Prac_Statistical_Inference_Solutions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistical Inference Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BDIMS Data Overview\n",
    "\n",
    "In the following Exercises, you will be working with measurements of body dimensions. This data set contains measurements from 247 men and 260 women, most of whom were considered healthy young adults.\n",
    "\n",
    "Data source: amstat.org\n",
    "\n",
    "You will begin by downloading the dataset of around 500 observations from OpenIntro website as below.\n",
    "\n",
    "[link](https://github.com/akmand/datasets/blob/main/openintro/bdims.csv)\n",
    "\n",
    "Place the csv in the same directory as this notebook and read in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>bia.di</th>\n",
       "      <th>bii.di</th>\n",
       "      <th>bit.di</th>\n",
       "      <th>che.de</th>\n",
       "      <th>che.di</th>\n",
       "      <th>elb.di</th>\n",
       "      <th>wri.di</th>\n",
       "      <th>kne.di</th>\n",
       "      <th>ank.di</th>\n",
       "      <th>sho.gi</th>\n",
       "      <th>che.gi</th>\n",
       "      <th>wai.gi</th>\n",
       "      <th>nav.gi</th>\n",
       "      <th>hip.gi</th>\n",
       "      <th>thi.gi</th>\n",
       "      <th>bic.gi</th>\n",
       "      <th>for.gi</th>\n",
       "      <th>kne.gi</th>\n",
       "      <th>cal.gi</th>\n",
       "      <th>ank.gi</th>\n",
       "      <th>wri.gi</th>\n",
       "      <th>age</th>\n",
       "      <th>wgt</th>\n",
       "      <th>hgt</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42.9</td>\n",
       "      <td>26.0</td>\n",
       "      <td>31.5</td>\n",
       "      <td>17.7</td>\n",
       "      <td>28.0</td>\n",
       "      <td>13.1</td>\n",
       "      <td>10.4</td>\n",
       "      <td>18.8</td>\n",
       "      <td>14.1</td>\n",
       "      <td>106.2</td>\n",
       "      <td>89.5</td>\n",
       "      <td>71.5</td>\n",
       "      <td>74.5</td>\n",
       "      <td>93.5</td>\n",
       "      <td>51.5</td>\n",
       "      <td>32.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>34.5</td>\n",
       "      <td>36.5</td>\n",
       "      <td>23.5</td>\n",
       "      <td>16.5</td>\n",
       "      <td>21</td>\n",
       "      <td>65.6</td>\n",
       "      <td>174.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43.7</td>\n",
       "      <td>28.5</td>\n",
       "      <td>33.5</td>\n",
       "      <td>16.9</td>\n",
       "      <td>30.8</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>20.6</td>\n",
       "      <td>15.1</td>\n",
       "      <td>110.5</td>\n",
       "      <td>97.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>86.5</td>\n",
       "      <td>94.8</td>\n",
       "      <td>51.5</td>\n",
       "      <td>34.4</td>\n",
       "      <td>28.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>37.5</td>\n",
       "      <td>24.5</td>\n",
       "      <td>17.0</td>\n",
       "      <td>23</td>\n",
       "      <td>71.8</td>\n",
       "      <td>175.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.1</td>\n",
       "      <td>28.2</td>\n",
       "      <td>33.3</td>\n",
       "      <td>20.9</td>\n",
       "      <td>31.7</td>\n",
       "      <td>13.9</td>\n",
       "      <td>10.9</td>\n",
       "      <td>19.7</td>\n",
       "      <td>14.1</td>\n",
       "      <td>115.1</td>\n",
       "      <td>97.5</td>\n",
       "      <td>83.2</td>\n",
       "      <td>82.9</td>\n",
       "      <td>95.0</td>\n",
       "      <td>57.3</td>\n",
       "      <td>33.4</td>\n",
       "      <td>28.8</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.3</td>\n",
       "      <td>21.9</td>\n",
       "      <td>16.9</td>\n",
       "      <td>28</td>\n",
       "      <td>80.7</td>\n",
       "      <td>193.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44.3</td>\n",
       "      <td>29.9</td>\n",
       "      <td>34.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>28.2</td>\n",
       "      <td>13.9</td>\n",
       "      <td>11.2</td>\n",
       "      <td>20.9</td>\n",
       "      <td>15.0</td>\n",
       "      <td>104.5</td>\n",
       "      <td>97.0</td>\n",
       "      <td>77.8</td>\n",
       "      <td>78.8</td>\n",
       "      <td>94.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>26.2</td>\n",
       "      <td>37.0</td>\n",
       "      <td>34.8</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16.6</td>\n",
       "      <td>23</td>\n",
       "      <td>72.6</td>\n",
       "      <td>186.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42.5</td>\n",
       "      <td>29.9</td>\n",
       "      <td>34.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>29.4</td>\n",
       "      <td>15.2</td>\n",
       "      <td>11.6</td>\n",
       "      <td>20.7</td>\n",
       "      <td>14.9</td>\n",
       "      <td>107.5</td>\n",
       "      <td>97.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>82.5</td>\n",
       "      <td>98.5</td>\n",
       "      <td>55.4</td>\n",
       "      <td>32.0</td>\n",
       "      <td>28.4</td>\n",
       "      <td>37.7</td>\n",
       "      <td>38.6</td>\n",
       "      <td>24.4</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22</td>\n",
       "      <td>78.8</td>\n",
       "      <td>187.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bia.di  bii.di  bit.di  che.de  che.di  elb.di  wri.di  kne.di  ank.di  \\\n",
       "0    42.9    26.0    31.5    17.7    28.0    13.1    10.4    18.8    14.1   \n",
       "1    43.7    28.5    33.5    16.9    30.8    14.0    11.8    20.6    15.1   \n",
       "2    40.1    28.2    33.3    20.9    31.7    13.9    10.9    19.7    14.1   \n",
       "3    44.3    29.9    34.0    18.4    28.2    13.9    11.2    20.9    15.0   \n",
       "4    42.5    29.9    34.0    21.5    29.4    15.2    11.6    20.7    14.9   \n",
       "\n",
       "   sho.gi  che.gi  wai.gi  nav.gi  hip.gi  thi.gi  bic.gi  for.gi  kne.gi  \\\n",
       "0   106.2    89.5    71.5    74.5    93.5    51.5    32.5    26.0    34.5   \n",
       "1   110.5    97.0    79.0    86.5    94.8    51.5    34.4    28.0    36.5   \n",
       "2   115.1    97.5    83.2    82.9    95.0    57.3    33.4    28.8    37.0   \n",
       "3   104.5    97.0    77.8    78.8    94.0    53.0    31.0    26.2    37.0   \n",
       "4   107.5    97.5    80.0    82.5    98.5    55.4    32.0    28.4    37.7   \n",
       "\n",
       "   cal.gi  ank.gi  wri.gi  age   wgt    hgt  sex  \n",
       "0    36.5    23.5    16.5   21  65.6  174.0    1  \n",
       "1    37.5    24.5    17.0   23  71.8  175.3    1  \n",
       "2    37.3    21.9    16.9   28  80.7  193.5    1  \n",
       "3    34.8    23.0    16.6   23  72.6  186.5    1  \n",
       "4    38.6    24.4    18.0   22  78.8  187.2    1  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import io\n",
    "import requests\n",
    "\n",
    "# so that we can see all the columns\n",
    "pd.set_option('display.max_columns', None) \n",
    "\n",
    "df_url = 'https://raw.githubusercontent.com/akmand/datasets/master/openintro/bdims.csv'\n",
    "url_content = requests.get(df_url, verify=False).content\n",
    "bdims = pd.read_csv(io.StringIO(url_content.decode('utf-8')))\n",
    "\n",
    "# bdims = pd.read_csv('bdims.csv')\n",
    "\n",
    "bdims.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 1:** Make a histogram of men's heights and a histogram of women's heights. How would you compare the various aspects of the two distributions?\n",
    "\n",
    "**Hint:** `sex = 1` means male, otherwise female."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 358,
       "width": 499
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 358,
       "width": 492
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both distributions roughly follow the normal distribution. Average height is higher for men.\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import norm,t\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "plt.style.use(\"seaborn\")\n",
    "\n",
    "# Create two data sets: one with only men and another one with only women.\n",
    "mdims = bdims[bdims['sex'] == 1]\n",
    "fdims = bdims[bdims['sex'] == 0]\n",
    "\n",
    "plt.hist(mdims['hgt'], color = 'blue')\n",
    "plt.xlabel('Height')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Male Height')\n",
    "plt.show()\n",
    "\n",
    "plt.hist(fdims['hgt'], color = 'orange')\n",
    "plt.xlabel('Height')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Female Height')\n",
    "plt.show()\n",
    "\n",
    "print('Both distributions roughly follow the normal distribution. Average height is higher for men.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 2:** Make a normal probability plot of men's heights and a normal probability plot of women's heights. Do all of the points fall on the line?\n",
    "\n",
    "**Hint:** Use `probplot` function from [`scipy.stats`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.probplot.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 875,
       "width": 610
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import probplot\n",
    "\n",
    "plt.figure(figsize=(10,15))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "probplot(mdims['hgt'], plot = plt)\n",
    "plt.title('Normal Q-Q Plot (Data) for Male Height')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "probplot(fdims['hgt'], plot = plt)\n",
    "plt.title('Normal Q-Q Plot (Data) Female Height')\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 3:** Now that we have seen that both male and female heights are approximately normally distributed, let's focus on male height. What is the sample mean and standard deviation of `hgt`? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The sample mean is 177.75\n",
      "The sample standard deviation is 7.18\n"
     ]
    }
   ],
   "source": [
    "mht = mdims['hgt']\n",
    "\n",
    "print(f\"The sample mean is {mht.mean():.2f}\")\n",
    "print(f\"The sample standard deviation is {np.std(mht, ddof = 1):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 4:** What is the standard error for male height?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The standard error is: 0.457\n"
     ]
    }
   ],
   "source": [
    "# standard deviation\n",
    "sample_sd = np.std(mht , ddof=1)\n",
    "\n",
    "# standard error = std / sqrt(n)\n",
    "standard_error = sample_sd/np.sqrt(len(mht))\n",
    "print(f\"The standard error is: {standard_error:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 5:** Calculate the 95% confidence interval for men's height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ME = |Zscore * standard error| = 0.90\n",
      "CI_lower = sample mean - Margin of error = 177.75 - Z_0.025 * 0.457 = 176.85\n",
      "CI_upper = sample mean + Margin of error = 177.75 + Z_0.975 * 0.457 = 178.65\n",
      "The 95% confidence interval is [176.85, 178.65]\n"
     ]
    }
   ],
   "source": [
    "alpha = (1-0.95)/2\n",
    "\n",
    "print(f\"ME = |Zscore * standard error| = {abs(norm.ppf(alpha)*0.457):.2f}\")\n",
    "\n",
    "CI_lower = 177.75 - abs(norm.ppf(alpha)*0.457)\n",
    "CI_upper = 177.75 + abs(norm.ppf(alpha)*0.457)\n",
    "\n",
    "print(f\"CI_lower = sample mean - Margin of error = 177.75 - Z_0.025 * 0.457 = {CI_lower:.2f}\")\n",
    "print(f\"CI_upper = sample mean + Margin of error = 177.75 + Z_0.975 * 0.457 = {CI_upper:.2f}\") \n",
    "\n",
    "print(f\"The 95% confidence interval is [{CI_lower:.2f}, {CI_upper:.2f}]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 6:** Would you expect the 90% confidence interval to be wider or more narrow than the 95% confidence interval? Calculate the 90% Confidence interval for mens height to confirm your answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ME = |Zscore * standard error| = 0.75\n",
      "CI_lower = sample mean - Margin of error = 177.75 - Z_0.025 * 0.457 = 177.00\n",
      "CI_upper = sample mean + Margin of error = 177.75 + Z_0.975 * 0.457 = 178.50\n",
      "The 90% confidence interval is [177.00, 178.50]\n"
     ]
    }
   ],
   "source": [
    "alpha = (1-0.90)/2\n",
    "\n",
    "print(f\"ME = |Zscore * standard error| = {abs(norm.ppf(alpha)*0.457):.2f}\")\n",
    "\n",
    "CI_lower = 177.75 - abs(norm.ppf(alpha)*0.457)\n",
    "CI_upper = 177.75 + abs(norm.ppf(alpha)*0.457)\n",
    "\n",
    "print(f\"CI_lower = sample mean - Margin of error = 177.75 - Z_0.025 * 0.457 = {CI_lower:.2f}\")\n",
    "print(f\"CI_upper = sample mean + Margin of error = 177.75 + Z_0.975 * 0.457 = {CI_upper:.2f}\") \n",
    "\n",
    "print(f\"The 90% confidence interval is [{CI_lower:.2f}, {CI_upper:.2f}]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 7:** Perform a hypothesis test using a 5% significance test on female height to see whether the mean height is different to 164."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let x represent the mean female height\n",
      "H0: x = 164\n",
      "Ha: x != 164\n",
      "\n",
      "test_statistic = (x - sample_mean)/(std/sqrt(n)) = -2.15\n",
      "p_value = P(|T|> 0.29) = 0.032\n",
      "\n",
      "Since our p value is less than 0.05, we reject the null hypothesis at a 5% significance level\n",
      "and conclude that the mean female height is different to 164\n"
     ]
    }
   ],
   "source": [
    "# get female heights\n",
    "fht = fdims['hgt']\n",
    "\n",
    "# mean and std\n",
    "mean = fht.mean()\n",
    "std = fht.std(ddof=1)\n",
    "\n",
    "# define your hypothesis\n",
    "print(\"Let x represent the mean female height\")\n",
    "print(\"H0: x = 164\")\n",
    "print(\"Ha: x != 164\\n\")\n",
    "\n",
    "# Calculate the test statistic\n",
    "T = (164-mean)/(std/np.sqrt(len(fht)))\n",
    "print(f\"test_statistic = (x - sample_mean)/(std/sqrt(n)) = {T:.2f}\")\n",
    "\n",
    "p = 2*(1-norm.cdf(abs(T)))\n",
    "\n",
    "print(f\"p_value = P(|T|> 0.29) = {p:.3f}\\n\")\n",
    "\n",
    "print(\"Since our p value is less than 0.05, we reject the null hypothesis at a 5% significance level\\nand conclude that the mean female height is different to 164\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 8:** Would we have reached the same conclusion in the previous question if we were using a 1% significance level?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If we were using a 1% significance level, then the p value would be greater than 0.01 \n",
      "so we would fail to reject the null hypothesis and conclude that the mean female height is 164.\n"
     ]
    }
   ],
   "source": [
    "print(\"\"\"If we were using a 1% significance level, then the p value would be greater than 0.01 \n",
    "so we would fail to reject the null hypothesis and conclude that the mean female height is 164.\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 9:** We will now shift our attention to t distributions.\n",
    "\n",
    "We use scipy.stats generate a random sample (seed =11) of 20 centered around 5 and with a standard deviation of 0.9 by running the following code:\n",
    "\n",
    "```{code}\n",
    "np.random.seed(11)\n",
    "x = norm.rvs(loc = 5 , scale = 0.9, size =20)\n",
    "```\n",
    "\n",
    "What is the sample mean and standard deviation?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The sample mean is 4.83 and standard deviation is 0.92\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(11)\n",
    "x = norm.rvs(loc = 5 , scale = 0.9, size =20)\n",
    "\n",
    "print(f\"The sample mean is {x.mean():.2f} and standard deviation is {x.std(ddof=1):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 10:** Plot this random sample as a distribution plot in seaborn with kde set to True. How would you describe the distribution?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 329,
       "width": 495
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The data is unimodal and somewhat symmetric\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.distplot(x ,kde=True)\n",
    "plt.show()\n",
    "\n",
    "print(\"The data is unimodal and somewhat symmetric\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 11:** Calculate the p_value for the null and alternate hypothesis below on the randomly generated data.\n",
    "\n",
    "$$H_0: \\mu = 5.22 $$\n",
    "$$H_1: \\mu \\neq 5.22$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The p value is 0.072\n"
     ]
    }
   ],
   "source": [
    "# standard error\n",
    "se = np.std(x , ddof=1)/np.sqrt(20)\n",
    "\n",
    "null = 5.22\n",
    "mean = x.mean()\n",
    "\n",
    "# test statistic\n",
    "test = (null - mean)/se\n",
    "\n",
    "# p value\n",
    "p_value = 2*(1-abs(t.cdf(test , df=19)))\n",
    "\n",
    "print(f\"The p value is {p_value:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercises 12:**\n",
    "How do we interpret the p value? For what conventional significance levels (1%, 5% and 10%) do we reject and fail to reject the hypothesis.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The p-value is a measure of the probability that an observed deviance from the null hypothesis \n",
      "could have occurred justby random chance.\n",
      "\n",
      "For a significance level of of 1% and 5% we fail to reject the null hypothesis whereas we \n",
      "reject the null hypothesis for a significance level of 10%.\n"
     ]
    }
   ],
   "source": [
    "print(\"\"\"\n",
    "The p-value is a measure of the probability that an observed deviance from the null hypothesis \n",
    "could have occurred justby random chance.\n",
    "\n",
    "For a significance level of of 1% and 5% we fail to reject the null hypothesis whereas we \n",
    "reject the null hypothesis for a significance level of 10%.\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise 13:** For a different alone variable, you are given the following hypotheses:\n",
    "\n",
    "$Ho$: $\\mu$ = 60\n",
    "\n",
    "$Ha$: $\\mu$ != 60\n",
    "\n",
    "The sample standard deviation is 8 and the sample size is 20. For what sample mean would the p-value be equal to 0.05? Assume that all conditions necessary for inference are satisfied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "For the single tails to each be 0.025 at n - 1 = 20 - 1 = 19 degrees of feedom, T score must\n",
      "equal to be either -2.09 or 2.09. Then, either:\n",
      "-2.09 = (sample_mean-60)/(8/(20)^1/2) ---> sample_mean = 56.26\n",
      " 2.09 = (sample_mean-60)/(8/(20)^1/2) ---> sample_mean = 63.74\n"
     ]
    }
   ],
   "source": [
    "print(f\"\"\"\n",
    "For the single tails to each be 0.025 at n - 1 = 20 - 1 = 19 degrees of feedom, T score must\n",
    "equal to be either {t.ppf(0.025, df=19):.2f} or {t.ppf(1-0.025, df=19):.2f}. Then, either:\"\"\")\n",
    "###\n",
    "print(f\"-2.09 = (sample_mean-60)/(8/(20)^1/2) ---> sample_mean = {(-2.09*8/np.sqrt(20)+60):.2f}\")\n",
    "print(f\" 2.09 = (sample_mean-60)/(8/(20)^1/2) ---> sample_mean = {(2.09*8/np.sqrt(20)+60):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "www.featureranking.com"
   ]
  }
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