Hypothesis_testing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b48451b9",
   "metadata": {},
   "source": [
    "# Hypothesis testing "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17dd8d7e",
   "metadata": {},
   "source": [
    "- Hypothesis testing is a statistical method to determine whether observed data is likely to be real or just a coincidence. \n",
    "- It involves comparing the data to a null hypothesis and accepting or rejecting it based on the probability that \n",
    "   the observed results occurred by chance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec383807",
   "metadata": {},
   "source": [
    "### Chi-Square Test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f125dc24",
   "metadata": {},
   "source": [
    "- Chi-Square Test- The test is applied when you have two categorical variables from a single population. \n",
    "- It is used to determine whether there is a significant association between the two variables. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dff46f7",
   "metadata": {},
   "source": [
    "χ2 = ∑(O – E)2/E\n",
    "\n",
    "- where:\n",
    "- O is the observed value in each category\n",
    "- E is the expected value in each category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bee01f08",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ac884ff3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ce17e44e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = sns.load_dataset('tips')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5bff36ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bf2c6ffd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step1:- Pick up the catogarical features.\n",
    "# crosstab creates a frequency table or cross matrix b/w 2 or more catogrical variable.\n",
    "dataset_table = pd.crosstab(dataset['sex'], dataset['smoker'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d13fa640",
   "metadata": {},
   "outputs": [
    {
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       "    }\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>smoker</th>\n",
       "      <th>Yes</th>\n",
       "      <th>No</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>60</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>33</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "smoker  Yes  No\n",
       "sex            \n",
       "Male     60  97\n",
       "Female   33  54"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7f6ed47f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[60, 97],\n",
       "       [33, 54]], dtype=int64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_table.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0b5ec0cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observed Values :-\n",
      " [[60 97]\n",
      " [33 54]]\n"
     ]
    }
   ],
   "source": [
    "#Observed Values\n",
    "Observed_Values = dataset_table.values \n",
    "print(f\"Observed Values :-\\n {Observed_Values}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e71ac3a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "val = stats.chi2_contingency(dataset_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "25e44bb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0,\n",
       " 1.0,\n",
       " 1,\n",
       " array([[59.84016393, 97.15983607],\n",
       "        [33.15983607, 53.84016393]]))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here we get an array of 2*2 matrix.\n",
    "val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "91a62227",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[59.84016393, 97.15983607],\n",
       "       [33.15983607, 53.84016393]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e251c929",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keep this array inside Expected_Values\n",
    "Expected_Values = val[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "90f5c20b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degree of Freedom: 1\n"
     ]
    }
   ],
   "source": [
    "# Since we have only 2 rows and columns in dataset table\n",
    "no_of_rows = len(dataset_table.iloc[0:2,0])\n",
    "no_of_columns = len(dataset_table.iloc[0,0:2])\n",
    "ddof = (no_of_rows-1) * (no_of_columns-1)\n",
    "print(f\"Degree of Freedom: {ddof}\")\n",
    "alpha = 0.05"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c439c32",
   "metadata": {},
   "source": [
    "Degree of freedom = (no_of_rows-1)*(no_of_columns-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "75fc8d2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_of_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bacf0c30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_of_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "892ad2bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import chi2\n",
    "\n",
    "# It will give 2 chi-square values for 1st and 2nd row\n",
    "chi_square = sum([(o-e)**2 / e for o,e in zip(Observed_Values,Expected_Values)]) \n",
    "\n",
    "chi_square_statistic = chi_square[0] + chi_square[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb105d3f",
   "metadata": {},
   "source": [
    "- Here we are zipping Observed_Values,Expected_Values using zip() function \n",
    "- since we have 2*2 matrix and then appling the chi-square formula \n",
    "- i.e Σ(observed-expected)^2/expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6d1d4b57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chi-square statistic: 0.001934818536627623\n"
     ]
    }
   ],
   "source": [
    "print(f\"chi-square statistic: {chi_square_statistic}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7da285c",
   "metadata": {},
   "source": [
    "## Method 1 w.r.t critical value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "33d61d09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "critical_value: 3.841458820694124\n"
     ]
    }
   ],
   "source": [
    "critical_value = chi2.ppf(q=1-alpha,df=ddof)\n",
    "print(f'critical_value: {critical_value}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ace03bef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retain H0,There is no relationship between 2 categorical variables\n"
     ]
    }
   ],
   "source": [
    "if chi_square_statistic >= critical_value:\n",
    "    print(f\"Reject H0,There is a relationship between 2 categorical variables\")\n",
    "else:\n",
    "    print(f\"Retain H0,There is no relationship between 2 categorical variables\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24d9fe26",
   "metadata": {},
   "source": [
    "## Method 2 w.r.t p value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9eb35520",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value: 0.964915107315732\n",
      "Significance level: 0.05\n",
      "Degree of Freedom: 1\n",
      "p-value: 0.964915107315732\n"
     ]
    }
   ],
   "source": [
    "p_value = 1-chi2.cdf(x=chi_square_statistic,df=ddof)\n",
    "print(f'p-value: {p_value}')\n",
    "print(f'Significance level: {alpha}')\n",
    "print(f'Degree of Freedom: {ddof}')\n",
    "print(f'p-value: {p_value}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f4949d37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retain H0,There is no relationship between 2 categorical variables\n"
     ]
    }
   ],
   "source": [
    "if p_value <= alpha:\n",
    "    print(\"Reject H0,There is a relationship between 2 categorical variables\")\n",
    "else:\n",
    "    print(\"Retain H0,There is no relationship between 2 categorical variables\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "324e9845",
   "metadata": {},
   "source": [
    "1) What is the use of performing this test?\n",
    "- To find the coorelation between two features which will help in feature selection."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42d77a9b",
   "metadata": {},
   "source": [
    "# T - Test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2fe4ba1",
   "metadata": {},
   "source": [
    "- One-sample T-test with Python\n",
    "- The test will tell us whether means of the sample and the population are different"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83c77e33",
   "metadata": {},
   "source": [
    "### One-sample t-test formula:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d88f986",
   "metadata": {},
   "source": [
    "- t = (x̄ - μ) / (s / sqrt(n))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f416486",
   "metadata": {},
   "source": [
    "- Where:\n",
    "\n",
    "- t is the test statistic\n",
    "- x̄ is the sample mean\n",
    "- μ is the hypothesized population mean\n",
    "- s is the sample standard deviation\n",
    "- n is the sample size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99ccc46a",
   "metadata": {},
   "source": [
    "## Two-sample t-test formula:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c732d5b2",
   "metadata": {},
   "source": [
    "- t = (x̄1 - x̄2) / (sqrt(s1^2/n1 + s2^2/n2))\n",
    "\n",
    "- where:\n",
    "- t is the test statistic\n",
    "- x̄1 and x̄2 are the sample means of the two groups being compared\n",
    "- s1 and s2 are the sample standard deviations of the two groups being compared\n",
    "- n1 and n2 are the sample sizes of the two groups being compared"
   ]
  },
  {
   "cell_type": "raw",
   "id": "fe12f050",
   "metadata": {},
   "source": [
    "The calculated t-value is then compared to a t-distribution table to determine the corresponding p-value. \n",
    "\n",
    "The p-value is compared to the significance level usually 0.05 \n",
    "\n",
    "To determine whether the difference between the groups is statistically significant or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "88eb63d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# population age\n",
    "ages = [10,20,35,50,28,40,55,18,16,55,30,25,43,18,30,28,14,24,16,17,32,35,26,27,65,18,43,23,21,20,19,70]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "451cb804",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10,\n",
       " 20,\n",
       " 35,\n",
       " 50,\n",
       " 28,\n",
       " 40,\n",
       " 55,\n",
       " 18,\n",
       " 16,\n",
       " 55,\n",
       " 30,\n",
       " 25,\n",
       " 43,\n",
       " 18,\n",
       " 30,\n",
       " 28,\n",
       " 14,\n",
       " 24,\n",
       " 16,\n",
       " 17,\n",
       " 32,\n",
       " 35,\n",
       " 26,\n",
       " 27,\n",
       " 65,\n",
       " 18,\n",
       " 43,\n",
       " 23,\n",
       " 21,\n",
       " 20,\n",
       " 19,\n",
       " 70]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "efcd28c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a31ceed5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30.34375\n"
     ]
    }
   ],
   "source": [
    "# Calulate the mean of ages\n",
    "import numpy as np\n",
    "ages_mean = np.mean(ages)\n",
    "print(ages_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d48dd10b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets take sample\n",
    "#random.choice:- Generates a random sample from a given 1-D array\n",
    "# Why are we performing t test here?--> Since sample sizeis 8 i.e <30\n",
    "\n",
    "sample_size = 8\n",
    "age_sample = np.random.choice(ages,sample_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f5e9e1f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([70, 20, 20, 14, 35, 65, 18, 14])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "469e92a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(age_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5c843413",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import library to perform t-test.\n",
    "from scipy.stats import ttest_1samp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "77c0304a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform t-test on sample and 30 is the mean of population data\n",
    "# Automatically it gives 2 values one is ttest and p_value\n",
    "# Here by default confidence interval is 95%. then significance value i.e α = 1 - 0.95 = 0.05\n",
    "\n",
    "ttest,p_value = ttest_1samp(age_sample,30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ef5f8a8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24691895664086197"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "80b315ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8120560627668317\n"
     ]
    }
   ],
   "source": [
    "print(p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d81fdfd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We fail to reject the null hypothesis\n"
     ]
    }
   ],
   "source": [
    "# Here since we are comparing p with 0.05 means α = 0.05 and C.I = 1- 0.05 = 0.95 i.e 95%\n",
    "# if we use want to take C.I = 85% then we need to compare p_value with 0.15.\n",
    "if p_value < 0.05:    # alpha value is 0.05 or 5%\n",
    "    print(\" We are rejecting null hypothesis\")\n",
    "else:\n",
    "    print(\"We fail to reject the null hypothesis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21fcdf2f",
   "metadata": {},
   "source": [
    "## Example of the age of students in a college and in Class A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "33e80dd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import math\n",
    "np.random.seed(6)\n",
    "\n",
    "# Here this both will create a set of data.\n",
    "school_ages = stats.poisson.rvs(loc=18,mu=35,size=1500)\n",
    "classA_ages = stats.poisson.rvs(loc=18,mu=30,size=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c0ac7b5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([62, 59, 44, ..., 45, 52, 50])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "school_ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "77772d96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([52, 46, 40, 40, 47, 50, 51, 45, 44, 52, 46, 53, 43, 44, 51, 50, 54,\n",
       "       42, 54, 45, 61, 53, 49, 46, 47])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classA_ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "da1daeb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53.303333333333335"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(school_ages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "74b76583",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48.2"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classA_ages.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e70fc0c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pass the sample and population mean as parameter for ttest function.\n",
    "# _ is just a placeholder here. Since ttest_1samp returns 2 variable.\n",
    "_, p_value = stats.ttest_1samp(a=classA_ages, popmean=school_ages.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f2004f19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.26936314797003e-05"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9a2078c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53.303333333333335"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "school_ages.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "646ac7f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " we are rejecting null hypothesis\n"
     ]
    }
   ],
   "source": [
    "if p_value < 0.05:    # alpha value is 0.05 or 5%\n",
    "    print(\" we are rejecting null hypothesis\")\n",
    "else:\n",
    "    print(\"we are accepting null hypothesis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ae3ae2d",
   "metadata": {},
   "source": [
    "## Two-sample T-test With Python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bedd0e0",
   "metadata": {},
   "source": [
    "- The Independent Samples t Test or 2-sample t-test compares the means of two independent groups in order to determine \n",
    "   whether there is statistical evidence that the associated population means are significantly different. \n",
    "\n",
    "- The Independent Samples t Test is a parametric test.\n",
    "\n",
    "- This test is also known as: Independent t Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2d8453f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.63333333333333"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here we are applying poission distr on class B. Since we need 2 samples.\n",
    "np.random.seed(12)\n",
    "ClassB_ages = stats.poisson.rvs(loc=18,mu=33,size=60)\n",
    "ClassB_ages.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6ca27ef2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([44, 47, 60, 65, 47, 52, 56, 56, 44, 48, 50, 47, 55, 49, 50, 50, 50,\n",
       "       41, 53, 47, 39, 50, 52, 43, 53, 51, 59, 53, 51, 46, 41, 53, 63, 53,\n",
       "       48, 65, 63, 54, 49, 53, 48, 45, 49, 59, 50, 51, 56, 45, 56, 43, 43,\n",
       "       47, 54, 48, 45, 41, 60, 48, 51, 49])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ClassB_ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6d67fb9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# perform 2-ttest on both Class A, and class B\n",
    "_, p_value = stats.ttest_ind(a=classA_ages,b=ClassB_ages,equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "78210ef1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06021969607248894"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1c4edfad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we are accepting null hypothesis\n"
     ]
    }
   ],
   "source": [
    "if p_value < 0.05:    # alpha value is 0.05 or 5%\n",
    "    print(\" we are rejecting null hypothesis\")\n",
    "else:\n",
    "    print(\"we are accepting null hypothesis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39f7ee93",
   "metadata": {},
   "source": [
    "## Paired T-test With Python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fca74709",
   "metadata": {},
   "source": [
    "- When you want to check how different samples from the same group are, you can go for a paired T-test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a6539cff",
   "metadata": {},
   "outputs": [],
   "source": [
    "weight1 = [25,30,28,35,28,34,26,29,30,26,28,32,31,30,45]\n",
    "weight2 = weight1+stats.norm.rvs(scale=5,loc=-1.25,size=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "37008127",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[25, 30, 28, 35, 28, 34, 26, 29, 30, 26, 28, 32, 31, 30, 45]\n",
      "[30.57926457 34.91022437 29.00444617 30.54295091 19.86201983 37.57873174\n",
      " 18.3299827  21.3771395  36.36420881 32.05941216 26.93827982 29.519014\n",
      " 26.42851213 30.50667769 41.32984284]\n"
     ]
    }
   ],
   "source": [
    "print(weight1)\n",
    "print(weight2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "983c059b",
   "metadata": {},
   "outputs": [],
   "source": [
    "weight_df = pd.DataFrame({\"weight_10\": np.array(weight1),\n",
    "                         \"weight_20\": np.array(weight2),\n",
    "                        \"weight_change\": np.array(weight2)-np.array(weight1)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "58d80080",
   "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>weight_10</th>\n",
       "      <th>weight_20</th>\n",
       "      <th>weight_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>30.579265</td>\n",
       "      <td>5.579265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30</td>\n",
       "      <td>34.910224</td>\n",
       "      <td>4.910224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>29.004446</td>\n",
       "      <td>1.004446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35</td>\n",
       "      <td>30.542951</td>\n",
       "      <td>-4.457049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>19.862020</td>\n",
       "      <td>-8.137980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>34</td>\n",
       "      <td>37.578732</td>\n",
       "      <td>3.578732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>26</td>\n",
       "      <td>18.329983</td>\n",
       "      <td>-7.670017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>29</td>\n",
       "      <td>21.377139</td>\n",
       "      <td>-7.622861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>30</td>\n",
       "      <td>36.364209</td>\n",
       "      <td>6.364209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>26</td>\n",
       "      <td>32.059412</td>\n",
       "      <td>6.059412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>28</td>\n",
       "      <td>26.938280</td>\n",
       "      <td>-1.061720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>32</td>\n",
       "      <td>29.519014</td>\n",
       "      <td>-2.480986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>31</td>\n",
       "      <td>26.428512</td>\n",
       "      <td>-4.571488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>30</td>\n",
       "      <td>30.506678</td>\n",
       "      <td>0.506678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>45</td>\n",
       "      <td>41.329843</td>\n",
       "      <td>-3.670157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    weight_10  weight_20  weight_change\n",
       "0          25  30.579265       5.579265\n",
       "1          30  34.910224       4.910224\n",
       "2          28  29.004446       1.004446\n",
       "3          35  30.542951      -4.457049\n",
       "4          28  19.862020      -8.137980\n",
       "5          34  37.578732       3.578732\n",
       "6          26  18.329983      -7.670017\n",
       "7          29  21.377139      -7.622861\n",
       "8          30  36.364209       6.364209\n",
       "9          26  32.059412       6.059412\n",
       "10         28  26.938280      -1.061720\n",
       "11         32  29.519014      -2.480986\n",
       "12         31  26.428512      -4.571488\n",
       "13         30  30.506678       0.506678\n",
       "14         45  41.329843      -3.670157"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c242d596",
   "metadata": {},
   "outputs": [],
   "source": [
    "# perform t test on weight1 and weight 2 and get the p_value\n",
    "_, p_value = stats.ttest_rel(a=weight1,b=weight2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b5cf99d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5732936534411279"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "76b6f7cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we are accepting null hypothesis\n"
     ]
    }
   ],
   "source": [
    "if p_value < 0.05:    # alpha value is 0.05 or 5%\n",
    "    print(\" we are rejecting null hypothesis\")\n",
    "else:\n",
    "    print(\"we are accepting null hypothesis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8536cf91",
   "metadata": {},
   "source": [
    "- Observation:Here it lies inside the Confidence Interval hence we are accepting the null hypothesis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc136ebb",
   "metadata": {},
   "source": [
    "# Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a7b6142f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "df = sns.load_dataset('iris')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e770d88c",
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "9fe8ec39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b7df2281",
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal_length</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.117570</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <td>-0.117570</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.428440</td>\n",
       "      <td>-0.366126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_length</th>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.428440</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <td>0.817941</td>\n",
       "      <td>-0.366126</td>\n",
       "      <td>0.962865</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sepal_length  sepal_width  petal_length  petal_width\n",
       "sepal_length      1.000000    -0.117570      0.871754     0.817941\n",
       "sepal_width      -0.117570     1.000000     -0.428440    -0.366126\n",
       "petal_length      0.871754    -0.428440      1.000000     0.962865\n",
       "petal_width       0.817941    -0.366126      0.962865     1.000000"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "05cacb2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x27453cc2bb0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "949ae6c6",
   "metadata": {},
   "source": [
    "## ANOVA Test(F-Test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "320fcf89",
   "metadata": {},
   "source": [
    "- The t-test works well when dealing with two groups, but sometimes we want to compare more than two groups at the same time.\n",
    "\n",
    "- For example, if we wanted to test whether petal_width age differs based on some categorical variable like species, we have to compare the means of each level or group the variable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5db5a9be",
   "metadata": {},
   "source": [
    "## One Way F-test(ANOVA) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bfebd09",
   "metadata": {},
   "source": [
    "- It tell whether two or more groups are similar or not based on their mean similarity and f-score.\n",
    "\n",
    "- Example : there are 3 different category of iris flowers and their petal width and need to check whether all 3 group are similar or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c97c79d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "df1=sns.load_dataset('iris')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d7390b87",
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "1730f080",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_anova = df1[['petal_width','species']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a287d23e",
   "metadata": {},
   "outputs": [],
   "source": [
    "grps = pd.unique(df_anova.species.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "87378d78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype=object)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e191ff4",
   "metadata": {},
   "source": [
    "- Observation:- Total there are 3 types of species of flower i.e 'setosa', 'versicolor' and'virginica'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "23890c43",
   "metadata": {},
   "outputs": [],
   "source": [
    "# based on groups we are displaying all the petal width using Dictionary comprehension\n",
    "d_data = {grp : df_anova['petal_width'] [df_anova.species == grp] for grp in grps}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f56ea9f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'setosa': 0     0.2\n",
       " 1     0.2\n",
       " 2     0.2\n",
       " 3     0.2\n",
       " 4     0.2\n",
       " 5     0.4\n",
       " 6     0.3\n",
       " 7     0.2\n",
       " 8     0.2\n",
       " 9     0.1\n",
       " 10    0.2\n",
       " 11    0.2\n",
       " 12    0.1\n",
       " 13    0.1\n",
       " 14    0.2\n",
       " 15    0.4\n",
       " 16    0.4\n",
       " 17    0.3\n",
       " 18    0.3\n",
       " 19    0.3\n",
       " 20    0.2\n",
       " 21    0.4\n",
       " 22    0.2\n",
       " 23    0.5\n",
       " 24    0.2\n",
       " 25    0.2\n",
       " 26    0.4\n",
       " 27    0.2\n",
       " 28    0.2\n",
       " 29    0.2\n",
       " 30    0.2\n",
       " 31    0.4\n",
       " 32    0.1\n",
       " 33    0.2\n",
       " 34    0.2\n",
       " 35    0.2\n",
       " 36    0.2\n",
       " 37    0.1\n",
       " 38    0.2\n",
       " 39    0.2\n",
       " 40    0.3\n",
       " 41    0.3\n",
       " 42    0.2\n",
       " 43    0.6\n",
       " 44    0.4\n",
       " 45    0.3\n",
       " 46    0.2\n",
       " 47    0.2\n",
       " 48    0.2\n",
       " 49    0.2\n",
       " Name: petal_width, dtype: float64,\n",
       " 'versicolor': 50    1.4\n",
       " 51    1.5\n",
       " 52    1.5\n",
       " 53    1.3\n",
       " 54    1.5\n",
       " 55    1.3\n",
       " 56    1.6\n",
       " 57    1.0\n",
       " 58    1.3\n",
       " 59    1.4\n",
       " 60    1.0\n",
       " 61    1.5\n",
       " 62    1.0\n",
       " 63    1.4\n",
       " 64    1.3\n",
       " 65    1.4\n",
       " 66    1.5\n",
       " 67    1.0\n",
       " 68    1.5\n",
       " 69    1.1\n",
       " 70    1.8\n",
       " 71    1.3\n",
       " 72    1.5\n",
       " 73    1.2\n",
       " 74    1.3\n",
       " 75    1.4\n",
       " 76    1.4\n",
       " 77    1.7\n",
       " 78    1.5\n",
       " 79    1.0\n",
       " 80    1.1\n",
       " 81    1.0\n",
       " 82    1.2\n",
       " 83    1.6\n",
       " 84    1.5\n",
       " 85    1.6\n",
       " 86    1.5\n",
       " 87    1.3\n",
       " 88    1.3\n",
       " 89    1.3\n",
       " 90    1.2\n",
       " 91    1.4\n",
       " 92    1.2\n",
       " 93    1.0\n",
       " 94    1.3\n",
       " 95    1.2\n",
       " 96    1.3\n",
       " 97    1.3\n",
       " 98    1.1\n",
       " 99    1.3\n",
       " Name: petal_width, dtype: float64,\n",
       " 'virginica': 100    2.5\n",
       " 101    1.9\n",
       " 102    2.1\n",
       " 103    1.8\n",
       " 104    2.2\n",
       " 105    2.1\n",
       " 106    1.7\n",
       " 107    1.8\n",
       " 108    1.8\n",
       " 109    2.5\n",
       " 110    2.0\n",
       " 111    1.9\n",
       " 112    2.1\n",
       " 113    2.0\n",
       " 114    2.4\n",
       " 115    2.3\n",
       " 116    1.8\n",
       " 117    2.2\n",
       " 118    2.3\n",
       " 119    1.5\n",
       " 120    2.3\n",
       " 121    2.0\n",
       " 122    2.0\n",
       " 123    1.8\n",
       " 124    2.1\n",
       " 125    1.8\n",
       " 126    1.8\n",
       " 127    1.8\n",
       " 128    2.1\n",
       " 129    1.6\n",
       " 130    1.9\n",
       " 131    2.0\n",
       " 132    2.2\n",
       " 133    1.5\n",
       " 134    1.4\n",
       " 135    2.3\n",
       " 136    2.4\n",
       " 137    1.8\n",
       " 138    1.8\n",
       " 139    2.1\n",
       " 140    2.4\n",
       " 141    2.3\n",
       " 142    1.9\n",
       " 143    2.3\n",
       " 144    2.5\n",
       " 145    2.3\n",
       " 146    1.9\n",
       " 147    2.0\n",
       " 148    2.3\n",
       " 149    1.8\n",
       " Name: petal_width, dtype: float64}"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e56c13d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform Anova test on 3 different species of iris dataset i.e 'setosa', 'versicolor' and 'virginica'\n",
    "F, p = stats.f_oneway(d_data['setosa'], d_data['versicolor'], d_data['virginica'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "24e0a63f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.169445839443116e-85\n"
     ]
    }
   ],
   "source": [
    "# p value\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "539d9660",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We reject null hypothesis\n"
     ]
    }
   ],
   "source": [
    "if p<0.05:\n",
    "    print(\"We reject null hypothesis\")\n",
    "else:\n",
    "    print(\"We accept null hypothesis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cb82211",
   "metadata": {},
   "source": [
    "- Conclusion:- Petal width is different in all the species."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca08dc99",
   "metadata": {},
   "source": [
    "# Z-Test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0fd27c2",
   "metadata": {},
   "source": [
    "- The z-test is a statistical test used to determine whether a sample mean is significantly different from a population mean.\n",
    "\n",
    "- when the population standard deviation is known. \n",
    "\n",
    "- It is a hypothesis test that compares a sample mean to a population mean.\n",
    "\n",
    "- Assuming that the distribution of the sample means follows a normal distribution."
   ]
  },
  {
   "cell_type": "raw",
   "id": "40ede030",
   "metadata": {},
   "source": [
    "The z-test formula is as follows:\n",
    "\n",
    "z = (x̄ - μ) / (σ / sqrt(n))\n",
    "\n",
    "where:\n",
    "\n",
    "z is the test statistic\n",
    "x̄ is the sample mean\n",
    "μ is the hypothesized population mean\n",
    "σ is the population standard deviation\n",
    "n is the sample size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f270c3d8",
   "metadata": {},
   "source": [
    "- The calculated z-value is then compared to a standard normal distribution table to determine the corresponding p-value. \n",
    "\n",
    "- The p-value is compared to the significance level (usually 0.05) to determine whether the difference between the sample mean and population mean is statistically significant or not.\n",
    "\n",
    "- The z-test is commonly used in quality control, medical research, and market research to evaluate the difference between a sample and population mean.\n",
    "\n",
    "- However, it is less commonly used than the t-test because it requires knowing the population standard deviation, which is often unknown in practice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "9192baea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "from numpy.random import randn\n",
    "from statsmodels.stats.weightstats import ztest\n",
    "\n",
    "# Using these values Generate a random array of 50 numbers having mean 110 and sd 15\n",
    "# Here n = 50, µ = 110, σ = 15, CI = 95%\n",
    "\n",
    "mean_iq = 110 # population mean\n",
    "sd_iq = 15/math.sqrt(50) # this is σ/√n\n",
    "alpha =0.05 # Significance value(α) = 1-0.95= 0.05\n",
    "null_mean =100 # Sample mean\n",
    "data = sd_iq*randn(50)+mean_iq # Generate data where n = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "7759c69f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.1213203435596424"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sd_iq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "bd35d865",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.3498344 , -1.4212769 , -1.16358834, -1.00667629,  0.05002176,\n",
       "        0.76543014, -0.02851488, -1.2056456 , -0.38154484,  0.56684364,\n",
       "        1.07733675, -0.94035872,  0.28360712, -0.39032048, -2.15412444,\n",
       "       -1.29468375, -0.56622069, -0.51770921, -0.39587831, -0.60369525,\n",
       "        0.36739289, -0.95901181, -1.59529672,  0.50752268, -0.61837052,\n",
       "        0.79079299, -0.83440503,  1.30946996, -1.23874208, -1.20273749,\n",
       "        0.6961466 ,  1.77898441, -0.79631728,  1.56983921,  1.52166351,\n",
       "        0.7899156 , -0.21722089, -2.18405985, -1.56726841, -0.80967026,\n",
       "        0.50049511, -0.19350985, -0.6642032 ,  0.08352685, -1.65842528,\n",
       "        0.38011359, -0.95667303,  1.26985899,  0.15051938, -0.76513136])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 50 data having mean = 0 and sd = 1.\n",
    "randn(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "46c0e84d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([106.79025721, 108.38030724, 110.52863984, 106.62750666,\n",
       "       107.92211173, 110.26041674, 113.58352968, 110.37706441,\n",
       "       110.06789502, 114.10096951, 107.74695688, 108.44585899,\n",
       "       111.78772446, 112.28411044, 110.97090825, 104.44321553,\n",
       "       111.56775404, 111.41598357, 110.09843486, 111.41296396,\n",
       "       112.98670976, 110.10850381, 108.01449749, 106.09866001,\n",
       "       110.13529511, 108.48544495, 108.80806263, 107.59767079,\n",
       "       110.58185908, 111.56110533, 110.9213292 , 107.62403402,\n",
       "       111.88605448, 110.66716998, 104.72214552, 111.26411306,\n",
       "       105.68128445, 107.58533997, 112.2435981 , 111.38473118,\n",
       "       108.63287936, 108.12589389, 110.73332426, 106.18835336,\n",
       "       110.87124667, 109.38094898, 111.55736234, 109.73378349,\n",
       "       107.69090586, 110.42899657])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "255512e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50,)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "87dec668",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean = 109.61, stdv = 2.22\n"
     ]
    }
   ],
   "source": [
    "# print mean and sd of data we generated\n",
    "print(f'mean = {np.mean(data):.2f}, stdv = {np.std(data):.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "1a229326",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We Reject Null Hypothesis\n"
     ]
    }
   ],
   "source": [
    "# now we perform the test. In this function, we passed data, in the value parameter\n",
    "# we passed mean value in the null hypothesis, in alternative hypothesis we check whether the\n",
    "# mean is larger\n",
    "\n",
    "ztest_Score, p_value= ztest(data,value = null_mean)\n",
    "# the function outputs a p_value and z-score corresponding to that value, we compare the\n",
    "# p-value with alpha, if it is greater than alpha then we do not null hypothesis\n",
    "# else we reject it.\n",
    "\n",
    "if(p_value < alpha):\n",
    "    print(\"We Reject Null Hypothesis\")\n",
    "else:\n",
    "    print(\"WE Fail to Reject NUll Hypothesis\")"
   ]
  }
 ],
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