{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to mathematical statistics \n",
    "\n",
    "Welcome to the lecture 8 in 02403\n",
    "\n",
    "During the lectures we will present both slides and notebooks. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example: Skive fjord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     year  month     TN      TP     chla    din     dip    prim  KrIltSv  \\\n",
      "1    1982      1  1.312  0.0887  0.00396  0.415  0.0775  0.0189        0   \n",
      "2    1982      2  1.371  0.0737  0.00568  0.486  0.0427  0.0895        0   \n",
      "3    1982      3  1.500  0.0640  0.00200  0.865  0.0245  0.1640        0   \n",
      "4    1982      4  1.200  0.0450  0.01300  0.588  0.0045  0.7290        0   \n",
      "5    1982      5  0.772  0.0862  0.04325  0.072  0.0098  3.4730        2   \n",
      "..    ...    ...    ...     ...      ...    ...     ...     ...      ...   \n",
      "296  2006      8  0.975  0.3410  0.02043  0.361  0.2480  2.7933        9   \n",
      "297  2006      9  1.018  0.2362  0.02934  0.293  0.1444  3.8355        2   \n",
      "298  2006     10  0.661  0.1375  0.01579  0.198  0.0772  1.0872        5   \n",
      "299  2006     11  0.833  0.0583  0.00226  0.480  0.0307  0.0904        0   \n",
      "300  2006     12  0.820  0.0703  0.00172  0.590  0.0253  0.0409        0   \n",
      "\n",
      "     IltSv  IIltSv   N.load  P.load  Q.load   temp      gr  vmp  \n",
      "1        0       0  236.578     NaN     NaN   2.23   18.00    0  \n",
      "2        0       0  219.673     NaN     NaN   2.12   42.32    0  \n",
      "3        0       2  222.722     NaN     NaN   3.50   91.96    0  \n",
      "4        0       2  175.406     NaN     NaN   5.95  152.74    0  \n",
      "5        0       2  146.009     NaN     NaN  12.36  221.35    0  \n",
      "..     ...     ...      ...     ...     ...    ...     ...  ...  \n",
      "296      0       3   51.537     NaN     NaN  20.13  174.18    3  \n",
      "297      2       6   58.042     NaN     NaN  16.97  129.28    3  \n",
      "298      2       2   76.244     NaN     NaN  14.47   61.05    3  \n",
      "299      0       6   87.738     NaN     NaN   8.28   25.99    3  \n",
      "300      0       5  103.756     NaN     NaN   6.67   12.52    3  \n",
      "\n",
      "[300 rows x 17 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f081bd39c30>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SkiveAvg = pd.read_csv(\"../week1/skiveAvg.csv\", sep=';')\n",
    "y = np.array(np.log(SkiveAvg[\"chla\"]))\n",
    "x = np.array(SkiveAvg[\"temp\"])\n",
    "print(SkiveAvg)\n",
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parameter estimates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.82521379,  0.09495466])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sxx = np.sum((x - np.mean(x)) ** 2)\n",
    "beta1 = np.sum((y - np.mean(y)) * (x - np.mean(x))) / Sxx\n",
    "beta0 = np.mean(y) - beta1 * np.mean(x)\n",
    "np.array([beta0, beta1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f081bda6ec0>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)\n",
    "temp_plot = np.arange(-2,22,0.1)\n",
    "plt.plot(temp_plot,beta0 + beta1 * temp_plot, color = \"red\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standard errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-5.82521379,  0.08811298],\n",
       "       [ 0.09495466,  0.00771234]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e = y-beta0- x*beta1\n",
    "n = len(e)\n",
    "sigma2 = sum(e ** 2) / (n-2)\n",
    "sigma = np.sqrt(sigma2)\n",
    "se_beta0 = sigma * np.sqrt(1/n + np.mean(x)**2/Sxx)\n",
    "se_beta1 = sigma * np.sqrt(1/Sxx)\n",
    "np.array([[beta0,se_beta0],[beta1,se_beta1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matrix formulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Estimates</th>\n",
       "      <th>Std.Error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>beta0</th>\n",
       "      <td>-5.825214</td>\n",
       "      <td>0.088113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta1</th>\n",
       "      <td>0.094955</td>\n",
       "      <td>0.007712</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Estimates  Std.Error\n",
       "beta0  -5.825214   0.088113\n",
       "beta1   0.094955   0.007712"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.array([np.repeat(1,n), x]).T\n",
    "beta = np.linalg.inv(X.T @ X) @ X.T @ y\n",
    "Sigma_beta = sigma2 * np.linalg.inv(X.T @ X)\n",
    "se = np.sqrt(Sigma_beta.diagonal())\n",
    "\n",
    "## Coefficient table\n",
    "coefTab = np.array([beta,se]).T\n",
    "col_names = [\"Estimates\",\"Std.Error\"]\n",
    "row_names = [\"beta0\",\"beta1\"]\n",
    "coefTab = pd.DataFrame(coefTab,columns = col_names, index = row_names)\n",
    "coefTab\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Include hypothesis tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Estimates</th>\n",
       "      <th>Std.Error</th>\n",
       "      <th>t_obs</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>beta0</th>\n",
       "      <td>-5.825214</td>\n",
       "      <td>0.088113</td>\n",
       "      <td>-66.110732</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>0.094955</td>\n",
       "      <td>0.007712</td>\n",
       "      <td>12.312034</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Estimates  Std.Error      t_obs  p-value\n",
       "beta0  -5.825214   0.088113 -66.110732      0.0\n",
       "beta    0.094955   0.007712  12.312034      0.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_obs = beta/se\n",
    "pv = 2*(1-stats.t.cdf(np.abs(t_obs),df=n-1))\n",
    "## Coefficient table\n",
    "coefTab = np.array([beta,se,t_obs,pv]).T\n",
    "col_names = [\"Estimates\",\"Std.Error\",\"t_obs\",\"p-value\"]\n",
    "row_names = [\"beta0\",\"beta\"]\n",
    "coefTab = pd.DataFrame(coefTab,columns = col_names, index = row_names)\n",
    "coefTab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some confidence intervals "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Estimates</th>\n",
       "      <th>Std.Error</th>\n",
       "      <th>t_obs</th>\n",
       "      <th>p-value</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>beta0</th>\n",
       "      <td>-5.825214</td>\n",
       "      <td>0.088113</td>\n",
       "      <td>-66.110732</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.998616</td>\n",
       "      <td>-5.651811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>0.094955</td>\n",
       "      <td>0.007712</td>\n",
       "      <td>12.312034</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.079777</td>\n",
       "      <td>0.110132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Estimates  Std.Error      t_obs  p-value    ci_low   ci_high\n",
       "beta0  -5.825214   0.088113 -66.110732      0.0 -5.998616 -5.651811\n",
       "beta    0.094955   0.007712  12.312034      0.0  0.079777  0.110132"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tq = stats.t.ppf(0.975,df=n-2)\n",
    "ci_low = beta-se*tq\n",
    "ci_high = beta+se*tq\n",
    "\n",
    "## Coefficient table\n",
    "coefTab = np.array([beta,se,t_obs,pv,ci_low,ci_high]).T\n",
    "col_names = [\"Estimates\",\"Std.Error\",\"t_obs\",\"p-value\",\"ci_low\",\"ci_high\"]\n",
    "row_names = [\"beta0\",\"beta\"]\n",
    "coefTab = pd.DataFrame(coefTab,columns = col_names, index = row_names)\n",
    "coefTab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confidence and prediction interlals for the line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conf Int  [-4.97160769 -4.87566722 -4.77972675]\n",
      "Pred Int  [-6.53555203 -4.87566722 -3.21578241]\n"
     ]
    }
   ],
   "source": [
    "x0 = np.array([1,10])\n",
    "y_hat = x0 @ beta\n",
    "ME_c0 = tq * sigma * np.sqrt(x0 @ np.linalg.inv(X.T @ X) @ x0.T)\n",
    "ME_p0 = tq * sigma * np.sqrt(1+x0 @ np.linalg.inv(X.T @ X) @ x0.T)\n",
    "\n",
    "ci0_low = y_hat - ME_c0\n",
    "ci0_high = y_hat + ME_c0\n",
    "\n",
    "pi0_low = y_hat - ME_p0\n",
    "pi0_high = y_hat + ME_p0\n",
    "\n",
    "print(\"Conf Int \",np.array([ci0_low,y_hat,ci0_high]))\n",
    "print(\"Pred Int \",np.array([pi0_low,y_hat,pi0_high]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f081bf3b3d0>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x0 = np.arange(-5,25,0.1)\n",
    "X0 = np.array([np.repeat(1,len(x0)), x0]).T\n",
    "y_hat0 = X0 @ beta\n",
    "V = X0 @ np.linalg.inv(X.T @ X) @ X0.T\n",
    "y_hatLow_c = y_hat0 - tq * sigma * np.sqrt(V.diagonal())\n",
    "y_hatHigh_c = y_hat0 + tq * sigma * np.sqrt(V.diagonal())\n",
    "y_hatLow_p = y_hat0 - tq * sigma * np.sqrt(1+V.diagonal())\n",
    "y_hatHigh_p = y_hat0 + tq * sigma * np.sqrt(1+V.diagonal())\n",
    "\n",
    "\n",
    "plt.scatter(x,y,color=\"black\")\n",
    "plt.plot(x0,y_hat0,color=\"red\")\n",
    "plt.plot(x0,y_hatLow_c,color=\"green\")\n",
    "plt.plot(x0,y_hatHigh_c,color=\"green\")\n",
    "plt.plot(x0,y_hatLow_p,color=\"blue\")\n",
    "plt.plot(x0,y_hatHigh_p,color=\"blue\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 0.3371682543277057\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "np.float64(0.5806619105191122)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat = X @ beta\n",
    "Rsq = 1-np.sum((y-y_hat)**2) / np.sum((y-np.mean(y))**2)\n",
    "print(\"R^2\",Rsq)\n",
    "np.corrcoef(x,y)[0,1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                               OLS Regression Results                                                              \n",
      "===================================================================================================================================================\n",
      "Dep. Variable:     <statsmodels.regression.linear_model.RegressionResultsWrapper object at 0x7f086d66abc0>   R-squared:                       0.337\n",
      "Model:                                                                                                 OLS   Adj. R-squared:                  0.335\n",
      "No. Observations:                                                                                      300   F-statistic:                     151.6\n",
      "Covariance Type:                                                                                 nonrobust   Prob (F-statistic):           1.94e-28\n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -5.8252      0.088    -66.111      0.000      -5.999      -5.652\n",
      "temp           0.0950      0.008     12.312      0.000       0.080       0.110\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "SkiveAvg[\"lchla\"] = np.log(SkiveAvg[\"chla\"])\n",
    "fit = smf.ols(formula = \"lchla~temp\", data = SkiveAvg).fit()\n",
    "print(fit.summary(fit,slim=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Estimates</th>\n",
       "      <th>Std.Error</th>\n",
       "      <th>t_obs</th>\n",
       "      <th>p-value</th>\n",
       "      <th>ci_low</th>\n",
       "      <th>ci_high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>beta0</th>\n",
       "      <td>-5.825214</td>\n",
       "      <td>0.088113</td>\n",
       "      <td>-66.110732</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.998616</td>\n",
       "      <td>-5.651811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta</th>\n",
       "      <td>0.094955</td>\n",
       "      <td>0.007712</td>\n",
       "      <td>12.312034</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.079777</td>\n",
       "      <td>0.110132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Estimates  Std.Error      t_obs  p-value    ci_low   ci_high\n",
       "beta0  -5.825214   0.088113 -66.110732      0.0 -5.998616 -5.651811\n",
       "beta    0.094955   0.007712  12.312034      0.0  0.079777  0.110132"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coefTab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3371682543277057\n",
      "0.8420459632272005\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "np.float64(0.8420459632272006)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(Rsq)\n",
    "print(sigma)\n",
    "np.sqrt(fit.scale)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction using Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   mean  mean_se  mean_ci_lower  mean_ci_upper  obs_ci_lower  obs_ci_upper\n",
      "0 -5.35     0.06          -5.47          -5.23         -7.01         -3.69\n"
     ]
    }
   ],
   "source": [
    "new_data = pd.DataFrame({\"temp\": [5]})\n",
    "pred = fit.get_prediction(new_data).summary_frame(alpha=0.05)\n",
    "print(round(pred,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     year  month     TN      TP     chla    din     dip    prim  KrIltSv  \\\n",
      "1    1982      1  1.312  0.0887  0.00396  0.415  0.0775  0.0189        0   \n",
      "2    1982      2  1.371  0.0737  0.00568  0.486  0.0427  0.0895        0   \n",
      "3    1982      3  1.500  0.0640  0.00200  0.865  0.0245  0.1640        0   \n",
      "4    1982      4  1.200  0.0450  0.01300  0.588  0.0045  0.7290        0   \n",
      "5    1982      5  0.772  0.0862  0.04325  0.072  0.0098  3.4730        2   \n",
      "..    ...    ...    ...     ...      ...    ...     ...     ...      ...   \n",
      "296  2006      8  0.975  0.3410  0.02043  0.361  0.2480  2.7933        9   \n",
      "297  2006      9  1.018  0.2362  0.02934  0.293  0.1444  3.8355        2   \n",
      "298  2006     10  0.661  0.1375  0.01579  0.198  0.0772  1.0872        5   \n",
      "299  2006     11  0.833  0.0583  0.00226  0.480  0.0307  0.0904        0   \n",
      "300  2006     12  0.820  0.0703  0.00172  0.590  0.0253  0.0409        0   \n",
      "\n",
      "     IltSv  IIltSv   N.load  P.load  Q.load   temp      gr  vmp     lchla  \n",
      "1        0       0  236.578     NaN     NaN   2.23   18.00    0 -5.531511  \n",
      "2        0       0  219.673     NaN     NaN   2.12   42.32    0 -5.170804  \n",
      "3        0       2  222.722     NaN     NaN   3.50   91.96    0 -6.214608  \n",
      "4        0       2  175.406     NaN     NaN   5.95  152.74    0 -4.342806  \n",
      "5        0       2  146.009     NaN     NaN  12.36  221.35    0 -3.140758  \n",
      "..     ...     ...      ...     ...     ...    ...     ...  ...       ...  \n",
      "296      0       3   51.537     NaN     NaN  20.13  174.18    3 -3.890751  \n",
      "297      2       6   58.042     NaN     NaN  16.97  129.28    3 -3.528804  \n",
      "298      2       2   76.244     NaN     NaN  14.47   61.05    3 -4.148378  \n",
      "299      0       6   87.738     NaN     NaN   8.28   25.99    3 -6.092390  \n",
      "300      0       5  103.756     NaN     NaN   6.67   12.52    3 -6.365431  \n",
      "\n",
      "[300 rows x 18 columns]\n"
     ]
    }
   ],
   "source": [
    "print(SkiveAvg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                               OLS Regression Results                                                              \n",
      "===================================================================================================================================================\n",
      "Dep. Variable:     <statsmodels.regression.linear_model.RegressionResultsWrapper object at 0x7f081be2c910>   R-squared:                       0.366\n",
      "Model:                                                                                                 OLS   Adj. R-squared:                  0.362\n",
      "No. Observations:                                                                                      300   F-statistic:                     85.83\n",
      "Covariance Type:                                                                                 nonrobust   Prob (F-statistic):           3.83e-30\n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -5.8825      0.088    -67.086      0.000      -6.055      -5.710\n",
      "temp           0.0610      0.012      5.123      0.000       0.038       0.084\n",
      "gr             0.0033      0.001      3.693      0.000       0.002       0.005\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "fit2 = smf.ols(formula = \"lchla ~ temp + gr\", data = SkiveAvg).fit()\n",
    "print(fit2.summary(fit2,slim=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Residual Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "e = fit2.resid\n",
    "e1 = e[1:(n-1)]\n",
    "e2 = np.roll(e,-1)[1:(n-1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.3718256130566072)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,3)\n",
    "sm.qqplot(fit.resid, line=\"q\", a=1/2, ax=ax[0])\n",
    "ax[1].scatter(fit2.fittedvalues, fit2.resid)\n",
    "ax[2].scatter(e1, e2)\n",
    "np.corrcoef(e1,e2)[0,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extra if time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                               OLS Regression Results                                                              \n",
      "===================================================================================================================================================\n",
      "Dep. Variable:     <statsmodels.regression.linear_model.RegressionResultsWrapper object at 0x7f08208d0b20>   R-squared:                       0.000\n",
      "Model:                                                                                                 OLS   Adj. R-squared:                 -0.003\n",
      "No. Observations:                                                                                      300   F-statistic:                    0.1414\n",
      "Covariance Type:                                                                                 nonrobust   Prob (F-statistic):              0.707\n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept    -28.4290    100.949     -0.282      0.778    -227.093     170.235\n",
      "year           0.0190      0.051      0.376      0.707      -0.081       0.119\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, 5.51e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "fitTemp = smf.ols(formula = \"temp ~ year\", data = SkiveAvg).fit()\n",
    "print(fitTemp.summary(fitTemp,slim=True))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}