sinh đầu tiên trong bộ dữ liệu, bạn hãy xây dựng khoảng tin cậy **99%** cho điểm SAT trung bình của trường phổ thông đấy. (`Đáp án: (1428.22, 1602.89)`)\n",
        "\n",
        "e. Giả sử bộ dữ liệu là toàn bộ kết quả điểm thi SAT của học sinh một trường phố thông. Sử dụng mẫu là điểm thi của 36 học sinh đầu tiên trong bộ dữ liệu, bạn hãy xây dựng khoảng tin cậy **95%** cho điểm SAT trung bình của trường phổ thông đấy. Cho biết khoảng ước lượng bạn xây dựng có đúng không, dựa trên giá trị đã tính ở câu trên."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZUxunyz-G9H8",
        "outputId": "7bd5d69e-9fe4-4c39-df70-5ba7bd4df412"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   SAT Score  College GPA\n",
            "0       1300         3.66\n",
            "1       1520         2.92\n",
            "2       1580         2.66\n",
            "3       1430         2.27\n",
            "4       1610         2.35\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Đọc dữ liệu từ tệp CSV\n",
        "data = pd.read_csv('SAT-lard.csv')\n",
        "\n",
        "# Xem các dòng đầu tiên của dữ liệu\n",
        "print(data.head())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mp9EGlBBcBPG",
        "outputId": "2c297079-a492-4617-8549-67d6cb07fa74"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Khoảng tin cậy 99% cho trung bình điểm thi SAT: (1511.3939951002396, 1546.0860048997604)\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import scipy.stats as stats\n",
        "\n",
        "# Lấy điểm thi SAT\n",
        "sat_scores = data['SAT Score']\n",
        "\n",
        "# Tính các thông số\n",
        "n = len(sat_scores)  # Kích thước mẫu\n",
        "mean_score = np.mean(sat_scores)  # Trung bình mẫu\n",
        "std_score = np.std(sat_scores, ddof=1)  # Độ lệch chuẩn mẫu\n",
        "alpha = 0.01  # Mức ý nghĩa cho khoảng tin cậy 99%\n",
        "\n",
        "# Tính khoảng tin cậy\n",
        "t_score = stats.t.ppf(1 - alpha / 2, n - 1)  # Giá trị t cho 99% khoảng tin cậy\n",
        "margin_of_error = t_score * (std_score / np.sqrt(n))\n",
        "\n",
        "confidence_interval_99 = (mean_score - margin_of_error, mean_score + margin_of_error)\n",
        "print(\"Khoảng tin cậy 99% cho trung bình điểm thi SAT:\", confidence_interval_99)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cjyDT7-zcDEK",
        "outputId": "3b1008b6-27a2-44f2-e29c-6eaeddb8b899"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Khoảng tin cậy 90% cho trung bình điểm thi SAT: (1517.6742187756427, 1539.8057812243574)\n"
          ]
        }
      ],
      "source": [
        "alpha = 0.10  # Mức ý nghĩa cho khoảng tin cậy 90%\n",
        "\n",
        "# Tính khoảng tin cậy\n",
        "t_score = stats.t.ppf(1 - alpha / 2, n - 1)  # Giá trị t cho 90% khoảng tin cậy\n",
        "margin_of_error = t_score * (std_score / np.sqrt(n))\n",
        "\n",
        "confidence_interval_90 = (mean_score - margin_of_error, mean_score + margin_of_error)\n",
        "print(\"Khoảng tin cậy 90% cho trung bình điểm thi SAT:\", confidence_interval_90)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0cL8z0UecEs5",
        "outputId": "578763f9-b702-46c0-d457-a79176612595"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Điểm SAT trung bình của trường phổ thông: 1528.74\n"
          ]
        }
      ],
      "source": [
        "# Tính trung bình điểm SAT\n",
        "mean_score_full = np.mean(sat_scores)\n",
        "print(\"Điểm SAT trung bình của trường phổ thông:\", mean_score_full)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PgRdQzAAcG-W",
        "outputId": "9c1d3099-5658-44d4-b86a-be7ebb8cd04b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Khoảng tin cậy 99% cho điểm SAT trung bình (36 học sinh đầu tiên): (1423.171232801838, 1607.9398783092734)\n"
          ]
        }
      ],
      "source": [
        "# Lấy điểm thi của 36 học sinh đầu tiên\n",
        "sample_first_36 = sat_scores[:36]\n",
        "\n",
        "# Tính toán khoảng tin cậy\n",
        "n_sample = len(sample_first_36)\n",
        "mean_sample = np.mean(sample_first_36)\n",
        "std_sample = np.std(sample_first_36, ddof=1)\n",
        "alpha = 0.01  # Mức ý nghĩa cho khoảng tin cậy 99%\n",
        "\n",
        "# Tính khoảng tin cậy\n",
        "t_score = stats.t.ppf(1 - alpha / 2, n_sample - 1)\n",
        "margin_of_error_sample = t_score * (std_sample / np.sqrt(n_sample))\n",
        "\n",
        "confidence_interval_sample_99 = (mean_sample - margin_of_error_sample, mean_sample + margin_of_error_sample)\n",
        "print(\"Khoảng tin cậy 99% cho điểm SAT trung bình (36 học sinh đầu tiên):\", confidence_interval_sample_99)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q2gPWMpscIZu",
        "outputId": "059ef580-39fc-45d8-fc59-e2bea32f79ad"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Khoảng tin cậy 95% cho điểm SAT trung bình (36 học sinh đầu tiên): (1446.6996332344288, 1584.4114778766825)\n",
            "Khoảng tin cậy 95% không bao gồm khoảng tin cậy 99%.\n"
          ]
        }
      ],
      "source": [
        "# Tính khoảng tin cậy 95%\n",
        "alpha = 0.05  # Mức ý nghĩa cho khoảng tin cậy 95%\n",
        "\n",
        "# Tính khoảng tin cậy\n",
        "t_score = stats.t.ppf(1 - alpha / 2, n_sample - 1)\n",
        "margin_of_error_sample_95 = t_score * (std_sample / np.sqrt(n_sample))\n",
        "\n",
        "confidence_interval_sample_95 = (mean_sample - margin_of_error_sample_95, mean_sample + margin_of_error_sample_95)\n",
        "print(\"Khoảng tin cậy 95% cho điểm SAT trung bình (36 học sinh đầu tiên):\", confidence_interval_sample_95)\n",
        "\n",
        "# Nhận xét\n",
        "if confidence_interval_sample_95[0] <= confidence_interval_sample_99[0] and confidence_interval_sample_95[1] >= confidence_interval_sample_99[1]:\n",
        "    print(\"Khoảng tin cậy 95% bao gồm khoảng tin cậy 99%.\")\n",
        "else:\n",
        "    print(\"Khoảng tin cậy 95% không bao gồm khoảng tin cậy 99%.\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YFBpmPoKG9H8"
      },
      "source": [
        "---"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    }
  },
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