Companions Breweries Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "104a4aef",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install pandas numpy statsmodels xlrd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5fb44038",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   PUB    REV  MAN  STA  TYP  INCOME   COM\n",
      "0    1   4.32    2    8    1    10.0  0.10\n",
      "1    2   9.69    6    7    1    12.0  0.68\n",
      "2    3  12.56    7    5    1     9.0  0.79\n",
      "3    4  16.54    9    6    1     8.3  0.68\n",
      "4    5   9.41    3    7    1     7.9  0.96\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load the data from the Excel file\n",
    "df = pd.read_excel('Companions.xlsx')\n",
    "\n",
    "# Display the first few rows of the dataset\n",
    "print(df.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b7f2e008",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PUB         int64\n",
      "REV       float64\n",
      "MAN         int64\n",
      "STA         int64\n",
      "TYP         int64\n",
      "INCOME    float64\n",
      "COM       float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Display the data types of all columns\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e52f945e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define a list of predictor variables\n",
    "predictor_vars = ['MAN', 'STA', 'TYP', 'INCOME', 'COM']\n",
    "\n",
    "# Create scatter plots for each predictor variable vs 'REV'\n",
    "plt.figure(figsize=(15, 10))\n",
    "for i, var in enumerate(predictor_vars, 1):\n",
    "    plt.subplot(2, 3, i)\n",
    "    plt.scatter(df[var], df['REV'], alpha=0.5)\n",
    "    plt.xlabel(var)\n",
    "    plt.ylabel('Revenue (REV)')\n",
    "    plt.title(f'Scatter Plot of {var} vs Revenue')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a21aa80a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             MAN       STA       TYP    INCOME       COM       REV\n",
      "MAN     1.000000 -0.391449 -0.080618 -0.098316  0.053454  0.933402\n",
      "STA    -0.391449  1.000000 -0.038127  0.135303 -0.060660 -0.408426\n",
      "TYP    -0.080618 -0.038127  1.000000 -0.287721  0.066518  0.124112\n",
      "INCOME -0.098316  0.135303 -0.287721  1.000000  0.170229 -0.339120\n",
      "COM     0.053454 -0.060660  0.066518  0.170229  1.000000  0.021672\n",
      "REV     0.933402 -0.408426  0.124112 -0.339120  0.021672  1.000000\n"
     ]
    }
   ],
   "source": [
    "# Calculate the correlation matrix\n",
    "correlation_matrix = df[['MAN', 'STA', 'TYP', 'INCOME', 'COM', 'REV']].corr()\n",
    "\n",
    "# Print the correlation matrix\n",
    "print(correlation_matrix)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f7d84e48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Calculate the correlation matrix for the independent variables\n",
    "independent_corr_matrix = df[['MAN', 'STA', 'TYP', 'INCOME', 'COM']].corr()\n",
    "\n",
    "# Create a mask for the upper triangle\n",
    "mask = np.triu(np.ones_like(independent_corr_matrix, dtype=bool))\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "# Create a heatmap of the correlation matrix for the independent variables\n",
    "sns.heatmap(independent_corr_matrix, annot=True, cmap='coolwarm', mask=mask, fmt=\".2f\", linewidths=0.5)\n",
    "\n",
    "# Set the title\n",
    "plt.title('Correlation Matrix Heatmap (Independent Variables)')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4dfcd454",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    REV   R-squared:                       0.951\n",
      "Model:                            OLS   Adj. R-squared:                  0.945\n",
      "Method:                 Least Squares   F-statistic:                     157.9\n",
      "Date:                Sat, 07 Oct 2023   Prob (F-statistic):           1.16e-25\n",
      "Time:                        13:19:15   Log-Likelihood:                -60.057\n",
      "No. Observations:                  47   AIC:                             132.1\n",
      "Df Residuals:                      41   BIC:                             143.2\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          4.6350      1.134      4.089      0.000       2.346       6.924\n",
      "MAN            1.5886      0.066     24.092      0.000       1.455       1.722\n",
      "STA           -0.0339      0.081     -0.420      0.677      -0.197       0.129\n",
      "TYP            1.0870      0.289      3.756      0.001       0.502       1.672\n",
      "INCOME        -0.4326      0.079     -5.504      0.000      -0.591      -0.274\n",
      "COM           -0.0455      0.671     -0.068      0.946      -1.401       1.310\n",
      "==============================================================================\n",
      "Omnibus:                       36.614   Durbin-Watson:                   2.008\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              108.270\n",
      "Skew:                           2.057   Prob(JB):                     3.09e-24\n",
      "Kurtosis:                       9.193   Cond. No.                         114.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "# Define the independent variables (predictors)\n",
    "X = df[['MAN', 'STA', 'TYP', 'INCOME', 'COM']]\n",
    "\n",
    "# Add a constant term to the model (intercept)\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "# Define the dependent variable (target)\n",
    "y = df['REV']\n",
    "\n",
    "# Fit the multiple linear regression model\n",
    "model = sm.OLS(y, X).fit()\n",
    "\n",
    "# Print the regression summary\n",
    "print(model.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9a027226",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    REV   R-squared:                       0.951\n",
      "Model:                            OLS   Adj. R-squared:                  0.946\n",
      "Method:                 Least Squares   F-statistic:                     202.2\n",
      "Date:                Sat, 07 Oct 2023   Prob (F-statistic):           7.65e-27\n",
      "Time:                        13:19:15   Log-Likelihood:                -60.060\n",
      "No. Observations:                  47   AIC:                             130.1\n",
      "Df Residuals:                      42   BIC:                             139.4\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          4.6189      1.095      4.218      0.000       2.409       6.829\n",
      "MAN            1.5883      0.065     24.420      0.000       1.457       1.720\n",
      "TYP            1.0845      0.284      3.824      0.000       0.512       1.657\n",
      "INCOME        -0.4337      0.076     -5.715      0.000      -0.587      -0.281\n",
      "STA           -0.0336      0.080     -0.422      0.676      -0.194       0.127\n",
      "==============================================================================\n",
      "Omnibus:                       36.658   Durbin-Watson:                   2.011\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              108.902\n",
      "Skew:                           2.057   Prob(JB):                     2.25e-24\n",
      "Kurtosis:                       9.219   Cond. No.                         110.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "# Define the independent variables (predictors)\n",
    "X = df[['MAN', 'TYP', 'INCOME', 'STA']]\n",
    "\n",
    "# Add a constant term to the model (intercept)\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "# Define the dependent variable (target)\n",
    "y = df['REV']\n",
    "\n",
    "# Fit the multiple linear regression model\n",
    "model = sm.OLS(y, X).fit()\n",
    "\n",
    "# Print the regression summary\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8f5a3d9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    REV   R-squared:                       0.950\n",
      "Model:                            OLS   Adj. R-squared:                  0.947\n",
      "Method:                 Least Squares   F-statistic:                     274.8\n",
      "Date:                Sat, 07 Oct 2023   Prob (F-statistic):           4.61e-28\n",
      "Time:                        13:19:16   Log-Likelihood:                -60.159\n",
      "No. Observations:                  47   AIC:                             128.3\n",
      "Df Residuals:                      43   BIC:                             135.7\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          4.3971      0.951      4.623      0.000       2.479       6.315\n",
      "MAN            1.5989      0.059     26.906      0.000       1.479       1.719\n",
      "TYP            1.0901      0.281      3.885      0.000       0.524       1.656\n",
      "INCOME        -0.4365      0.075     -5.829      0.000      -0.587      -0.285\n",
      "==============================================================================\n",
      "Omnibus:                       35.486   Durbin-Watson:                   2.026\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              102.666\n",
      "Skew:                           1.989   Prob(JB):                     5.09e-23\n",
      "Kurtosis:                       9.049   Cond. No.                         87.8\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "# Define the independent variables (predictors) without 'STA' and 'COM'\n",
    "X = df[['MAN', 'TYP', 'INCOME']]\n",
    "\n",
    "# Add a constant term to the model (intercept)\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "# Define the dependent variable (target)\n",
    "y = df['REV']\n",
    "\n",
    "# Fit the multiple linear regression model\n",
    "model = sm.OLS(y, X).fit()\n",
    "\n",
    "# Print the new regression summary\n",
    "print(model.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fc6316cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# Load the data from the Excel file\n",
    "df = pd.read_excel('Companions.xlsx')\n",
    "\n",
    "# Define the independent variables (predictors) without 'STA' and 'COM'\n",
    "X = df[['MAN', 'TYP', 'INCOME']]\n",
    "\n",
    "# Add a constant term to the model (intercept)\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "# Define the dependent variable (target)\n",
    "y = df['REV']\n",
    "\n",
    "# Fit the multiple linear regression model\n",
    "model = sm.OLS(y, X).fit()\n",
    "\n",
    "# Get the predicted values from the model\n",
    "predicted_values = model.predict(X)\n",
    "\n",
    "# Create a scatter plot of the data\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(y, predicted_values, alpha=0.5)\n",
    "plt.xlabel('Actual Revenue')\n",
    "plt.ylabel('Predicted Revenue')\n",
    "plt.title('Actual vs. Predicted Revenue')\n",
    "\n",
    "# Plot the regression line\n",
    "plt.plot([min(y), max(y)], [min(y), max(y)], linestyle='--', color='red', linewidth=2)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bf8dc124",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# Load the data from the Excel file\n",
    "df = pd.read_excel('Companions.xlsx')\n",
    "\n",
    "# Define the independent variables (predictors) without 'STA' and 'COM'\n",
    "X = df[['MAN', 'TYP', 'INCOME']]\n",
    "\n",
    "# Add a constant term to the model (intercept)\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "# Define the dependent variable (target)\n",
    "y = df['REV']\n",
    "\n",
    "# Fit the multiple linear regression model\n",
    "model = sm.OLS(y, X).fit()\n",
    "\n",
    "# Get the residuals from the model\n",
    "residuals = model.resid\n",
    "\n",
    "# Create a Residuals vs. Fitted Values plot\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(model.fittedvalues, residuals, alpha=0.5)\n",
    "plt.xlabel('Fitted Values')\n",
    "plt.ylabel('Residuals')\n",
    "plt.axhline(y=0, color='red', linestyle='--', linewidth=2)\n",
    "plt.title('Residuals vs. Fitted Values')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c5588e93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# Load the data from the Excel file\n",
    "df = pd.read_excel('Companions.xlsx')\n",
    "\n",
    "# Define the independent variables (predictors) without 'STA' and 'COM'\n",
    "X = df[['MAN', 'TYP', 'INCOME']]\n",
    "\n",
    "# Add a constant term to the model (intercept)\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "# Define the dependent variable (target)\n",
    "y = df['REV']\n",
    "\n",
    "# Fit the multiple linear regression model\n",
    "model = sm.OLS(y, X).fit()\n",
    "\n",
    "# Get the residuals from the model\n",
    "residuals = model.resid\n",
    "\n",
    "# Create separate Residuals vs. Predictor Variable plots for each variable\n",
    "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
    "\n",
    "# Plot for 'MAN' variable\n",
    "axes[0].scatter(X['MAN'], residuals, alpha=0.5)\n",
    "axes[0].set_xlabel('MAN')\n",
    "axes[0].set_ylabel('Residuals')\n",
    "axes[0].axhline(y=0, color='red', linestyle='--', linewidth=2)\n",
    "axes[0].set_title('Residuals vs. MAN')\n",
    "\n",
    "# Plot for 'TYP' variable\n",
    "axes[1].scatter(X['TYP'], residuals, alpha=0.5)\n",
    "axes[1].set_xlabel('TYP')\n",
    "axes[1].set_ylabel('Residuals')\n",
    "axes[1].axhline(y=0, color='red', linestyle='--', linewidth=2)\n",
    "axes[1].set_title('Residuals vs. TYP')\n",
    "\n",
    "# Plot for 'INCOME' variable\n",
    "axes[2].scatter(X['INCOME'], residuals, alpha=0.5)\n",
    "axes[2].set_xlabel('INCOME')\n",
    "axes[2].set_ylabel('Residuals')\n",
    "axes[2].axhline(y=0, color='red', linestyle='--', linewidth=2)\n",
    "axes[2].set_title('Residuals vs. INCOME')\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plots\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "05d0ded4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted revenue for Site A: 12.0974\n",
      "Predicted revenue for Site B: 8.620399999999998\n",
      "Recommendation: Site A is recommended.\n"
     ]
    }
   ],
   "source": [
    "# Define the coefficients from the last regression model\n",
    "const_coeff = 4.3971\n",
    "man_coeff = 1.5989\n",
    "typ_coeff = 1.0901\n",
    "income_coeff = -0.4365\n",
    "\n",
    "# Define the characteristics of Site A\n",
    "site_a_values = {\n",
    "    'const': 1,\n",
    "    'MAN': 7,\n",
    "    'TYP': 0,\n",
    "    'INCOME': 8.0  # In £000s\n",
    "}\n",
    "\n",
    "# Define the characteristics of Site B\n",
    "site_b_values = {\n",
    "    'const': 1,\n",
    "    'MAN': 6,\n",
    "    'TYP': 1,\n",
    "    'INCOME': 14.8  # In £000s\n",
    "}\n",
    "\n",
    "# Calculate the predicted revenues for Site A and Site B\n",
    "predicted_rev_site_a = const_coeff * site_a_values['const'] + \\\n",
    "                        man_coeff * site_a_values['MAN'] + \\\n",
    "                        typ_coeff * site_a_values['TYP'] + \\\n",
    "                        income_coeff * site_a_values['INCOME']\n",
    "\n",
    "predicted_rev_site_b = const_coeff * site_b_values['const'] + \\\n",
    "                        man_coeff * site_b_values['MAN'] + \\\n",
    "                        typ_coeff * site_b_values['TYP'] + \\\n",
    "                        income_coeff * site_b_values['INCOME']\n",
    "\n",
    "print(\"Predicted revenue for Site A:\", predicted_rev_site_a)\n",
    "print(\"Predicted revenue for Site B:\", predicted_rev_site_b)\n",
    "\n",
    "# Compare the predicted revenues to make a recommendation\n",
    "if predicted_rev_site_a > predicted_rev_site_b:\n",
    "    print(\"Recommendation: Site A is recommended.\")\n",
    "elif predicted_rev_site_b > predicted_rev_site_a:\n",
    "    print(\"Recommendation: Site B is recommended.\")\n",
    "else:\n",
    "    print(\"Both sites are predicted to have the same revenue.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b39fca04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Actual vs. Predicted Revenue')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import pandas as pd\n",
    "\n",
    "# Load the data from the Excel file\n",
    "df = pd.read_excel('Companions.xlsx')\n",
    "\n",
    "# Define the independent variables (predictors) without 'STA' and 'COM'\n",
    "X = df[['MAN','TYP', 'INCOME']]\n",
    "\n",
    "# Add a constant term to the model (intercept)\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "# Create a quadratic term for the 'INCOME' variable\n",
    "X['INCOME_SQ'] = df['INCOME'] ** 2\n",
    "\n",
    "# Define the dependent variable (target)\n",
    "y = df['REV']\n",
    "\n",
    "# Fit the multiple linear regression model\n",
    "model = sm.OLS(y, X).fit()\n",
    "\n",
    "# Get the predicted values from the model\n",
    "predicted_values = model.predict(X)\n",
    "\n",
    "# Create a scatter plot of the data\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(y, predicted_values, alpha=0.5)\n",
    "plt.xlabel('Actual Revenue')\n",
    "plt.ylabel('Predicted Revenue')\n",
    "plt.title('Actual vs. Predicted Revenue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cd9797bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    REV   R-squared:                       0.988\n",
      "Model:                            OLS   Adj. R-squared:                  0.987\n",
      "Method:                 Least Squares   F-statistic:                     893.6\n",
      "Date:                Sat, 07 Oct 2023   Prob (F-statistic):           5.03e-40\n",
      "Time:                        13:19:18   Log-Likelihood:                -26.053\n",
      "No. Observations:                  47   AIC:                             62.11\n",
      "Df Residuals:                      42   BIC:                             71.36\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         26.0577      1.906     13.668      0.000      22.210      29.905\n",
      "MAN            1.5126      0.030     50.386      0.000       1.452       1.573\n",
      "TYP            1.1021      0.137      8.021      0.000       0.825       1.379\n",
      "INCOME        -4.3543      0.336    -12.945      0.000      -5.033      -3.675\n",
      "INCOME_SQ      0.1755      0.015     11.717      0.000       0.145       0.206\n",
      "==============================================================================\n",
      "Omnibus:                        2.560   Durbin-Watson:                   2.441\n",
      "Prob(Omnibus):                  0.278   Jarque-Bera (JB):                1.757\n",
      "Skew:                          -0.122   Prob(JB):                        0.415\n",
      "Kurtosis:                       3.915   Cond. No.                     3.73e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 3.73e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "# Print the new regression summary\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4601376a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the residuals\n",
    "residuals = y - predicted_values\n",
    "\n",
    "# Create a scatter plot of the residuals vs. the predicted values\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(predicted_values, residuals, alpha=0.5)\n",
    "plt.xlabel('Predicted Values')\n",
    "plt.ylabel('Residuals')\n",
    "plt.title('Residual Plot')\n",
    "plt.axhline(y=0, color='r', linestyle='--')  # Add a horizontal line at y=0\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Create a histogram of the residuals to check for normality\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.hist(residuals, bins=20, alpha=0.5)\n",
    "plt.xlabel('Residuals')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Residuals')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Create a Q-Q plot to check for normality\n",
    "sm.qqplot(residuals, line='s')\n",
    "plt.title('Q-Q Plot of Residuals')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7a087661",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the residuals\n",
    "residuals = y - predicted_values\n",
    "\n",
    "# Create a scatter plot of residuals vs. 'MAN' variable\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(X['MAN'], residuals, alpha=0.5)\n",
    "plt.xlabel('MAN')\n",
    "plt.ylabel('Residuals')\n",
    "plt.title('Residual Plot for MAN')\n",
    "plt.axhline(y=0, color='r', linestyle='--')  # Add a horizontal line at y=0\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Create a scatter plot of residuals vs. 'TYP' variable\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(X['TYP'], residuals, alpha=0.5)\n",
    "plt.xlabel('TYP')\n",
    "plt.ylabel('Residuals')\n",
    "plt.title('Residual Plot for TYP')\n",
    "plt.axhline(y=0, color='r', linestyle='--')  # Add a horizontal line at y=0\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Create a scatter plot of residuals vs. 'INCOME' variable\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(X['INCOME'], residuals, alpha=0.5)\n",
    "plt.xlabel('INCOME')\n",
    "plt.ylabel('Residuals')\n",
    "plt.title('Residual Plot for INCOME')\n",
    "plt.axhline(y=0, color='r', linestyle='--')  # Add a horizontal line at y=0\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Create a scatter plot of residuals vs. 'INCOME_SQ' variable\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(X['INCOME_SQ'], residuals, alpha=0.5)\n",
    "plt.xlabel('INCOME_SQ')\n",
    "plt.ylabel('Residuals')\n",
    "plt.title('Residual Plot for INCOME_SQ')\n",
    "plt.axhline(y=0, color='r', linestyle='--')  # Add a horizontal line at y=0\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9b3fd06b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 0.9884\n",
      "Proportion of Variance Explained by Each Variable:\n",
      "MAN: 0.7604\n",
      "TYP: 0.0190\n",
      "INCOME: 0.0499\n",
      "INCOME_SQ: 0.0408\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import pandas as pd\n",
    "\n",
    "# Load the data from the Excel file\n",
    "df = pd.read_excel('Companions.xlsx')\n",
    "\n",
    "# Define the independent variables (predictors) without 'STA' and 'COM'\n",
    "X = df[['MAN','TYP', 'INCOME']]\n",
    "\n",
    "# Add a constant term to the model (intercept)\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "# Create a quadratic term for the 'INCOME' variable\n",
    "X['INCOME_SQ'] = df['INCOME'] ** 2\n",
    "\n",
    "# Define the dependent variable (target)\n",
    "y = df['REV']\n",
    "\n",
    "# Fit the multiple linear regression model\n",
    "model = sm.OLS(y, X).fit()\n",
    "\n",
    "# Get the predicted values from the model\n",
    "predicted_values = model.predict(X)\n",
    "\n",
    "# Calculate R-squared value\n",
    "r_squared = model.rsquared\n",
    "\n",
    "# Create a list to store the proportion of variance explained for each variable\n",
    "proportion_variance_explained = []\n",
    "\n",
    "# Iterate through the independent variables\n",
    "for var in X.columns:\n",
    "    if var != 'const':\n",
    "        # Fit the model without the current variable\n",
    "        model_without_var = sm.OLS(y, X.drop(var, axis=1)).fit()\n",
    "        # Calculate the proportion of variance explained by excluding the variable\n",
    "        proportion_explained = (model.rsquared_adj - model_without_var.rsquared_adj) / model.rsquared_adj\n",
    "        proportion_variance_explained.append((var, proportion_explained))\n",
    "\n",
    "# Print the R-squared value, the proportion of variance explained by each variable, and the percentage of variance explained\n",
    "print(f'R-squared: {r_squared:.4f}')\n",
    "print(f'Proportion of Variance Explained by Each Variable:')\n",
    "for var, proportion in proportion_variance_explained:\n",
    "    print(f'{var}: {proportion:.4f}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d3fc9731",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import pandas as pd\n",
    "\n",
    "# Load the data from the Excel file\n",
    "df = pd.read_excel('Companions.xlsx')\n",
    "\n",
    "# Define the independent variables (predictors) without 'STA' and 'COM'\n",
    "X = df[['MAN', 'TYP', 'INCOME']]\n",
    "\n",
    "# Add a constant term to the model (intercept)\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "# Create a quadratic term for the 'INCOME' variable\n",
    "X['INCOME_SQ'] = df['INCOME'] ** 2\n",
    "\n",
    "# Define the dependent variable (target)\n",
    "y = df['REV']\n",
    "\n",
    "# Fit the multiple linear regression model\n",
    "model = sm.OLS(y, X).fit()\n",
    "\n",
    "# Calculate R-squared value\n",
    "r_squared = model.rsquared\n",
    "\n",
    "# Create a list to store the proportion of variance explained for each variable\n",
    "proportion_variance_explained = []\n",
    "\n",
    "# Iterate through the independent variables\n",
    "for var in X.columns:\n",
    "    if var != 'const':\n",
    "        # Fit the model without the current variable\n",
    "        model_without_var = sm.OLS(y, X.drop(var, axis=1)).fit()\n",
    "        # Calculate the proportion of variance explained by excluding the variable\n",
    "        proportion_explained = (model.rsquared_adj - model_without_var.rsquared_adj) / model.rsquared_adj\n",
    "        proportion_variance_explained.append((var, proportion_explained))\n",
    "\n",
    "# Extract variable names and proportions\n",
    "variables, proportions = zip(*proportion_variance_explained)\n",
    "\n",
    "# Create a smaller bar chart\n",
    "plt.figure(figsize=(8, 4))  # Adjust the figure size here\n",
    "plt.barh(variables, proportions, color='skyblue')\n",
    "plt.xlabel('Proportion of Variance Explained')\n",
    "plt.title('Proportion of Variance Explained by Each Variable')\n",
    "plt.gca().invert_yaxis()\n",
    "\n",
    "# Customize x-axis ticks and labels to display percentages\n",
    "plt.xticks(np.arange(0, 1.1, 0.1), ['0%', '10%', '20%', '30%', '40%', '50%', '60%', '70%', '80%', '90%', '100%'])\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "1f8e86d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Coefficients from your regression model\n",
    "coefficients = {\n",
    "    'const': 26.0577,\n",
    "    'MAN': 1.5126,\n",
    "    'TYP': 1.1021,\n",
    "    'INCOME': -4.3543,\n",
    "    'INCOME_SQ': 0.1755\n",
    "}\n",
    "\n",
    "# Site characteristics\n",
    "site_a = {\n",
    "    'Site': 'A',\n",
    "    'MAN': 4,\n",
    "    'TYP': 0,  # Conventional type\n",
    "    'INCOME': 8,\n",
    "    'INCOME_SQ': 8**2\n",
    "}\n",
    "\n",
    "site_b = {\n",
    "    'Site': 'B',\n",
    "    'MAN': 1,\n",
    "    'TYP': 1,  # Modern or unusual theme/ambience\n",
    "    'INCOME': 14.8,\n",
    "    'INCOME_SQ': 14.8**2\n",
    "}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "4b812542",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table:\n",
      "  Site  Predicted Revenue\n",
      "0    A            8.50570\n",
      "1    B            2.67028\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate Predicted Revenue for Sites A and B\n",
    "predicted_revenue_a = (\n",
    "    coefficients['const'] +\n",
    "    coefficients['MAN'] * site_a['MAN'] +\n",
    "    coefficients['TYP'] * site_a['TYP'] +\n",
    "    coefficients['INCOME'] * site_a['INCOME'] +\n",
    "    coefficients['INCOME_SQ'] * site_a['INCOME_SQ']\n",
    ")\n",
    "\n",
    "predicted_revenue_b = (\n",
    "    coefficients['const'] +\n",
    "    coefficients['MAN'] * site_b['MAN'] +\n",
    "    coefficients['TYP'] * site_b['TYP'] +\n",
    "    coefficients['INCOME'] * site_b['INCOME'] +\n",
    "    coefficients['INCOME_SQ'] * site_b['INCOME_SQ']\n",
    ")\n",
    "\n",
    "# Create a DataFrame for the results\n",
    "data = {\n",
    "    'Site': [site_a['Site'], site_b['Site']],\n",
    "    'Predicted Revenue': [predicted_revenue_a, predicted_revenue_b]\n",
    "}\n",
    "\n",
    "result_df = pd.DataFrame(data)\n",
    "\n",
    "# Print the table with non-rounded numbers\n",
    "print(\"Table:\")\n",
    "print(result_df)\n",
    "\n",
    "# Plot Predicted Revenue without rounding on the y-axis\n",
    "plt.bar(result_df['Site'], result_df['Predicted Revenue'])\n",
    "plt.xlabel('Site')\n",
    "plt.ylabel('Predicted Revenue')\n",
    "plt.title('Predicted Revenue for Sites A and B')\n",
    "\n",
    "# Display non-rounded numbers on the y-axis\n",
    "plt.gca().get_yaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:,.2f}\".format(x)))\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "10871613",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table:\n",
      "  Site  Predicted Revenue\n",
      "0    A            8.50570\n",
      "1    B            2.67028\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Coefficients from your regression model\n",
    "coefficients = {\n",
    "    'const': 26.0577,\n",
    "    'MAN': 1.5126,\n",
    "    'TYP': 1.1021,\n",
    "    'INCOME': -4.3543,\n",
    "    'INCOME_SQ': 0.1755\n",
    "}\n",
    "\n",
    "# Site characteristics\n",
    "site_a = {\n",
    "    'Site': 'A',\n",
    "    'MAN': 4,\n",
    "    'TYP': 0,  # Conventional type\n",
    "    'INCOME': 8,\n",
    "    'INCOME_SQ': 8**2\n",
    "}\n",
    "\n",
    "site_b = {\n",
    "    'Site': 'B',\n",
    "    'MAN': 1,\n",
    "    'TYP': 1,  # Modern or unusual theme/ambience\n",
    "    'INCOME': 14.8,\n",
    "    'INCOME_SQ': 14.8**2\n",
    "}\n",
    "\n",
    "# Calculate Predicted Revenue for Sites A and B\n",
    "predicted_revenue_a = (\n",
    "    coefficients['const'] +\n",
    "    coefficients['MAN'] * site_a['MAN'] +\n",
    "    coefficients['TYP'] * site_a['TYP'] +\n",
    "    coefficients['INCOME'] * site_a['INCOME'] +\n",
    "    coefficients['INCOME_SQ'] * site_a['INCOME_SQ']\n",
    ")\n",
    "\n",
    "predicted_revenue_b = (\n",
    "    coefficients['const'] +\n",
    "    coefficients['MAN'] * site_b['MAN'] +\n",
    "    coefficients['TYP'] * site_b['TYP'] +\n",
    "    coefficients['INCOME'] * site_b['INCOME'] +\n",
    "    coefficients['INCOME_SQ'] * site_b['INCOME_SQ']\n",
    ")\n",
    "\n",
    "# Create a DataFrame for the results\n",
    "data = {\n",
    "    'Site': [site_a['Site'], site_b['Site']],\n",
    "    'Predicted Revenue': [predicted_revenue_a, predicted_revenue_b]\n",
    "}\n",
    "\n",
    "result_df = pd.DataFrame(data)\n",
    "\n",
    "# Print the table with non-rounded numbers\n",
    "print(\"Table:\")\n",
    "print(result_df)\n",
    "\n",
    "# Plot Predicted Revenue without rounding on the y-axis\n",
    "plt.bar(result_df['Site'], result_df['Predicted Revenue'])\n",
    "plt.xlabel('Site')\n",
    "plt.ylabel('Predicted Revenue')\n",
    "plt.title('Predicted Revenue for Sites A and B')\n",
    "\n",
    "# Display non-rounded numbers on the y-axis\n",
    "plt.gca().get_yaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:,.2f}\".format(x)))\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2504abd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction Equation:\n",
      "Predicted Revenue = 26.0577 + (1.5126 * MAN) + (1.1021 * TYP) + (-4.3543 * INCOME) + (0.1755 * INCOME^2)\n",
      "Predicted Revenue for Site A: 8.505699999999997\n",
      "Predicted Revenue for Site B: 2.670279999999998\n"
     ]
    }
   ],
   "source": [
    "# Prediction equation coefficients\n",
    "const_coeff = 26.0577\n",
    "man_coeff = 1.5126\n",
    "typ_coeff = 1.1021\n",
    "income_coeff = -4.3543\n",
    "income_sq_coeff = 0.1755\n",
    "\n",
    "# Site characteristics\n",
    "MAN_A = 4\n",
    "MAN_B = 1\n",
    "TYP_A = 0  # Conventional type\n",
    "TYP_B = 1  # Modern or unusual theme/ambience\n",
    "INCOME_A = 8  # Local per capita annual income for Site A in millions\n",
    "INCOME_B = 14.8  # Local per capita annual income for Site B in millions\n",
    "INCOME_SQ_A = INCOME_A ** 2\n",
    "INCOME_SQ_B = INCOME_B ** 2\n",
    "\n",
    "# Calculate Predicted Revenue for Site A and Site B\n",
    "predicted_revenue_A = (\n",
    "    const_coeff +\n",
    "    man_coeff * MAN_A +\n",
    "    typ_coeff * TYP_A +\n",
    "    income_coeff * INCOME_A +\n",
    "    income_sq_coeff * INCOME_SQ_A\n",
    ")\n",
    "\n",
    "predicted_revenue_B = (\n",
    "    const_coeff +\n",
    "    man_coeff * MAN_B +\n",
    "    typ_coeff * TYP_B +\n",
    "    income_coeff * INCOME_B +\n",
    "    income_sq_coeff * INCOME_SQ_B\n",
    ")\n",
    "\n",
    "# Display the prediction equation\n",
    "print(\"Prediction Equation:\")\n",
    "print(f\"Predicted Revenue = {const_coeff} + ({man_coeff} * MAN) + ({typ_coeff} * TYP) + ({income_coeff} * INCOME) + ({income_sq_coeff} * INCOME^2)\")\n",
    "\n",
    "# Print the predicted revenues\n",
    "print(\"Predicted Revenue for Site A:\", predicted_revenue_A)\n",
    "print(\"Predicted Revenue for Site B:\", predicted_revenue_B)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "126588ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction Equation:\n",
      "Predicted Revenue for Site A: 8.505699999999997\n",
      "95.0% Confidence Interval for Site A: (4.200367813052064, 12.81103218694793)\n",
      "Predicted Revenue for Site B: 2.670279999999998\n",
      "95.0% Confidence Interval for Site B: (-2.007426946893294, 7.347986946893291)\n"
     ]
    }
   ],
   "source": [
    "# Standard errors of the coefficients (taken from your regression output)\n",
    "se_const = 1.906\n",
    "se_man = 0.030\n",
    "se_typ = 0.137\n",
    "se_income = 0.336\n",
    "se_income_sq = 0.015\n",
    "\n",
    "# Confidence level (e.g., 95% confidence interval)\n",
    "confidence_level = 0.95\n",
    "\n",
    "# Calculate the critical value for the confidence interval\n",
    "from scipy.stats import t\n",
    "degrees_of_freedom = len(df) - len(coefficients)  # Adjust for the number of coefficients\n",
    "alpha = 1 - confidence_level\n",
    "t_critical = t.ppf(1 - alpha / 2, degrees_of_freedom)\n",
    "\n",
    "# Calculate the margin of error for the predictions\n",
    "margin_of_error_A = (\n",
    "    t_critical *\n",
    "    ((se_const ** 2) +\n",
    "    (se_man ** 2) * MAN +\n",
    "    (se_typ ** 2) * TYP_A +\n",
    "    (se_income ** 2) * INCOME_A +\n",
    "    (se_income_sq ** 2) * INCOME_SQ_A) ** 0.5\n",
    ")\n",
    "\n",
    "margin_of_error_B = (\n",
    "    t_critical *\n",
    "    ((se_const ** 2) +\n",
    "    (se_man ** 2) * MAN +\n",
    "    (se_typ ** 2) * TYP_B +\n",
    "    (se_income ** 2) * INCOME_B +\n",
    "    (se_income_sq ** 2) * INCOME_SQ_B) ** 0.5\n",
    ")\n",
    "\n",
    "# Calculate the confidence intervals for the predictions\n",
    "confidence_interval_A = (predicted_revenue_A - margin_of_error_A, predicted_revenue_A + margin_of_error_A)\n",
    "confidence_interval_B = (predicted_revenue_B - margin_of_error_B, predicted_revenue_B + margin_of_error_B)\n",
    "\n",
    "# Display the prediction equation\n",
    "print(\"Prediction Equation:\")\n",
    "# (Include coefficients as needed)\n",
    "\n",
    "# Print the predicted revenues with confidence intervals\n",
    "print(\"Predicted Revenue for Site A:\", predicted_revenue_A)\n",
    "print(f\"{confidence_level*100}% Confidence Interval for Site A:\", confidence_interval_A)\n",
    "print(\"Predicted Revenue for Site B:\", predicted_revenue_B)\n",
    "print(f\"{confidence_level*100}% Confidence Interval for Site B:\", confidence_interval_B)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7c6ad0e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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