2_SLR_Significance_Confidence_fkingfisher.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "aa13cd0f-de9a-4a9a-9177-7c53d9aea186",
   "metadata": {
    "tags": []
   },
   "source": [
    "The number of pounds of steam used per month at a plant is thought to be related to the average monthly ambient temperature. The past year’s usages and temperatures follow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cd90d453-fe1f-44f2-a453-51c7779907ec",
   "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>Month</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Usage/l000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan.</td>\n",
       "      <td>21</td>\n",
       "      <td>185.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jul.</td>\n",
       "      <td>68</td>\n",
       "      <td>621.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Feb.</td>\n",
       "      <td>24</td>\n",
       "      <td>214.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aug.</td>\n",
       "      <td>74</td>\n",
       "      <td>675.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mar.</td>\n",
       "      <td>32</td>\n",
       "      <td>288.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sep.</td>\n",
       "      <td>62</td>\n",
       "      <td>562.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Apr.</td>\n",
       "      <td>47</td>\n",
       "      <td>424.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Oct.</td>\n",
       "      <td>50</td>\n",
       "      <td>452.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>May</td>\n",
       "      <td>50</td>\n",
       "      <td>454.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nov.</td>\n",
       "      <td>41</td>\n",
       "      <td>369.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Jun.</td>\n",
       "      <td>59</td>\n",
       "      <td>539.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dec.</td>\n",
       "      <td>30</td>\n",
       "      <td>273.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month  Temperature  Usage/l000\n",
       "0   Jan.           21      185.79\n",
       "1   Jul.           68      621.55\n",
       "2   Feb.           24      214.47\n",
       "3   Aug.           74      675.06\n",
       "4   Mar.           32      288.03\n",
       "5   Sep.           62      562.03\n",
       "6   Apr.           47      424.84\n",
       "7   Oct.           50      452.93\n",
       "8    May           50      454.68\n",
       "9   Nov.           41      369.95\n",
       "10  Jun.           59      539.03\n",
       "11  Dec.           30      273.98"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "dat212 = pd.read_csv(\"https://raw.githubusercontent.com/forestwhite/Regression/main/data/212Table.csv\")\n",
    "display(dat212)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b0ddae9-7fe7-4246-8a4e-0e18da05c52b",
   "metadata": {
    "tags": []
   },
   "source": [
    "**b. Test for significance of regression.**\n",
    "\n",
    "There are two tests we can use to test the significance of the whole model, the t and F tests. To apply the t test, we can use summary data from the linear model or calculate the t statistic manually and compare it to the t critical value for the degrees of freedom and 95% confidence target:\n",
    "\n",
    " * t_critical value (alpha = 0.05, df = n-2 = 10): 2.2281388519649385, \n",
    " * t_statistic for our data: 272.254998757909\n",
    " \n",
    "t_statistic > t_critical, therefore we <u>reject H_0</u> and conclude that <u>number of pounds of steam used per month is significantly correlated to the average monthly ambient temperature</u>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a82ebcb5-6eb6-48d1-abe2-aebb34206c1e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.2281388519649385"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 1., 21.],\n",
       "       [ 1., 68.],\n",
       "       [ 1., 24.],\n",
       "       [ 1., 74.],\n",
       "       [ 1., 32.],\n",
       "       [ 1., 62.],\n",
       "       [ 1., 47.],\n",
       "       [ 1., 50.],\n",
       "       [ 1., 50.],\n",
       "       [ 1., 41.],\n",
       "       [ 1., 59.],\n",
       "       [ 1., 30.]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "const   -6.332087\n",
       "x1       9.208468\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "272.254998757909"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t statistic: \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "272.2550042226885"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate two-tail t critical value for alpha=0.05, degrees of freedom=n-2=10\n",
    "from scipy import stats\n",
    "display(stats.t.ppf(q=1-.05/2, df=10))\n",
    "\n",
    "# calculate t statistic for our data set using the linear model\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "\n",
    "dataset = pd.read_csv(\"https://raw.githubusercontent.com/forestwhite/Regression/main/data/212Table.csv\")\n",
    "\n",
    "# Creating X and y\n",
    "X = dataset['Temperature'].values\n",
    "y = dataset['Usage/l000']\n",
    "\n",
    "# Add a list of ones with length of X to identify intercept terms in linear model\n",
    "x = sm.add_constant(X)\n",
    "lm212 = sm.OLS(y,x).fit()\n",
    "# To list all fields in lm212 model display(dir(lm212))\n",
    "display(lm212.params)\n",
    "display(lm212.tvalues[1])\n",
    "\n",
    "# calculate y_hat estimate, sum of squares error, variance, and sum of squares x^2\n",
    "yhat = 9.208468*X - 6.332087\n",
    "SS_error=sum(np.square(np.subtract(y,yhat)))\n",
    "variance_y=SS_error/10\n",
    "SSxx = sum(np.square(np.subtract(X,np.average(X))))\n",
    "\n",
    "# calculate t statistic for our data set manually\n",
    "t_stat = 9.208468/(np.sqrt(variance_y/SSxx))\n",
    "print(\"t statistic: \")\n",
    "display(t_stat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "028faa54-4f4a-45ac-abcc-5ecdfba2208c",
   "metadata": {
    "tags": []
   },
   "source": [
    "To apply the F test, we use the F value calculated in the linear model or calculate the F statistic for our model manually and compare it to the critical F value given our degrees of freedom:\n",
    "\n",
    " * F_critical value (alpha = 0.05, df = n-2 = 10): 4.964603 \n",
    " * F_statistic for our data: 74122.78\n",
    " \n",
    "F_statistic > F_critical, therefore we <u>reject H_0</u> and conclude that <u>number of pounds of steam used per month is significantly correlated to the average monthly ambient temperature</u>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b2caab6c-d127-404a-85bd-e96c34287949",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.9646027437307145"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "const         -6.332087\n",
       "Temperature    9.208468\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "74122.78434866904"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F statistic: \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "74122.78732429638"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate one-sided F critical value for alpha=0.05, degrees of freedom=n-2=10\n",
    "from scipy import stats\n",
    "display(stats.f.ppf(q=1-0.05, dfn=1, dfd=10))\n",
    "\n",
    "# calculate F-statistic for our data set using the linear model\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "\n",
    "dataset = pd.read_csv(\"https://raw.githubusercontent.com/forestwhite/Regression/main/data/212Table.csv\")\n",
    "\n",
    "# Creating X and y\n",
    "X = dataset['Temperature']\n",
    "y = dataset['Usage/l000']\n",
    "\n",
    "x = sm.add_constant(X)\n",
    "lm212 = sm.OLS(y,x).fit()\n",
    "# To list all fields in lm212 model display(dir(lm212))\n",
    "display(lm212.params)\n",
    "\n",
    "# calculate F statistic for our data set using the linear model\n",
    "display(lm212.fvalue)\n",
    "\n",
    "# calculate y_hat estimate, sum of squares error, and sum of squares regression (response)\n",
    "yhat = 9.208468*X - 6.332087\n",
    "SS_error=sum(np.square(np.subtract(y,yhat)))\n",
    "SS_regression=sum(np.square(np.subtract(yhat,np.average(y))))\n",
    "\n",
    "# calculate F-statistic for our data set manually\n",
    "f_stat=(SS_regression/1)/(SS_error/10)\n",
    "print(\"F statistic: \")\n",
    "display(f_stat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97408316-6354-48cc-8640-80ed191957c0",
   "metadata": {
    "tags": []
   },
   "source": [
    "**c. Plant management believes that an increase in average ambient temperature of 1 degree will increase average monthly steam consumption by 10,000 lb. Do the data support this statement?**\n",
    "\n",
    "This question suggests Beta_1 is 10 for the population, and asks us to test this against the Beta_1_hat we get from the data. The t test statistics are:\n",
    "\n",
    " * t_critical value (alpha = 0.05, df = n-2 = 10): 2.2281388519649385\n",
    " * t_statistic for Beta_1 = 10: -23.40215587352691\n",
    " \n",
    "|t_statistic| > t_critical, therefore we <u>reject H_0</u> and conclude that <u>the slope is not 10</u>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8e5db4e8-fa47-459e-9d6b-242380f70907",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.2281388519649385"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t statistic: \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-23.40215587352691"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate two-tail t critical value for alpha=0.05, degrees of freedom=n-2=10\n",
    "from scipy import stats\n",
    "display(stats.t.ppf(q=1-.05/2, df=10))\n",
    "\n",
    "# calculate t statistic for our data set using the linear model\n",
    "import numpy as np\n",
    "\n",
    "beta_1_hat = 9.20847 # least squares fit coefficient\n",
    "beta_1_0 = 10 # hypothesized coefficient from plant management\n",
    "\n",
    "# calculate y_hat estimate, sum of squares error, variance, and sum of squares x^2\n",
    "yhat = 9.208468*X - 6.332087\n",
    "SS_error=sum(np.square(np.subtract(y,yhat)))\n",
    "variance_y=SS_error/10\n",
    "SSxx = sum(np.square(np.subtract(X,np.average(X))))\n",
    "\n",
    "# calculate t statistic that compares coefficients manually\n",
    "t_stat = (beta_1_hat-beta_1_0)/(np.sqrt(variance_y/SSxx))\n",
    "print(\"t statistic: \")\n",
    "display(t_stat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cad2d31-8bcb-47e3-9797-53d00057cf86",
   "metadata": {
    "tags": []
   },
   "source": [
    "**d. Construct a 99% confidence and prediction intervals on steam usage in a month with average ambient temperature of 58°.**\n",
    "\n",
    " * The Confidence Interval (E[y|58]) = (525.594,529.9244)\n",
    " * The Prediction Interval (y_i,new) = (521.2238,534.2945)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9fa8a834-8b15-48fc-935c-bdbab99e4cb0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.169272667175838"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "const         -6.332087\n",
       "Temperature    9.208468\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "525.593841143829"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "529.924272856171"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "mean             527.759047\n",
       "mean_se            0.683190\n",
       "mean_ci_lower    525.593831\n",
       "mean_ci_upper    529.924262\n",
       "obs_ci_lower     521.223718\n",
       "obs_ci_upper     534.294375\n",
       "Name: 1, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate two-tail t critical value for alpha=0.05, degrees of freedom=n-2=10\n",
    "from scipy import stats\n",
    "t_99=stats.t.ppf(q=1-.01/2, df=10)\n",
    "display(t_99)\n",
    "\n",
    "# calculate t-statistic for our data set using the linear model\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "\n",
    "dataset = pd.read_csv(\"https://raw.githubusercontent.com/forestwhite/Regression/main/data/212Table.csv\")\n",
    "\n",
    "# Creating X and y\n",
    "X = dataset['Temperature']\n",
    "y = dataset['Usage/l000']\n",
    "\n",
    "x = sm.add_constant(X)\n",
    "lm212 = sm.OLS(y,x).fit()\n",
    "# To list all fields in lm212 model display(dir(lm212))\n",
    "display(lm212.params)\n",
    "\n",
    "# calculate y_hat estimate, sum of squares error, variance, and sum of squares x^2\n",
    "yhat_58 = 9.208468*58 - 6.332087\n",
    "yhat = 9.208468*X - 6.332087\n",
    "SS_error=sum(np.square(np.subtract(y,yhat)))\n",
    "variance_y=SS_error/10\n",
    "SSxx = sum(np.square(np.subtract(X,np.average(X))))\n",
    "\n",
    "#mean squared error is equal to variance of y, manually calculate confidence interval\n",
    "lower_confidence = yhat_58-t_99*(np.sqrt(variance_y*(1/12+np.square(58-np.average(X))/SSxx)))\n",
    "upper_confidence = yhat_58+t_99*(np.sqrt(variance_y*(1/12+np.square(58-np.average(X))/SSxx)))\n",
    "display(lower_confidence)\n",
    "display(upper_confidence)\n",
    "\n",
    "#use linear model to automatically calculate confidence\n",
    "y_prd = lm212.get_prediction(sm.add_constant([0,58])) #IMPORTANT: input >1 predictor values\n",
    "display(y_prd.summary_frame(alpha = 0.01).T[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9144f5b5-270f-4610-ad28-b820934db3d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "Plot the data adding a fitted regression line, 95% confidence interval, and 95% prediction interval (assume the error is normally distributed)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3bbb7224-cdef-4ef1-b0ac-68ec2e576b9b",
   "metadata": {
    "tags": []
   },
   "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>Temperature</th>\n",
       "      <th>Usage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>185.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68</td>\n",
       "      <td>621.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>214.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>74</td>\n",
       "      <td>675.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>288.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>62</td>\n",
       "      <td>562.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>47</td>\n",
       "      <td>424.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>50</td>\n",
       "      <td>452.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>50</td>\n",
       "      <td>454.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>41</td>\n",
       "      <td>369.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>59</td>\n",
       "      <td>539.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30</td>\n",
       "      <td>273.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Temperature   Usage\n",
       "0            21  185.79\n",
       "1            68  621.55\n",
       "2            24  214.47\n",
       "3            74  675.06\n",
       "4            32  288.03\n",
       "5            62  562.03\n",
       "6            47  424.84\n",
       "7            50  452.93\n",
       "8            50  454.68\n",
       "9            41  369.95\n",
       "10           59  539.03\n",
       "11           30  273.98"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Intercept     -6.332087\n",
       "Temperature    9.208468\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>mean</th>\n",
       "      <th>mean_se</th>\n",
       "      <th>mean_ci_lower</th>\n",
       "      <th>mean_ci_upper</th>\n",
       "      <th>obs_ci_lower</th>\n",
       "      <th>obs_ci_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>187.045737</td>\n",
       "      <td>1.029241</td>\n",
       "      <td>184.752446</td>\n",
       "      <td>189.339029</td>\n",
       "      <td>182.141399</td>\n",
       "      <td>191.950076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>619.843725</td>\n",
       "      <td>0.918840</td>\n",
       "      <td>617.796423</td>\n",
       "      <td>621.891027</td>\n",
       "      <td>615.049478</td>\n",
       "      <td>624.637971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>214.671141</td>\n",
       "      <td>0.945834</td>\n",
       "      <td>212.563692</td>\n",
       "      <td>216.778590</td>\n",
       "      <td>209.850903</td>\n",
       "      <td>219.491379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>675.094532</td>\n",
       "      <td>1.086554</td>\n",
       "      <td>672.673538</td>\n",
       "      <td>677.515525</td>\n",
       "      <td>670.129195</td>\n",
       "      <td>680.059868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>288.338883</td>\n",
       "      <td>0.745641</td>\n",
       "      <td>286.677492</td>\n",
       "      <td>290.000275</td>\n",
       "      <td>283.696301</td>\n",
       "      <td>292.981466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>564.592918</td>\n",
       "      <td>0.768310</td>\n",
       "      <td>562.881017</td>\n",
       "      <td>566.304819</td>\n",
       "      <td>559.932021</td>\n",
       "      <td>569.253815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>426.465901</td>\n",
       "      <td>0.561909</td>\n",
       "      <td>425.213889</td>\n",
       "      <td>427.717912</td>\n",
       "      <td>421.953597</td>\n",
       "      <td>430.978204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>454.091304</td>\n",
       "      <td>0.573995</td>\n",
       "      <td>452.812364</td>\n",
       "      <td>455.370244</td>\n",
       "      <td>449.571454</td>\n",
       "      <td>458.611154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>454.091304</td>\n",
       "      <td>0.573995</td>\n",
       "      <td>452.812364</td>\n",
       "      <td>455.370244</td>\n",
       "      <td>449.571454</td>\n",
       "      <td>458.611154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>371.215094</td>\n",
       "      <td>0.591660</td>\n",
       "      <td>369.896793</td>\n",
       "      <td>372.533394</td>\n",
       "      <td>366.683949</td>\n",
       "      <td>375.746238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>536.967514</td>\n",
       "      <td>0.702997</td>\n",
       "      <td>535.401140</td>\n",
       "      <td>538.533889</td>\n",
       "      <td>532.358080</td>\n",
       "      <td>541.576949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>269.921948</td>\n",
       "      <td>0.791775</td>\n",
       "      <td>268.157763</td>\n",
       "      <td>271.686132</td>\n",
       "      <td>265.241595</td>\n",
       "      <td>274.602300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          mean   mean_se  mean_ci_lower  mean_ci_upper  obs_ci_lower  \\\n",
       "0   187.045737  1.029241     184.752446     189.339029    182.141399   \n",
       "1   619.843725  0.918840     617.796423     621.891027    615.049478   \n",
       "2   214.671141  0.945834     212.563692     216.778590    209.850903   \n",
       "3   675.094532  1.086554     672.673538     677.515525    670.129195   \n",
       "4   288.338883  0.745641     286.677492     290.000275    283.696301   \n",
       "5   564.592918  0.768310     562.881017     566.304819    559.932021   \n",
       "6   426.465901  0.561909     425.213889     427.717912    421.953597   \n",
       "7   454.091304  0.573995     452.812364     455.370244    449.571454   \n",
       "8   454.091304  0.573995     452.812364     455.370244    449.571454   \n",
       "9   371.215094  0.591660     369.896793     372.533394    366.683949   \n",
       "10  536.967514  0.702997     535.401140     538.533889    532.358080   \n",
       "11  269.921948  0.791775     268.157763     271.686132    265.241595   \n",
       "\n",
       "    obs_ci_upper  \n",
       "0     191.950076  \n",
       "1     624.637971  \n",
       "2     219.491379  \n",
       "3     680.059868  \n",
       "4     292.981466  \n",
       "5     569.253815  \n",
       "6     430.978204  \n",
       "7     458.611154  \n",
       "8     458.611154  \n",
       "9     375.746238  \n",
       "10    541.576949  \n",
       "11    274.602300  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "\n",
    "dataset = pd.read_csv(\"https://raw.githubusercontent.com/forestwhite/Regression/main/data/212Table.csv\")\n",
    "\n",
    "# Creating X and y\n",
    "X = dataset['Temperature']\n",
    "y = dataset['Usage/l000']\n",
    "dat212 = pd.DataFrame(X).join(pd.DataFrame(y))\n",
    "# Change the column names\n",
    "dat212.columns =['Temperature', 'Usage']\n",
    "display(dat212)\n",
    "\n",
    "#Calculate 95% confidence and prediction intervals\n",
    "model = sm.formula.ols('Usage ~ Temperature', dat212)\n",
    "lm212 = model.fit()\n",
    "display(lm212.params)\n",
    "alpha = 0.05\n",
    "predictions = lm212.get_prediction(dat212).summary_frame(alpha)\n",
    "display(predictions)\n",
    "\n",
    "#Plot confidence and prediction intervals as bottom layer\n",
    "plt.figure(figsize=(15, 15))\n",
    "plt.fill_between(dat212['Temperature'], predictions['obs_ci_lower'], predictions['obs_ci_upper'], alpha=.1, label='Prediction interval', color='blue')\n",
    "plt.fill_between(dat212['Temperature'], predictions['mean_ci_lower'], predictions['mean_ci_upper'], alpha=.5, label='Confidence interval',color='orange')\n",
    "\n",
    "#plots the data points\n",
    "plt.scatter(X,y,label='Observed', marker='x', color='black')\n",
    "\n",
    "#Create and plot the regression line\n",
    "yhat = 9.2085*X - 6.3321\n",
    "plt.plot(X, yhat, lw=1, c='blue', label = 'regression line')\n",
    "\n",
    "#Add labels and legend last\n",
    "plt.xlabel('Temperature (°F)', fontsize = 20)\n",
    "plt.ylabel('Pounds of Steam/1000', fontsize = 20)\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59ca3597-7eb6-408a-a293-9ec90e410064",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}