{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "33171a5a-e795-4748-95d8-49e1a9f80e83",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import random\n",
    "import scipy\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import time\n",
    "import statistics\n",
    "from scipy import stats\n",
    "from datetime import datetime, date, timedelta\n",
    "from statsmodels.tsa.holtwinters import ExponentialSmoothing, SimpleExpSmoothing, Holt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "pd.options.display.float_format = '{:.2f}'.format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8323abae-b512-4275-bbfc-fca161d2a423",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We reach that point here ...\n",
      "And here...\n",
      "Here\n",
      "(65, 3)\n"
     ]
    }
   ],
   "source": [
    "import snowflake.connector\n",
    "import os\n",
    "from cryptography.hazmat.backends import default_backend\n",
    "from cryptography.hazmat.primitives.asymmetric import rsa\n",
    "from cryptography.hazmat.primitives.asymmetric import dsa\n",
    "from cryptography.hazmat.primitives import serialization\n",
    "\n",
    "from password import PRIVATE_KEY_PASSPHRASE\n",
    "\n",
    "with open(\"/Users/impr001/Keys_snowflake/rsa_key.p8\", \"rb\") as key: \n",
    "    p_key= serialization.load_pem_private_key(\n",
    "        key.read(),\n",
    "        password=PRIVATE_KEY_PASSPHRASE.encode(),\n",
    "        backend=default_backend()\n",
    "    )\n",
    "\n",
    "pkb = p_key.private_bytes(\n",
    "    encoding=serialization.Encoding.DER,\n",
    "    format=serialization.PrivateFormat.PKCS8,\n",
    "    encryption_algorithm=serialization.NoEncryption())\n",
    "\n",
    "print(\"We reach that point here ...\")\n",
    "ctx = snowflake.connector.connect(\n",
    "    user='eimpara@theorchard.com', \n",
    "    account='orchard',\n",
    "    private_key=pkb,\n",
    "    role= 'PROD_DATALYTICS_ROLE',\n",
    "    warehouse = \"DEV_ANALYTICS_ORCHARD\"\n",
    "    )\n",
    "print(\"And here...\")\n",
    "\n",
    "try:\n",
    "    cs = ctx.cursor()\n",
    "    print(\"Here\")\n",
    "    sql = \"\"\"\n",
    "    select * from intelligence.dev_zallard.aventura_spotify_straming_2024_to_date_weekly;\n",
    "    \"\"\"\n",
    "    cs.execute(sql)\n",
    "    original = cs.fetch_pandas_all()\n",
    "    \n",
    "    print(original.shape)\n",
    "\n",
    "finally:\n",
    "    cs.close()\n",
    "\n",
    "ctx.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6cfeba58-8f5b-4cac-9656-ecc4c651e1eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 65 entries, 0 to 64\n",
      "Data columns (total 3 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   ACTIVITY_WEEK  65 non-null     object\n",
      " 1   COUNTRY_NAME   65 non-null     object\n",
      " 2   STREAMS        65 non-null     int32 \n",
      "dtypes: int32(1), object(2)\n",
      "memory usage: 1.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df = original.copy()\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b92215c6-109f-45fc-b47b-068ff48ded12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(65, 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop_duplicates()\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "52f5ae05-b8c2-4b08-bd42-e3b51d233256",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ACTIVITY_WEEK</th>\n",
       "      <th>COUNTRY_NAME</th>\n",
       "      <th>STREAMS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-08-11</td>\n",
       "      <td>USA</td>\n",
       "      <td>5393662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-02-02</td>\n",
       "      <td>USA</td>\n",
       "      <td>6517999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-05-03</td>\n",
       "      <td>USA</td>\n",
       "      <td>9616806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-06-30</td>\n",
       "      <td>USA</td>\n",
       "      <td>5021785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-09-22</td>\n",
       "      <td>USA</td>\n",
       "      <td>5883383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ACTIVITY_WEEK COUNTRY_NAME  STREAMS\n",
       "0    2023-08-11          USA  5393662\n",
       "1    2024-02-02          USA  6517999\n",
       "2    2024-05-03          USA  9616806\n",
       "3    2023-06-30          USA  5021785\n",
       "4    2023-09-22          USA  5883383"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "40202088-6f8e-4375-b9c9-a7a590ba2322",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['MONTH'] = pd.to_datetime(df['ACTIVITY_WEEK']).dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a72a18d8-1074-4826-b43d-cf5749047a02",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ACTIVITY_WEEK'] = pd.to_datetime(df['ACTIVITY_WEEK'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d567b49d-ab04-4af8-8c57-cec1a738ccda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-05-26 00:00:00\n",
      "2024-08-16 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print(df['ACTIVITY_WEEK'].min())\n",
    "print(df['ACTIVITY_WEEK'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4193e147-54e8-4803-baac-bf2fdd3bd6f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.sort_values(by='ACTIVITY_WEEK', ascending=True).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "08ae79cb-eb99-49f6-85b9-a4d7afd1bef1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['time'] = df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b16e3a8d-9a27-40a3-8b57-120d2c84cf99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>ACTIVITY_WEEK</th>\n",
       "      <th>COUNTRY_NAME</th>\n",
       "      <th>STREAMS</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61</td>\n",
       "      <td>2023-06-02</td>\n",
       "      <td>USA</td>\n",
       "      <td>5645568</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31</td>\n",
       "      <td>2023-06-09</td>\n",
       "      <td>USA</td>\n",
       "      <td>5541280</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>2023-06-16</td>\n",
       "      <td>USA</td>\n",
       "      <td>5508941</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23</td>\n",
       "      <td>2023-06-23</td>\n",
       "      <td>USA</td>\n",
       "      <td>5324989</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>2023-06-30</td>\n",
       "      <td>USA</td>\n",
       "      <td>5021785</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>54</td>\n",
       "      <td>2024-07-19</td>\n",
       "      <td>USA</td>\n",
       "      <td>7942196</td>\n",
       "      <td>7</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>62</td>\n",
       "      <td>2024-07-26</td>\n",
       "      <td>USA</td>\n",
       "      <td>7703134</td>\n",
       "      <td>7</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>46</td>\n",
       "      <td>2024-08-02</td>\n",
       "      <td>USA</td>\n",
       "      <td>6970554</td>\n",
       "      <td>8</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>27</td>\n",
       "      <td>2024-08-09</td>\n",
       "      <td>USA</td>\n",
       "      <td>6971119</td>\n",
       "      <td>8</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>57</td>\n",
       "      <td>2024-08-16</td>\n",
       "      <td>USA</td>\n",
       "      <td>4214001</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    index ACTIVITY_WEEK COUNTRY_NAME  STREAMS  MONTH  time\n",
       "1      61    2023-06-02          USA  5645568      6     1\n",
       "2      31    2023-06-09          USA  5541280      6     2\n",
       "3      37    2023-06-16          USA  5508941      6     3\n",
       "4      23    2023-06-23          USA  5324989      6     4\n",
       "5       3    2023-06-30          USA  5021785      6     5\n",
       "..    ...           ...          ...      ...    ...   ...\n",
       "60     54    2024-07-19          USA  7942196      7    60\n",
       "61     62    2024-07-26          USA  7703134      7    61\n",
       "62     46    2024-08-02          USA  6970554      8    62\n",
       "63     27    2024-08-09          USA  6971119      8    63\n",
       "64     57    2024-08-16          USA  4214001      8    64\n",
       "\n",
       "[64 rows x 6 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.iloc[1:].copy()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb5836dd-d4c1-4870-b9aa-b48a87c2531d",
   "metadata": {},
   "source": [
    "# Discontinuity Design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3854b8d2-6096-4d7c-8bf2-458d2b75fea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cut_off'] = (df['time']  - 49)\n",
    "\n",
    "df['d'] = np.where(df['ACTIVITY_WEEK']>= '2024-05-02', 1, 0)\n",
    "\n",
    "df['tickets_on_sale'] = np.where(df['ACTIVITY_WEEK']=='2024-03-01', 1, 0)\n",
    "\n",
    "df['intercept']=1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ab6b5133-ba57-4c69-afd0-3ef9e2e62062",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pre = df.iloc[:49]\n",
    "\n",
    "X = pre[['time']]\n",
    "y = pre['STREAMS']\n",
    "\n",
    "trend = LinearRegression().fit(X, y)\n",
    "\n",
    "extended_X = X.copy()  \n",
    "for i in range(len(pre), len(df)):\n",
    "    extended_X.loc[i] = [i] \n",
    "\n",
    "pred = trend.predict(extended_X)\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(30, 15))\n",
    "\n",
    "ax.plot(df['ACTIVITY_WEEK'], df['STREAMS'], label='Streams', linewidth=3)\n",
    "ax.plot(df['ACTIVITY_WEEK'].to_list(), pred, color='orange', linestyle='--', label='Counterfactual')  \n",
    "ax.plot(df['ACTIVITY_WEEK'][:49].to_list(), pred[:49], color='red', linestyle='-', label='Linear trend pre campaigns')  \n",
    "\n",
    "interventions = pd.to_datetime(['2024-03-01', '2024-05-02', '2024-06-01', '2024-07-18', '2024-08-01'])\n",
    "tickets = interventions[0]\n",
    "phase1 = interventions[1]\n",
    "phase2 = interventions[2]\n",
    "phase3 = interventions[3]\n",
    "phase3a = interventions[4]\n",
    "\n",
    "# Plot intervention lines\n",
    "ax.axvline(tickets, color='purple', linestyle='--', label='Tickets go on sale')\n",
    "ax.axvline(phase1, color='green', linestyle='--', label='Phase 1 starts')\n",
    "ax.axvline(phase2, color='green', linestyle='--', label='Phase 2 starts')\n",
    "ax.axvline(phase3, color='green', linestyle='--', label='Phase 3a starts')\n",
    "ax.axvline(phase3a, color='green', linestyle='--', label='Phase 3b starts')\n",
    "plt.ylabel('Streams')\n",
    "plt.title('Distribution of Spotify WEEKLY Streams (US)', fontsize=20)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bed7b929-6c88-4e3a-93bd-3140e5ef6762",
   "metadata": {},
   "source": [
    "# OLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6569ef7e-3c12-4812-ba69-ad44c80fc0a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>STREAMS</td>     <th>  R-squared:         </th> <td>   0.829</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.814</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   56.13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 22 Aug 2024</td> <th>  Prob (F-statistic):</th> <td>5.71e-21</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>09:20:04</td>     <th>  Log-Likelihood:    </th> <td> -947.86</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    64</td>      <th>  AIC:               </th> <td>   1908.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    58</td>      <th>  BIC:               </th> <td>   1921.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th>       <td> 7.985e+06</td> <td> 2.41e+05</td> <td>   33.070</td> <td> 0.000</td> <td>  7.5e+06</td> <td> 8.47e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d</th>               <td> 2.358e+06</td> <td> 3.88e+05</td> <td>    6.078</td> <td> 0.000</td> <td> 1.58e+06</td> <td> 3.13e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cut_off</th>         <td> 5.818e+04</td> <td> 8042.636</td> <td>    7.234</td> <td> 0.000</td> <td> 4.21e+04</td> <td> 7.43e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>d:cut_off</th>       <td> -3.09e+05</td> <td> 3.94e+04</td> <td>   -7.835</td> <td> 0.000</td> <td>-3.88e+05</td> <td> -2.3e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tickets_on_sale</th> <td> 4.583e+06</td> <td> 7.07e+05</td> <td>    6.483</td> <td> 0.000</td> <td> 3.17e+06</td> <td>    6e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MONTH</th>           <td>-4.922e+04</td> <td> 3.12e+04</td> <td>   -1.579</td> <td> 0.120</td> <td>-1.12e+05</td> <td> 1.32e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.758</td> <th>  Durbin-Watson:     </th> <td>   0.860</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.034</td> <th>  Jarque-Bera (JB):  </th> <td>  11.305</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.131</td> <th>  Prob(JB):          </th> <td> 0.00351</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.042</td> <th>  Cond. No.          </th> <td>    210.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                STREAMS   R-squared:                       0.829\n",
       "Model:                            OLS   Adj. R-squared:                  0.814\n",
       "Method:                 Least Squares   F-statistic:                     56.13\n",
       "Date:                Thu, 22 Aug 2024   Prob (F-statistic):           5.71e-21\n",
       "Time:                        09:20:04   Log-Likelihood:                -947.86\n",
       "No. Observations:                  64   AIC:                             1908.\n",
       "Df Residuals:                      58   BIC:                             1921.\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------\n",
       "intercept        7.985e+06   2.41e+05     33.070      0.000     7.5e+06    8.47e+06\n",
       "d                2.358e+06   3.88e+05      6.078      0.000    1.58e+06    3.13e+06\n",
       "cut_off          5.818e+04   8042.636      7.234      0.000    4.21e+04    7.43e+04\n",
       "d:cut_off        -3.09e+05   3.94e+04     -7.835      0.000   -3.88e+05    -2.3e+05\n",
       "tickets_on_sale  4.583e+06   7.07e+05      6.483      0.000    3.17e+06       6e+06\n",
       "MONTH           -4.922e+04   3.12e+04     -1.579      0.120   -1.12e+05    1.32e+04\n",
       "==============================================================================\n",
       "Omnibus:                        6.758   Durbin-Watson:                   0.860\n",
       "Prob(Omnibus):                  0.034   Jarque-Bera (JB):               11.305\n",
       "Skew:                          -0.131   Prob(JB):                      0.00351\n",
       "Kurtosis:                       5.042   Cond. No.                         210.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['d:cut_off'] = df['d'] * df['cut_off']\n",
    "\n",
    "predictors0 = ['intercept', 'd', 'cut_off','d:cut_off', 'tickets_on_sale', 'MONTH']\n",
    "\n",
    "model_ols= sm.OLS(df['STREAMS'], df[predictors0]).fit()\n",
    "model_ols.summary()"
   ]
  }
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