{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8zg0VI58na-2"
   },
   "source": [
    "### PhucLam_TKMT_LAB08"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kNKf8S2EquHC"
   },
   "source": [
    "# Lab08 - Bài tập"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RFDWAhb8na-6"
   },
   "source": [
    "### Bài 1\n",
    "\n",
    "Nghiên cứu mức thu nhập ($X$) và chi tiêu ($Y$) trong một tháng của một công ty. Khảo sát ngẫu nhiên 7 nhân viên, ta thu được bảng số liệu sau:\n",
    "\n",
    "| X            | 130     | 150      | 133      | 170      | 170      | 210      | 230      |\n",
    "| :----------: | :-----: | :------: | :------: | :------: | :------: | :------: | :------: |\n",
    "| **Y**        | **84**  | **120**  | **108**  | **130**  | **130**  | **150**  | **160**  |\n",
    "\n",
    "\n",
    "a. Dựa vào bảng dữ liệu trên cho biết có xây dựng được mô hình hồi quy hay không? Nếu có hãy xây dựng mô hình hồi quy để ước tính chi tiêu dựa theo thu nhập\n",
    "\n",
    "b. Dự đoán mức chi tiêu của nhân viên công ty trên nếu biết thu nhập là 200$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4722,
     "status": "ok",
     "timestamp": 1730781684035,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "p3Kr7X5Zna-7",
    "outputId": "ffe01a0b-1848-4311-a859-857cc355cb99"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hệ số tương quan Pearson giữa thu nhập và chi tiêu: 0.9470524056307565\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.897\n",
      "Model:                            OLS   Adj. R-squared:                  0.876\n",
      "Method:                 Least Squares   F-statistic:                     43.50\n",
      "Date:                Tue, 05 Nov 2024   Prob (F-statistic):            0.00120\n",
      "Time:                        04:41:23   Log-Likelihood:                -24.099\n",
      "No. Observations:                   7   AIC:                             52.20\n",
      "Df Residuals:                       5   BIC:                             52.09\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         17.2706     16.829      1.026      0.352     -25.990      60.531\n",
      "x1             0.6380      0.097      6.595      0.001       0.389       0.887\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   1.452\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                1.671\n",
      "Skew:                          -1.192   Prob(JB):                        0.434\n",
      "Kurtosis:                       3.206   Cond. No.                         865.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "Dự đoán mức chi tiêu khi thu nhập là 200$: 144.8658752125922\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 7 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# Dữ liệu thu nhập (X) và chi tiêu (Y)\n",
    "X = np.array([130, 150, 133, 170, 170, 210, 230])\n",
    "Y = np.array([84, 120, 108, 130, 130, 150, 160])\n",
    "\n",
    "# Kiểm tra tương quan\n",
    "correlation = np.corrcoef(X, Y)[0, 1]\n",
    "print(f\"Hệ số tương quan Pearson giữa thu nhập và chi tiêu: {correlation}\")\n",
    "\n",
    "# Xây dựng mô hình hồi quy tuyến tính\n",
    "X = sm.add_constant(X)  # Thêm cột 1s vào X để ước lượng hệ số a và b\n",
    "model = sm.OLS(Y, X).fit()\n",
    "print(model.summary())\n",
    "\n",
    "# Dự đoán mức chi tiêu khi thu nhập là 200\n",
    "X_new = np.array([[1, 200]])\n",
    "Y_pred = model.predict(X_new)\n",
    "print(f\"Dự đoán mức chi tiêu khi thu nhập là 200$: {Y_pred[0]}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RuDr7NuIna-8"
   },
   "source": [
    "### Bài 2\n",
    "\n",
    "Tiến hành nghiên cứu mối quan hệ giữa:\n",
    "\n",
    "- X: giá bán kẻ của thịt gà (nghìn/kg)\n",
    "- Y: lượng thịt gà tiêu thụ (kg/tháng)\n",
    "\n",
    "Dữ liệu thu được gồm 10 tháng như sau:\n",
    "\n",
    "| X            | 35       | 40       | 45       | 40       | 38       | 40       | 45       | 30       | 42       | 38       |\n",
    "| :----------: | :------: | :------: | :------: | :------: | :------: | :------: | :------: | :------: | :------: | :------: |\n",
    "| **Y**        | **2.29** | **2.04** | **0.59** | **2.06** | **2.50** | **2.32** | **1.57** | **3.96** | **1.43** | **2.34** |\n",
    "\n",
    "\n",
    "a. Dựa vào bảng dữ liệu trên cho biết có xây dựng được mô hình hồi quy hay không? Nếu có hãy xây dựng mô hình hồi quy để ước tính lượng tiêu thụ thịt gà theo giá bán.\n",
    "\n",
    "b. Dự đoán lượng tiêu thụ khi giá bán lẻ là 41(kg/tháng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 323,
     "status": "ok",
     "timestamp": 1730781814071,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "B1Dlp7Wdna-8",
    "outputId": "fd11b647-33ab-4e43-db9b-93301f89f02a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hệ số tương quan Pearson giữa giá bán và lượng tiêu thụ: -0.9195328739188577\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.846\n",
      "Model:                            OLS   Adj. R-squared:                  0.826\n",
      "Method:                 Least Squares   F-statistic:                     43.79\n",
      "Date:                Tue, 05 Nov 2024   Prob (F-statistic):           0.000166\n",
      "Time:                        04:43:33   Log-Likelihood:                -2.9041\n",
      "No. Observations:                  10   AIC:                             9.808\n",
      "Df Residuals:                       8   BIC:                             10.41\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          9.0808      1.060      8.570      0.000       6.637      11.524\n",
      "x1            -0.1774      0.027     -6.618      0.000      -0.239      -0.116\n",
      "==============================================================================\n",
      "Omnibus:                        0.926   Durbin-Watson:                   1.332\n",
      "Prob(Omnibus):                  0.629   Jarque-Bera (JB):                0.714\n",
      "Skew:                          -0.542   Prob(JB):                        0.700\n",
      "Kurtosis:                       2.264   Cond. No.                         366.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "Dự đoán lượng tiêu thụ khi giá bán lẻ là 41 nghìn/kg: 1.808462383305872\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/scipy/stats/_axis_nan_policy.py:531: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=10\n",
      "  res = hypotest_fun_out(*samples, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# Dữ liệu giá bán lẻ (X) và lượng tiêu thụ (Y)\n",
    "X = np.array([35, 40, 45, 40, 38, 40, 45, 30, 42, 38])\n",
    "Y = np.array([2.29, 2.04, 0.59, 2.06, 2.50, 2.32, 1.57, 3.96, 1.43, 2.34])\n",
    "\n",
    "# Kiểm tra hệ số tương quan\n",
    "correlation = np.corrcoef(X, Y)[0, 1]\n",
    "print(f\"Hệ số tương quan Pearson giữa giá bán và lượng tiêu thụ: {correlation}\")\n",
    "\n",
    "# Xây dựng mô hình hồi quy tuyến tính\n",
    "X = sm.add_constant(X)  # Thêm cột 1s vào X để ước lượng hệ số a và b\n",
    "model = sm.OLS(Y, X).fit()\n",
    "print(model.summary())\n",
    "\n",
    "# Dự đoán lượng tiêu thụ khi giá bán lẻ là 41\n",
    "X_new = np.array([[1, 41]])\n",
    "Y_pred = model.predict(X_new)\n",
    "print(f\"Dự đoán lượng tiêu thụ khi giá bán lẻ là 41 nghìn/kg: {Y_pred[0]}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CUW8vNwfna-9"
   },
   "source": [
    "## Bài 3\n",
    "\n",
    "Dụa trên file dữ liệu `Advertising.csv` bạn hãy dự đoán doanh số bán hàng (`sales`) dựa trên số tiền chi cho các nền tảng tiếp thị khác nhau như `TV`, `Radio` và `Newspaper`\n",
    "\n",
    "a. Đọc file dữ liệu cho biết các thông tin cơ bản về dữ liệu: kích thước, dữ liệu có giá trị trống hay không, hiện các giá trị thống kê cơ bản của các thuộc tính.\n",
    "\n",
    "b. Để dự đoán doanh số bằng phương pháp hồi quy tuyến tính đơn giản (Simple Linear Regression) trước tiên cần lựa chọn một trong các thuộc tính còn lại. Theo bạn thuộc tính nào phù hợp để xây dựng mô hình hồi quy. Giải thích và minh họa bằng 2 cách\n",
    "\n",
    "c. Xây dụng phương trình hồi quy, trực quan mô hình (đường thẳng) tìm được. Theo bạn để đánh giá mô hình trên cần dùng đại lượng nào?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 1704,
     "status": "ok",
     "timestamp": 1730782330789,
     "user": {
      "displayName": "Tài liệu IT - IUH",
      "userId": "00234609408062679612"
     },
     "user_tz": -420
    },
    "id": "0znhzzchpuKT",
    "outputId": "42378583-cf02-4f68-a0db-79d0665818cb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kích thước dữ liệu: (200, 5)\n",
      "\n",
      "Giá trị trống trong dữ liệu:\n",
      "Unnamed: 0    0\n",
      "TV            0\n",
      "radio         0\n",
      "newspaper     0\n",
      "sales         0\n",
      "dtype: int64\n",
      "\n",
      "Các giá trị thống kê cơ bản:\n",
      "       Unnamed: 0          TV       radio   newspaper       sales\n",
      "count  200.000000  200.000000  200.000000  200.000000  200.000000\n",
      "mean   100.500000  147.042500   23.264000   30.554000   14.022500\n",
      "std     57.879185   85.854236   14.846809   21.778621    5.217457\n",
      "min      1.000000    0.700000    0.000000    0.300000    1.600000\n",
      "25%     50.750000   74.375000    9.975000   12.750000   10.375000\n",
      "50%    100.500000  149.750000   22.900000   25.750000   12.900000\n",
      "75%    150.250000  218.825000   36.525000   45.100000   17.400000\n",
      "max    200.000000  296.400000   49.600000  114.000000   27.000000\n",
      "\n",
      "Hệ số tương quan với sales:\n",
      "Unnamed: 0   -0.051616\n",
      "TV            0.782224\n",
      "radio         0.576223\n",
      "newspaper     0.228299\n",
      "sales         1.000000\n",
      "Name: sales, dtype: float64\n",
      "Index(['Unnamed: 0', 'TV', 'radio', 'newspaper', 'sales'], dtype='object')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1050x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  sales   R-squared:                       0.612\n",
      "Model:                            OLS   Adj. R-squared:                  0.610\n",
      "Method:                 Least Squares   F-statistic:                     312.1\n",
      "Date:                Tue, 05 Nov 2024   Prob (F-statistic):           1.47e-42\n",
      "Time:                        04:52:09   Log-Likelihood:                -519.05\n",
      "No. Observations:                 200   AIC:                             1042.\n",
      "Df Residuals:                     198   BIC:                             1049.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          7.0326      0.458     15.360      0.000       6.130       7.935\n",
      "TV             0.0475      0.003     17.668      0.000       0.042       0.053\n",
      "==============================================================================\n",
      "Omnibus:                        0.531   Durbin-Watson:                   1.935\n",
      "Prob(Omnibus):                  0.767   Jarque-Bera (JB):                0.669\n",
      "Skew:                          -0.089   Prob(JB):                        0.716\n",
      "Kurtosis:                       2.779   Cond. No.                         338.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hệ số xác định (R^2) của mô hình: 0.611875050850071\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# Phần a: Đọc dữ liệu và hiển thị thông tin cơ bản\n",
    "data = pd.read_csv('Advertising.csv')\n",
    "\n",
    "# Hiển thị kích thước dữ liệu\n",
    "print(\"Kích thước dữ liệu:\", data.shape)\n",
    "\n",
    "# Kiểm tra các giá trị trống\n",
    "print(\"\\nGiá trị trống trong dữ liệu:\")\n",
    "print(data.isnull().sum())\n",
    "\n",
    "# Thống kê cơ bản của các thuộc tính\n",
    "print(\"\\nCác giá trị thống kê cơ bản:\")\n",
    "print(data.describe())\n",
    "\n",
    "# Phần b: Chọn thuộc tính để xây dựng mô hình hồi quy\n",
    "# Tính hệ số tương quan giữa các thuộc tính với sales\n",
    "correlation = data.corr()\n",
    "print(\"\\nHệ số tương quan với sales:\")\n",
    "print(correlation['sales'])\n",
    "\n",
    "# Vẽ biểu đồ phân tán cho các thuộc tính với sales để trực quan\n",
    "# Check the actual column names in your DataFrame\n",
    "print(data.columns)\n",
    "# Adjust the column names in x_vars if necessary.\n",
    "# For example, if the column name is 'radio' instead of 'Radio', change it to:\n",
    "sns.pairplot(data, x_vars=['TV', 'radio', 'newspaper'], y_vars='sales', height=5, aspect=0.7)\n",
    "plt.show()\n",
    "\n",
    "# Lựa chọn thuộc tính: Dựa trên hệ số tương quan, TV có mối quan hệ mạnh nhất với sales\n",
    "\n",
    "# Phần c: Xây dựng mô hình hồi quy với thuộc tính TV và trực quan hóa\n",
    "X = data['TV']\n",
    "y = data['sales']\n",
    "\n",
    "# Thêm hệ số chặn vào X\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "# Xây dựng mô hình hồi quy tuyến tính đơn giản\n",
    "model = sm.OLS(y, X).fit()\n",
    "print(model.summary())\n",
    "\n",
    "# Trực quan hóa đường hồi quy\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(data['TV'], data['sales'], color='blue', label='Dữ liệu thực tế')\n",
    "plt.plot(data['TV'], model.predict(X), color='red', label='Đường hồi quy')\n",
    "plt.xlabel('Chi tiêu cho TV (nghìn USD)')\n",
    "plt.ylabel('Doanh số (nghìn USD)')\n",
    "plt.title('Hồi quy tuyến tính giữa TV và sales')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Đánh giá mô hình\n",
    "r_squared = model.rsquared\n",
    "print(f\"Hệ số xác định (R^2) của mô hình: {r_squared}\")"
   ]
  }
 ],
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   "pygments_lexer": "ipython3",
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