11th_April_Statistics_Assignment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "451d0138",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[91, 111, 115, 103, 92, 121, 126, 120, 117, 118, 124, 107, 93, 118, 106, 90, 116, 116, 124, 115, 116, 93, 98, 93, 109, 95, 130, 122, 114, 107, 108, 117, 130, 125, 105, 98, 111, 120, 109, 117, 118, 114, 103, 94, 119, 113, 91, 117, 128, 122, 130, 92, 108, 92, 91, 123, 105, 120, 128, 103, 110, 99, 128, 114, 96, 120, 102, 129, 124, 92, 116, 115, 95, 101, 111, 94, 115, 103, 106, 118, 128, 130, 105, 108, 100, 106, 90, 93, 124, 114, 96, 121, 122, 115, 91, 108, 125, 112, 110, 109]\n",
      "Mean: 110.46\n",
      "Median: 111.5\n",
      "Mode: 115\n",
      "Weighted Mean: 109.76491862567812\n",
      "Geometric Mean: 109.81643688180642\n",
      "Harmonic Mean: 109.16108027743113\n",
      "Midrange: 110.0\n",
      "Trimmed Mean: 110.5875\n"
     ]
    }
   ],
   "source": [
    "#1 Generate a list of 100 integers containing values between 90 to 130 and store it in the variable `int_list`. After generating the list, find the following:\n",
    "#(i) Write a Python function to calculate the mean of a given list of numbers.Create a function to find the median of a list of numbers.\n",
    "#(ii) Develop a program to compute the mode of a list of integers.\n",
    "#(iii) Implement a function to calculate the weighted mean of a list of values and their corresponding weights.\n",
    "#(iv) Write a Python function to find the geometric mean of a list of positive numbers.\n",
    "#(v) Create a program to calculate the harmonic mean of a list of values.\n",
    "#(vi) Build a function to determine the midrange of a list of numbers (average of the minimum and maximum).\n",
    "#(vii) Implement a Python program to find the trimmed mean of a list, excluding a certain percentage of outliers.\n",
    "\n",
    "import random\n",
    "import statistics\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "from typing import List\n",
    "\n",
    "# Generate the list of 100 integers between 90 and 130\n",
    "int_list = [random.randint(90, 130) for _ in range(100)]\n",
    "print(int_list)\n",
    "\n",
    "# (i) Mean Calculation\n",
    "def calculate_mean(numbers: List[int]) -> float:\n",
    "    return sum(numbers) / len(numbers)\n",
    "\n",
    "# Median Calculation\n",
    "def calculate_median(numbers: List[int]) -> float:\n",
    "    sorted_numbers = sorted(numbers)\n",
    "    n = len(sorted_numbers)\n",
    "    if n % 2 == 0:\n",
    "        median = (sorted_numbers[n // 2 - 1] + sorted_numbers[n // 2]) / 2\n",
    "    else:\n",
    "        median = sorted_numbers[n // 2]\n",
    "    return median\n",
    "\n",
    "# (ii) Mode Calculation\n",
    "def calculate_mode(numbers: List[int]) -> int:\n",
    "    return statistics.mode(numbers)\n",
    "\n",
    "# (iii) Weighted Mean Calculation\n",
    "def calculate_weighted_mean(values: List[int], weights: List[int]) -> float:\n",
    "    weighted_sum = sum(value * weight for value, weight in zip(values, weights))\n",
    "    total_weight = sum(weights)\n",
    "    return weighted_sum / total_weight\n",
    "\n",
    "# (iv) Geometric Mean Calculation\n",
    "def calculate_geometric_mean(numbers: List[int]) -> float:\n",
    "    product = 1\n",
    "    for number in numbers:\n",
    "        product *= number\n",
    "    return product ** (1 / len(numbers))\n",
    "\n",
    "# (v) Harmonic Mean Calculation\n",
    "def calculate_harmonic_mean(numbers: List[int]) -> float:\n",
    "    return len(numbers) / sum(1 / number for number in numbers)\n",
    "\n",
    "# (vi) Midrange Calculation\n",
    "def calculate_midrange(numbers: List[int]) -> float:\n",
    "    return (max(numbers) + min(numbers)) / 2\n",
    "\n",
    "# (vii) Trimmed Mean Calculation\n",
    "def calculate_trimmed_mean(numbers: List[int], percentage: float) -> float:\n",
    "    return stats.trim_mean(numbers, proportiontocut=percentage)\n",
    "\n",
    "# Example usage\n",
    "mean = calculate_mean(int_list)\n",
    "median = calculate_median(int_list)\n",
    "mode = calculate_mode(int_list)\n",
    "# For weighted mean, we need weights. Let's generate random weights\n",
    "weights = [random.randint(1, 10) for _ in range(100)]\n",
    "weighted_mean = calculate_weighted_mean(int_list, weights)\n",
    "geometric_mean = calculate_geometric_mean(int_list)\n",
    "harmonic_mean = calculate_harmonic_mean(int_list)\n",
    "midrange = calculate_midrange(int_list)\n",
    "trimmed_mean = calculate_trimmed_mean(int_list, 0.1)  # 10% trimmed mean\n",
    "\n",
    "# Print results\n",
    "print(f\"Mean: {mean}\")\n",
    "print(f\"Median: {median}\")\n",
    "print(f\"Mode: {mode}\")\n",
    "print(f\"Weighted Mean: {weighted_mean}\")\n",
    "print(f\"Geometric Mean: {geometric_mean}\")\n",
    "print(f\"Harmonic Mean: {harmonic_mean}\")\n",
    "print(f\"Midrange: {midrange}\")\n",
    "print(f\"Trimmed Mean: {trimmed_mean}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "086ae146",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[256 265 249 243 239 237 224 250 293 250 295 244 236 266 283 259 253 255\n",
      " 205 279 283 202 247 295 214 297 222 244 286 221 263 270 279 247 200 296\n",
      " 216 236 290 287 251 257 290 231 212 208 262 224 278 284 245 239 243 262\n",
      " 207 240 297 244 213 269 226 214 298 220 227 250 231 283 252 258 232 212\n",
      " 210 214 202 300 217 250 204 225 245 224 242 229 231 287 282 251 212 219\n",
      " 248 269 203 295 239 201 240 295 229 275 224 222 229 289 242 207 201 229\n",
      " 248 252 239 287 262 292 258 275 220 216 205 238 219 291 282 244 299 221\n",
      " 214 286 247 201 228 240 211 222 202 225 234 214 249 218 256 242 226 227\n",
      " 275 252 212 240 273 277 244 241 204 245 247 240 292 255 256 218 264 204\n",
      " 208 250 228 209 232 291 250 202 209 236 231 260 234 208 239 286 243 267\n",
      " 267 206 256 294 249 254 223 256 281 217 288 232 200 247 281 269 206 268\n",
      " 232 241 249 256 262 212 235 252 235 222 215 214 233 242 208 224 270 260\n",
      " 297 275 257 201 265 214 271 268 230 267 218 214 226 221 288 254 263 243\n",
      " 254 212 299 222 218 267 284 205 212 230 224 223 260 281 267 206 215 236\n",
      " 241 254 284 282 262 238 253 279 206 209 292 207 260 278 246 287 243 211\n",
      " 258 252 206 290 207 276 265 239 235 235 261 247 215 254 210 241 287 241\n",
      " 238 215 266 243 296 242 216 222 230 242 230 212 236 218 262 295 236 257\n",
      " 226 200 211 227 229 224 221 237 204 239 234 242 297 288 208 215 234 281\n",
      " 294 264 261 248 232 267 211 277 268 224 277 252 229 216 256 273 202 300\n",
      " 207 220 295 283 242 223 213 259 243 247 284 291 287 296 216 242 244 250\n",
      " 258 242 280 263 252 263 211 256 298 230 238 278 267 284 275 296 211 282\n",
      " 287 276 242 242 252 203 225 217 204 277 263 257 214 296 221 225 224 204\n",
      " 298 281 226 254 207 218 299 239 282 235 267 277 205 215 229 220 265 255\n",
      " 200 248 220 261 203 228 253 219 203 230 274 292 294 216 259 205 228 241\n",
      " 299 279 247 238 247 248 283 298 270 278 238 264 300 212 270 210 225 219\n",
      " 238 271 287 271 277 230 231 243 281 221 298 256 292 276 263 289 237 250\n",
      " 248 276 281 206 207 281 223 283 223 258 220 230 282 293 293 286 297 257\n",
      " 240 235 213 200 228 245 282 268 273 233 268 249 227 233]\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Range of int_list2: 100\n",
      "Variance of int_list2: 799.6498359999999\n",
      "Standard Deviation of int_list2: 28.27808048648281\n",
      "IQR of int_list2: 46.0\n",
      "Coefficient of Variation of int_list2: 11.465233207029952%\n",
      "Mean Absolute Deviation of int_list2: 24.050248\n",
      "Quartile Deviation of int_list2: 23.0\n",
      "Range-based Coefficient of Dispersion of int_list2: 40.544595000040545%\n"
     ]
    }
   ],
   "source": [
    "#2. Generate a list of 500 integers containing values between 200 to 300 and store it in the variable `int_list2`.After generating the list, find the following:\n",
    "\n",
    "#(i) Compare the given list of visualization for the given data:\n",
    "\n",
    "    #1. Frequency & Gaussian distribution\n",
    "\n",
    "    #2. Frequency smoothened KDE plot\n",
    "\n",
    "    #3. Gaussian distribution & smoothened KDE plot write exact code\n",
    "    \n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate 500 integers between 200 and 300\n",
    "int_list2 = np.random.randint(200, 301, 500)\n",
    "print(int_list2)\n",
    "# Plotting the distribution\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Plot 1: Frequency plot\n",
    "plt.subplot(2, 2, 1)\n",
    "sns.histplot(int_list2, bins=20, kde=False, color='blue', edgecolor='black')\n",
    "plt.title('Frequency Plot')\n",
    "plt.xlabel('Values')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "# Plot 2: Gaussian distribution (assuming mean and std of the data)\n",
    "plt.subplot(2, 2, 2)\n",
    "sns.histplot(int_list2, bins=20, kde=True, color='orange', edgecolor='black', stat='density')\n",
    "plt.title('Gaussian Distribution')\n",
    "plt.xlabel('Values')\n",
    "plt.ylabel('Density')\n",
    "\n",
    "# Plot 3: Smoothed KDE plot\n",
    "plt.subplot(2, 2, 3)\n",
    "sns.kdeplot(int_list2, color='green', linewidth=2)\n",
    "plt.title('Smoothed KDE Plot')\n",
    "plt.xlabel('Values')\n",
    "plt.ylabel('Density')\n",
    "\n",
    "# Plot 4: Comparison plot\n",
    "plt.subplot(2, 2, 4)\n",
    "sns.histplot(int_list2, bins=20, kde=False, color='blue', edgecolor='black', label='Frequency')\n",
    "sns.histplot(int_list2, bins=20, kde=True, color='orange', edgecolor='black', stat='density', label='Gaussian')\n",
    "sns.kdeplot(int_list2, color='green', linewidth=2, label='KDE')\n",
    "plt.title('Comparison of Plots')\n",
    "plt.xlabel('Values')\n",
    "plt.ylabel('Density')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "  #(ii) Write a Python function to calculate the range of a given list of numbers.\n",
    "def calculate_range(nums):\n",
    "    return max(nums) - min(nums)\n",
    "\n",
    "# Example usage:\n",
    "range_of_int_list2 = calculate_range(int_list2)\n",
    "print(f\"Range of int_list2: {range_of_int_list2}\")\n",
    "\n",
    "\n",
    "  #(iii) Create a program to find the variance and standard deviation of a list of numbers.\n",
    "def calculate_variance_and_stddev(nums):\n",
    "    mean = np.mean(nums)\n",
    "    variance = np.var(nums)\n",
    "    stddev = np.std(nums)\n",
    "    return variance, stddev\n",
    "\n",
    "# Example usage:\n",
    "variance_int_list2, stddev_int_list2 = calculate_variance_and_stddev(int_list2)\n",
    "print(f\"Variance of int_list2: {variance_int_list2}\")\n",
    "print(f\"Standard Deviation of int_list2: {stddev_int_list2}\")\n",
    "\n",
    "\n",
    "  #(iv) Implement a function to compute the interquartile range (IQR) of a list of values.\n",
    "def calculate_iqr(nums):\n",
    "    q75, q25 = np.percentile(nums, [75 ,25])\n",
    "    return q75 - q25\n",
    "\n",
    "# Example usage:\n",
    "iqr_int_list2 = calculate_iqr(int_list2)\n",
    "print(f\"IQR of int_list2: {iqr_int_list2}\")\n",
    "    \n",
    "#(v) Build a program to calculate the coefficient of variation for a dataset.\n",
    "def calculate_coefficient_of_variation(nums):\n",
    "    mean = np.mean(nums)\n",
    "    stddev = np.std(nums)\n",
    "    return (stddev / mean) * 100  # Multiply by 100 to get percentage\n",
    "\n",
    "# Example usage:\n",
    "cv_int_list2 = calculate_coefficient_of_variation(int_list2)\n",
    "print(f\"Coefficient of Variation of int_list2: {cv_int_list2}%\")\n",
    "\n",
    "\n",
    "  #(vi) Write a Python function to find the mean absolute deviation (MAD) of a list of numbers.\n",
    "def calculate_mean_absolute_deviation(nums):\n",
    "    mean = np.mean(nums)\n",
    "    mad = np.mean(np.abs(nums - mean))\n",
    "    return mad\n",
    "\n",
    "# Example usage:\n",
    "mad_int_list2 = calculate_mean_absolute_deviation(int_list2)\n",
    "print(f\"Mean Absolute Deviation of int_list2: {mad_int_list2}\")\n",
    "\n",
    "\n",
    "  #(vii) Create a program to calculate the quartile deviation of a list of values.\n",
    "def calculate_quartile_deviation(nums):\n",
    "    q75, q25 = np.percentile(nums, [75 ,25])\n",
    "    return (q75 - q25) / 2\n",
    "\n",
    "# Example usage:\n",
    "qd_int_list2 = calculate_quartile_deviation(int_list2)\n",
    "print(f\"Quartile Deviation of int_list2: {qd_int_list2}\")\n",
    "\n",
    "\n",
    "  #(viii) Implement a function to find the range-based coefficient of dispersion for a dataset.\n",
    "def calculate_range_based_cod(nums):\n",
    "    range_val = max(nums) - min(nums)\n",
    "    mean = np.mean(nums)\n",
    "    return (range_val / mean) * 100  # Multiply by 100 to get percentage\n",
    "\n",
    "# Example usage:\n",
    "cod_int_list2 = calculate_range_based_cod(int_list2)\n",
    "print(f\"Range-based Coefficient of Dispersion of int_list2: {cod_int_list2}%\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "97f4cd65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expected value: 3.5\n",
      "Variance: 2.9166666666666665\n"
     ]
    }
   ],
   "source": [
    "#3 Write a Python class representing a discrete random variable with methods to calculate its expected value and variance.\n",
    "class DiscreteRandomVariable:\n",
    "    def __init__(self, outcomes, probabilities):\n",
    "        \"\"\"\n",
    "        Initialize a discrete random variable with outcomes and corresponding probabilities.\n",
    "\n",
    "        :param outcomes: List or tuple of outcomes.\n",
    "        :param probabilities: List or tuple of probabilities corresponding to outcomes.\n",
    "        \"\"\"\n",
    "        if len(outcomes) != len(probabilities):\n",
    "            raise ValueError(\"Lengths of outcomes and probabilities must be the same.\")\n",
    "        \n",
    "        self.outcomes = outcomes\n",
    "        self.probabilities = probabilities\n",
    "\n",
    "    def expected_value(self):\n",
    "        \"\"\"\n",
    "        Calculate the expected value (mean) of the discrete random variable.\n",
    "\n",
    "        :return: Expected value of the discrete random variable.\n",
    "        \"\"\"\n",
    "        expected_value = sum(x * p for x, p in zip(self.outcomes, self.probabilities))\n",
    "        return expected_value\n",
    "\n",
    "    def variance(self):\n",
    "        \"\"\"\n",
    "        Calculate the variance of the discrete random variable.\n",
    "\n",
    "        :return: Variance of the discrete random variable.\n",
    "        \"\"\"\n",
    "        mean = self.expected_value()\n",
    "        variance = sum(p * (x - mean)**2 for x, p in zip(self.outcomes, self.probabilities))\n",
    "        return variance\n",
    "\n",
    "# Example usage:\n",
    "outcomes = [1, 2, 3, 4, 5, 6]  # Example outcomes of a dice roll\n",
    "probabilities = [1/6, 1/6, 1/6, 1/6, 1/6, 1/6]  # Example probabilities for a fair dice\n",
    "dice_variable = DiscreteRandomVariable(outcomes, probabilities)\n",
    "\n",
    "print(\"Expected value:\", dice_variable.expected_value())\n",
    "print(\"Variance:\", dice_variable.variance())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7460c59f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of simulations: 100000\n",
      "Expected value: 3.49438\n",
      "Variance: 2.9049574651746517\n"
     ]
    }
   ],
   "source": [
    "#4 Implement a program to simulate the rolling o6 a 6air six-sided die and calculate the expected value and variance o6 the outcomes.\n",
    "import random\n",
    "import statistics\n",
    "\n",
    "# Function to simulate rolling a fair six-sided die\n",
    "def roll_die():\n",
    "    return random.randint(1, 6)\n",
    "\n",
    "# Number of simulations\n",
    "num_simulations = 100000\n",
    "\n",
    "# List to store outcomes of simulations\n",
    "outcomes = []\n",
    "\n",
    "# Simulate rolling the die num_simulations times\n",
    "for _ in range(num_simulations):\n",
    "    outcome = roll_die()\n",
    "    outcomes.append(outcome)\n",
    "\n",
    "# Calculate the expected value\n",
    "expected_value = sum(outcomes) / num_simulations\n",
    "\n",
    "# Calculate the variance\n",
    "variance = statistics.variance(outcomes)\n",
    "\n",
    "# Print results\n",
    "print(f\"Number of simulations: {num_simulations}\")\n",
    "print(f\"Expected value: {expected_value}\")\n",
    "print(f\"Variance: {variance}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3cc8b662",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Binomial distribution: Mean = 5.0001, Variance = 2.4434999900000003\n",
      "Poisson distribution: Mean = 2.9925, Variance = 2.95944375\n"
     ]
    }
   ],
   "source": [
    "#5 Create a Python function to generate random samples from a given probability distribution (e.g.,binomial, Poisson) and calculate their mean and variance.\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "def generate_samples_distribution(distribution, params, sample_size):\n",
    "    \"\"\"\n",
    "    Generate random samples from a given probability distribution and calculate mean and variance.\n",
    "\n",
    "    Parameters:\n",
    "    - distribution: A string indicating the distribution ('binomial' or 'poisson').\n",
    "    - params: A tuple containing parameters specific to the distribution:\n",
    "        - For 'binomial': (n, p) where n is the number of trials and p is the probability of success.\n",
    "        - For 'poisson': (lam,) where lam is the rate parameter.\n",
    "    - sample_size: Number of samples to generate.\n",
    "\n",
    "    Returns:\n",
    "    - mean: Mean of the generated samples.\n",
    "    - variance: Variance of the generated samples.\n",
    "    \"\"\"\n",
    "    if distribution == 'binomial':\n",
    "        n, p = params\n",
    "        samples = np.random.binomial(n, p, sample_size)\n",
    "    elif distribution == 'poisson':\n",
    "        lam = params[0]\n",
    "        samples = np.random.poisson(lam, sample_size)\n",
    "    else:\n",
    "        raise ValueError(\"Unsupported distribution. Choose 'binomial' or 'poisson'.\")\n",
    "\n",
    "    mean = np.mean(samples)\n",
    "    variance = np.var(samples)\n",
    "\n",
    "    return mean, variance\n",
    "\n",
    "# Example usage:\n",
    "\n",
    "# Generate samples from a binomial distribution with n=10, p=0.5\n",
    "mean_binomial, var_binomial = generate_samples_distribution('binomial', (10, 0.5), 10000)\n",
    "print(f\"Binomial distribution: Mean = {mean_binomial}, Variance = {var_binomial}\")\n",
    "\n",
    "# Generate samples from a Poisson distribution with lambda=3.0\n",
    "mean_poisson, var_poisson = generate_samples_distribution('poisson', (3.0,), 10000)\n",
    "print(f\"Poisson distribution: Mean = {mean_poisson}, Variance = {var_poisson}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6cc5d058",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated normal distribution with mean = 0, std_dev = 1, sample_size = 10000:\n",
      "Mean: -0.02129446874616618\n",
      "Variance: 0.9877331697197904\n",
      "Standard Deviation: 0.9938476592113051\n"
     ]
    }
   ],
   "source": [
    "#6 Write a Python script to generate random numbers from a Gaussian (normal) distribution and compute the mean, variance, and standard deviation of the samples.\n",
    "import numpy as np\n",
    "\n",
    "def generate_normal_samples(mean, std_dev, sample_size):\n",
    "    \"\"\"\n",
    "    Generate random samples from a Gaussian (normal) distribution and calculate mean, variance, and standard deviation.\n",
    "\n",
    "    Parameters:\n",
    "    - mean: Mean of the normal distribution.\n",
    "    - std_dev: Standard deviation of the normal distribution.\n",
    "    - sample_size: Number of samples to generate.\n",
    "\n",
    "    Returns:\n",
    "    - mean_sample: Mean of the generated samples.\n",
    "    - variance_sample: Variance of the generated samples.\n",
    "    - std_dev_sample: Standard deviation of the generated samples.\n",
    "    \"\"\"\n",
    "    samples = np.random.normal(mean, std_dev, sample_size)\n",
    "    mean_sample = np.mean(samples)\n",
    "    variance_sample = np.var(samples)\n",
    "    std_dev_sample = np.std(samples)\n",
    "\n",
    "    return mean_sample, variance_sample, std_dev_sample\n",
    "\n",
    "# Example usage:\n",
    "\n",
    "mean = 0    # Mean of the normal distribution\n",
    "std_dev = 1 # Standard deviation of the normal distribution\n",
    "sample_size = 10000\n",
    "\n",
    "mean_sample, variance_sample, std_dev_sample = generate_normal_samples(mean, std_dev, sample_size)\n",
    "\n",
    "print(f\"Generated normal distribution with mean = {mean}, std_dev = {std_dev}, sample_size = {sample_size}:\")\n",
    "print(f\"Mean: {mean_sample}\")\n",
    "print(f\"Variance: {variance_sample}\")\n",
    "print(f\"Standard Deviation: {std_dev_sample}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b96f24c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness of total_bill: 1.1262346334818638\n",
      "Skewness of tip: 1.4564266884221506\n",
      "Total_bill column is Positive Skewness\n",
      "Tip column is Positive Skewness\n",
      "Covariance between total_bill and tip: 8.323501629224854\n",
      "Pearson correlation coefficient between total_bill and tip: 0.6757341092113646\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#7 Use seaborn library to load tips dataset. Find the following from the dataset for the columns total_bill and tip`:\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import skew\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the tips dataset\n",
    "tips = sns.load_dataset(\"tips\")\n",
    "tips\n",
    "\n",
    "#(i) Write a Python function that calculates their skewness.\n",
    "def calculate_skewness(column):\n",
    "    return skew(column)\n",
    "\n",
    "# Calculate skewness for 'total_bill' and 'tip'\n",
    "total_bill_skewness = calculate_skewness(tips['total_bill'])\n",
    "tip_skewness = calculate_skewness(tips['tip'])\n",
    "\n",
    "print(f\"Skewness of total_bill: {total_bill_skewness}\")\n",
    "print(f\"Skewness of tip: {tip_skewness}\")\n",
    "\n",
    "\n",
    "#(ii) Create a program that determines whether the columns exhibit positive skewness, negative skewness, or is approximately symmetric.\n",
    "def skewness_type(skewness_value):\n",
    "    if skewness_value > 0:\n",
    "        return \"Positive Skewness\"\n",
    "    elif skewness_value < 0:\n",
    "        return \"Negative Skewness\"\n",
    "    else:\n",
    "        return \"Approximately Symmetric\"\n",
    "\n",
    "# Determine skewness type for 'total_bill' and 'tip'\n",
    "total_bill_skewness_type = skewness_type(total_bill_skewness)\n",
    "tip_skewness_type = skewness_type(tip_skewness)\n",
    "\n",
    "print(f\"Total_bill column is {total_bill_skewness_type}\")\n",
    "print(f\"Tip column is {tip_skewness_type}\")\n",
    "\n",
    "#(iii) Write a function that calculates the covariance between two columns.\n",
    "def calculate_covariance(column1, column2):\n",
    "    return np.cov(column1, column2)[0, 1]\n",
    "\n",
    "# Calculate covariance between 'total_bill' and 'tip'\n",
    "covariance = calculate_covariance(tips['total_bill'], tips['tip'])\n",
    "\n",
    "print(f\"Covariance between total_bill and tip: {covariance}\")\n",
    "\n",
    "\n",
    "#(iv) Implement a Python program that calculates the Pearson correlation coefficient between two columns.\n",
    "def calculate_pearson_correlation(column1, column2):\n",
    "    return np.corrcoef(column1, column2)[0, 1]\n",
    "\n",
    "# Calculate Pearson correlation coefficient between 'total_bill' and 'tip'\n",
    "pearson_correlation = calculate_pearson_correlation(tips['total_bill'], tips['tip'])\n",
    "\n",
    "print(f\"Pearson correlation coefficient between total_bill and tip: {pearson_correlation}\")\n",
    "\n",
    "\n",
    "#(v) Write a script to visualize the correlation between two specific columns in a Pandas DataFrame using scatter plot.\n",
    "def plot_scatter(data, column1, column2):\n",
    "    \"\"\"\n",
    "    Plot a scatter plot to visualize the correlation between two specified columns in the DataFrame.\n",
    "    \n",
    "    Parameters:\n",
    "    data (pd.DataFrame): The DataFrame containing the data.\n",
    "    column1 (str): The name of the first column.\n",
    "    column2 (str): The name of the second column.\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    plt.scatter(data[column1], data[column2], alpha=0.5)\n",
    "    plt.title(f'Scatter Plot of {column1} vs {column2}')\n",
    "    plt.xlabel(column1)\n",
    "    plt.ylabel(column2)\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "# Plot scatter plot for 'total_bill' and 'tip'\n",
    "plot_scatter(tips, 'total_bill', 'tip')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "da9ea4f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24197072451914337"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#8 Write a Python function to calculate the probability density function (PDF) o6 a continuous random variable for a given normal distribution.\n",
    "import numpy as np\n",
    "\n",
    "def normal_pdf(x, mu, sigma):\n",
    "    \"\"\"\n",
    "    Calculate the PDF of a normal distribution at a given value x.\n",
    "    \n",
    "    Parameters:\n",
    "    x (float): The value at which to evaluate the PDF.\n",
    "    mu (float): The mean of the normal distribution.\n",
    "    sigma (float): The standard deviation of the normal distribution.\n",
    "    \n",
    "    Returns:\n",
    "    float: The PDF value at x.\n",
    "    \"\"\"\n",
    "    coefficient = 1 / (np.sqrt(2 * np.pi) * sigma)\n",
    "    exponent = np.exp(-((x - mu) ** 2) / (2 * sigma ** 2))\n",
    "    return coefficient * exponent\n",
    "\n",
    "# Example usage\n",
    "x_value = 1.0\n",
    "mean = 0.0\n",
    "std_dev = 1.0\n",
    "\n",
    "pdf_value = normal_pdf(x_value, mean, std_dev)\n",
    "pdf_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d3d1ef16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3934693402873666"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#9 Create a program to calculate the cumulative distribution function (CDF) of exponential distribution.\n",
    "import numpy as np\n",
    "\n",
    "def exponential_cdf(x, lam):\n",
    "    \"\"\"\n",
    "    Calculate the CDF of an exponential distribution at a given value x.\n",
    "    \n",
    "    Parameters:\n",
    "    x (float): The value at which to evaluate the CDF.\n",
    "    lam (float): The rate parameter (lambda) of the exponential distribution.\n",
    "    \n",
    "    Returns:\n",
    "    float: The CDF value at x.\n",
    "    \"\"\"\n",
    "    if x < 0:\n",
    "        return 0.0\n",
    "    return 1 - np.exp(-lam * x)\n",
    "\n",
    "# Example usage\n",
    "x_value = 1.0\n",
    "rate_param = 0.5\n",
    "\n",
    "cdf_value = exponential_cdf(x_value, rate_param)\n",
    "cdf_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "66a2a36c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1804470443154836"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#10 Write a Python 6unction to calculate the probability mass function (PMF) of Poisson distribution.\n",
    "import numpy as np\n",
    "import math\n",
    "\n",
    "def poisson_pmf(k, lam):\n",
    "    \"\"\"\n",
    "    Calculate the PMF of a Poisson distribution for a given number of occurrences k.\n",
    "    \n",
    "    Parameters:\n",
    "    k (int): The number of occurrences.\n",
    "    lam (float): The average rate (lambda) of occurrences.\n",
    "    \n",
    "    Returns:\n",
    "    float: The PMF value for k occurrences.\n",
    "    \"\"\"\n",
    "    if k < 0 or not isinstance(k, int):\n",
    "        raise ValueError(\"k must be a non-negative integer.\")\n",
    "    return (lam ** k * np.exp(-lam)) / math.factorial(k)\n",
    "\n",
    "# Example usage\n",
    "k_value = 3\n",
    "rate_param = 2.0\n",
    "\n",
    "pmf_value = poisson_pmf(k_value, rate_param)\n",
    "pmf_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4fd25260",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.05, 0.07, 1.883108942886774, 0.029842802766213095)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#11 A company wants to test if a new website layout leads to a higher conversion rate (percentage of visitors who make a purchase). \n",
    "#They collect data from the old and new layouts to compare To generate the data use the following command:\n",
    "\n",
    "\"\"\"python\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# 50 purchases out of 1000 visitors\n",
    "\n",
    "old_layout = np.array([1] * 50 + [0] * 950)\n",
    "\n",
    "# 70 purchases out of 1000 visitors  \n",
    "new_layout = np.array([1] * 70 + [0] * 930)\n",
    "  ```\n",
    "Apply z-test to find which layout is successful\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "\n",
    "# Generate the data\n",
    "old_layout = np.array([1] * 50 + [0] * 950)\n",
    "new_layout = np.array([1] * 70 + [0] * 930)\n",
    "\n",
    "# Conversion rates\n",
    "p1 = np.mean(old_layout)\n",
    "p2 = np.mean(new_layout)\n",
    "\n",
    "# Sample sizes\n",
    "n1 = len(old_layout)\n",
    "n2 = len(new_layout)\n",
    "\n",
    "# Pooled proportion\n",
    "p_hat = (np.sum(old_layout) + np.sum(new_layout)) / (n1 + n2)\n",
    "\n",
    "# Standard Error\n",
    "SE = np.sqrt(p_hat * (1 - p_hat) * (1/n1 + 1/n2))\n",
    "\n",
    "# Z-score\n",
    "z = (p2 - p1) / SE\n",
    "\n",
    "# p-value (one-tailed test)\n",
    "p_value = 1 - norm.cdf(z)\n",
    "\n",
    "p1, p2, z, p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7144eb6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.8, 2.616188916046478, 4.593190894944668, 1.6448536269514722)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#12 A tutoring service claims that its program improves students' exam scores. A sample of students who participated in the program was taken, and their scores before and after the program were recorded.\n",
    "\"\"\"Use the below code to generate samples of respective arrays of marks:\n",
    "python\n",
    "before_program = np.array([75, 80, 85, 70, 90, 78, 92, 88, 82, 87])\n",
    "after_program = np.array([80, 85, 90, 80, 92, 80, 95, 90, 85, 88])\n",
    "Use z-test to find if the claims made by tutor are true or false\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "\n",
    "# Sample data\n",
    "before_program = np.array([75, 80, 85, 70, 90, 78, 92, 88, 82, 87])\n",
    "after_program = np.array([80, 85, 90, 80, 92, 80, 95, 90, 85, 88])\n",
    "\n",
    "# Step 2: Calculate the differences\n",
    "differences = after_program - before_program\n",
    "\n",
    "# Step 3: Calculate the mean and standard deviation of the differences\n",
    "mean_difference = np.mean(differences)\n",
    "std_difference = np.std(differences, ddof=1)\n",
    "\n",
    "# Step 4: Calculate the test statistic\n",
    "n = len(differences)\n",
    "z = mean_difference / (std_difference / np.sqrt(n))\n",
    "\n",
    "# Step 5: Find the critical value for a one-tailed test at alpha = 0.05\n",
    "alpha = 0.05\n",
    "critical_value = stats.norm.ppf(1 - alpha)\n",
    "\n",
    "# Output the results\n",
    "mean_difference, std_difference, z, critical_value\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "70b30ec3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-10.1, 3.178049716414141, -10.049875621120888, -1.6448536269514729)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#13 A pharmaceutical company wants to determine if a new drug is effective in reducing blood pressure. They conduct a study and record blood pressure measurements before and aNter administering the drug.\n",
    "\"\"\"Use the below code to generate samples of respective arrays of blood pressure:\n",
    "python\n",
    "before_drug = np.array([145, 150, 140, 135, 155, 160, 152, 148, 130, 138])\n",
    "after_drug = np.array([130, 140, 132, 128, 145, 148, 138, 136, 125, 130])\n",
    "Implement z-test to find if the drug really works or not.\"\"\"\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "\n",
    "# Sample data\n",
    "before_drug = np.array([145, 150, 140, 135, 155, 160, 152, 148, 130, 138])\n",
    "after_drug = np.array([130, 140, 132, 128, 145, 148, 138, 136, 125, 130])\n",
    "\n",
    "# Step 2: Calculate the differences\n",
    "differences = after_drug - before_drug\n",
    "\n",
    "# Step 3: Calculate the mean and standard deviation of the differences\n",
    "mean_difference = np.mean(differences)\n",
    "std_difference = np.std(differences, ddof=1)\n",
    "\n",
    "# Step 4: Calculate the test statistic\n",
    "n = len(differences)\n",
    "z = mean_difference / (std_difference / np.sqrt(n))\n",
    "\n",
    "# Step 5: Find the critical value for a one-tailed test at alpha = 0.05\n",
    "alpha = 0.05\n",
    "critical_value = stats.norm.ppf(alpha)\n",
    "\n",
    "# Output the results\n",
    "mean_difference, std_difference, z, critical_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e93ec1ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.57, 0.4270050741306634, -3.184457226042963, -1.6448536269514729)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#14 A customer service department claims that their average response time is less than 5 minutes A sample of recent customer interactions was taken, and the response times were recorded.\n",
    "#Implement the below code to generate the array of response time:\n",
    "\"\"\"python\n",
    "response_times = np.array([4.3, 3.8, 5.1, 4.9, 4.7, 4.2, 5.2, 4.5, 4.6, 4.4])\n",
    "Implement z-test to find the claims made by customer service department are true or false.\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "\n",
    "# Sample data\n",
    "response_times = np.array([4.3, 3.8, 5.1, 4.9, 4.7, 4.2, 5.2, 4.5, 4.6, 4.4])\n",
    "\n",
    "# Step 2: Calculate the sample mean and standard deviation\n",
    "sample_mean = np.mean(response_times)\n",
    "sample_std = np.std(response_times, ddof=1)\n",
    "\n",
    "# Step 3: Calculate the test statistic\n",
    "n = len(response_times)\n",
    "mu = 5  # Claimed population mean\n",
    "z = (sample_mean - mu) / (sample_std / np.sqrt(n))\n",
    "\n",
    "# Step 4: Find the critical value for a one-tailed test at alpha = 0.05\n",
    "alpha = 0.05\n",
    "critical_value = stats.norm.ppf(alpha)\n",
    "\n",
    "# Output the results\n",
    "sample_mean, sample_std, z, critical_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7e04c8df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-7.298102156175071, 18, 8.833437608301987e-07)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#15 A company is testing two different website layouts to see which one leads to higher click-through rates\n",
    "#Write a Python function to perform an A/B test analysis, including calculating the t-statistic, degrees of freedom, and p-value.\n",
    "\"\"\"Use the following data:\n",
    "python\n",
    "layout_a_clicks = [28, 32, 33, 29, 31, 34, 30, 35, 36, 37]\n",
    "layout_b_clicks = [40, 41, 38, 42, 39, 44, 43, 41, 45, 47]\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "\n",
    "def ab_test(layout_a_clicks, layout_b_clicks):\n",
    "    # Calculate the sample means\n",
    "    mean_a = np.mean(layout_a_clicks)\n",
    "    mean_b = np.mean(layout_b_clicks)\n",
    "    \n",
    "    # Calculate the sample standard deviations\n",
    "    std_a = np.std(layout_a_clicks, ddof=1)\n",
    "    std_b = np.std(layout_b_clicks, ddof=1)\n",
    "    \n",
    "    # Calculate the sample sizes\n",
    "    n_a = len(layout_a_clicks)\n",
    "    n_b = len(layout_b_clicks)\n",
    "    \n",
    "    # Calculate the pooled standard deviation\n",
    "    pooled_std = np.sqrt(((std_a ** 2) / n_a) + ((std_b ** 2) / n_b))\n",
    "    \n",
    "    # Calculate the t-statistic\n",
    "    t_statistic = (mean_a - mean_b) / pooled_std\n",
    "    \n",
    "    # Calculate the degrees of freedom\n",
    "    df = n_a + n_b - 2\n",
    "    \n",
    "    # Calculate the p-value\n",
    "    p_value = stats.t.sf(np.abs(t_statistic), df) * 2  # two-tailed test\n",
    "    \n",
    "    return t_statistic, df, p_value\n",
    "\n",
    "# Sample data\n",
    "layout_a_clicks = [28, 32, 33, 29, 31, 34, 30, 35, 36, 37]\n",
    "layout_b_clicks = [40, 41, 38, 42, 39, 44, 43, 41, 45, 47]\n",
    "\n",
    "# Perform the A/B test\n",
    "t_statistic, df, p_value = ab_test(layout_a_clicks, layout_b_clicks)\n",
    "\n",
    "# Output the results\n",
    "t_statistic, df, p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "989a2c57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-4.140480986208661, 17.866770765582338, 0.00031146144727346535)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#17 A pharmaceutical company wants to determine if a new drug is more effective than an existing drug in\n",
    "#reducing cholesterol levels Create a program to analyze the clinical trial data and calculate the statistic and p-value for the treatment effect.\n",
    "\"\"\"Use the following data of cholestrol level:\n",
    "python\n",
    "existing_drug_levels = [180, 182, 175, 185, 178, 176, 172, 184, 179, 183]\n",
    "new_drug_levels = [170, 172, 165, 168, 175, 173, 170, 178, 172, 176]\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "\n",
    "def drug_effectiveness_test(existing_drug_levels, new_drug_levels):\n",
    "    # Calculate means and standard deviations\n",
    "    mean_existing = np.mean(existing_drug_levels)\n",
    "    mean_new = np.mean(new_drug_levels)\n",
    "    \n",
    "    std_existing = np.std(existing_drug_levels, ddof=1)\n",
    "    std_new = np.std(new_drug_levels, ddof=1)\n",
    "    \n",
    "    n_existing = len(existing_drug_levels)\n",
    "    n_new = len(new_drug_levels)\n",
    "    \n",
    "    # Calculate the t-statistic\n",
    "    t_statistic = (mean_new - mean_existing) / np.sqrt((std_new**2 / n_new) + (std_existing**2 / n_existing))\n",
    "    \n",
    "    # Calculate the degrees of freedom\n",
    "    df = ((std_new**2 / n_new) + (std_existing**2 / n_existing))**2 / ((std_new**2 / n_new)**2 / (n_new - 1) + (std_existing**2 / n_existing)**2 / (n_existing - 1))\n",
    "    \n",
    "    # Calculate the p-value\n",
    "    p_value = stats.t.cdf(t_statistic, df)\n",
    "    \n",
    "    return t_statistic, df, p_value\n",
    "\n",
    "# Sample data\n",
    "existing_drug_levels = [180, 182, 175, 185, 178, 176, 172, 184, 179, 183]\n",
    "new_drug_levels = [170, 172, 165, 168, 175, 173, 170, 178, 172, 176]\n",
    "\n",
    "# Perform the test\n",
    "t_statistic, df, p_value = drug_effectiveness_test(existing_drug_levels, new_drug_levels)\n",
    "\n",
    "# Output the results\n",
    "t_statistic, df, p_value\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5b77a7ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.428408839657611, 9, 0.0016509548165795493)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#18 A school district introduces an educational intervention program to improve math scores Write a Python function to analyze pre- and post-intervention test scores, calculating the t-statistic and p value to determine if the intervention had a significant impact.\n",
    "\"\"\"Use the following data of test score:\n",
    "  ```python\n",
    "  pre_intervention_scores = [80, 85, 90, 75, 88, 82, 92, 78, 85, 87]\n",
    "  post_intervention_scores = [90, 92, 88, 92, 95, 91, 96, 93, 89, 93]\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "\n",
    "def paired_t_test(pre_scores, post_scores):\n",
    "    # Calculate the differences\n",
    "    differences = np.array(post_scores) - np.array(pre_scores)\n",
    "    \n",
    "    # Calculate mean and standard deviation of the differences\n",
    "    mean_diff = np.mean(differences)\n",
    "    std_diff = np.std(differences, ddof=1)\n",
    "    \n",
    "    # Sample size\n",
    "    n = len(differences)\n",
    "    \n",
    "    # Calculate the t-statistic\n",
    "    t_statistic = mean_diff / (std_diff / np.sqrt(n))\n",
    "    \n",
    "    # Degrees of freedom\n",
    "    df = n - 1\n",
    "    \n",
    "    # Calculate the p-value\n",
    "    p_value = stats.t.sf(np.abs(t_statistic), df) * 2  # two-tailed test\n",
    "    \n",
    "    return t_statistic, df, p_value\n",
    "\n",
    "# Sample data\n",
    "pre_intervention_scores = [80, 85, 90, 75, 88, 82, 92, 78, 85, 87]\n",
    "post_intervention_scores = [90, 92, 88, 92, 95, 91, 96, 93, 89, 93]\n",
    "\n",
    "# Perform the paired t-test\n",
    "t_statistic, df, p_value = paired_t_test(pre_intervention_scores, post_intervention_scores)\n",
    "\n",
    "# Output the results\n",
    "t_statistic, df, p_value\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5c894d7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.061142089696313844, 36.166783312917644, 0.9515826849267927)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#19 An HR department wants to investigate if there's a gender-based salary gap within the company. Develop a program to analyze salary data, calculate the t-statistic, and determine if there's a statistically significant difference between the average salaries of male and female employees.\n",
    "#Use the below code to generate synthetic data\n",
    "\"\"\"python\n",
    "# Generate synthetic salary data for male and female employees\n",
    "np.random.seed(0)  # For reproducibility\n",
    "male_salaries = np.random.normal(loc=50000, scale=10000, size=20)\n",
    "female_salaries = np.random.normal(loc=55000, scale=9000, size=20)\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "\n",
    "# Generate synthetic salary data for male and female employees\n",
    "np.random.seed(0)  # For reproducibility\n",
    "male_salaries = np.random.normal(loc=50000, scale=10000, size=20)\n",
    "female_salaries = np.random.normal(loc=55000, scale=9000, size=20)\n",
    "\n",
    "def gender_salary_gap_test(male_salaries, female_salaries):\n",
    "    # Calculate means and standard deviations\n",
    "    mean_male = np.mean(male_salaries)\n",
    "    mean_female = np.mean(female_salaries)\n",
    "    \n",
    "    std_male = np.std(male_salaries, ddof=1)\n",
    "    std_female = np.std(female_salaries, ddof=1)\n",
    "    \n",
    "    n_male = len(male_salaries)\n",
    "    n_female = len(female_salaries)\n",
    "    \n",
    "    # Calculate the t-statistic\n",
    "    t_statistic = (mean_male - mean_female) / np.sqrt((std_male**2 / n_male) + (std_female**2 / n_female))\n",
    "    \n",
    "    # Calculate the degrees of freedom\n",
    "    df = ((std_male**2 / n_male) + (std_female**2 / n_female))**2 / ((std_male**2 / n_male)**2 / (n_male - 1) + (std_female**2 / n_female)**2 / (n_female - 1))\n",
    "    \n",
    "    # Calculate the p-value\n",
    "    p_value = stats.t.sf(np.abs(t_statistic), df) * 2  # two-tailed test\n",
    "    \n",
    "    return t_statistic, df, p_value\n",
    "\n",
    "# Perform the gender salary gap test\n",
    "t_statistic, df, p_value = gender_salary_gap_test(male_salaries, female_salaries)\n",
    "\n",
    "# Output the results\n",
    "t_statistic, df, p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "23755403",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T-Statistic: 5.480077554195743\n",
      "P-Value: 8.895290509945655e-07\n",
      "There is a statistically significant difference in customer satisfaction between the branches.\n"
     ]
    }
   ],
   "source": [
    "#20 A restaurant chain collects customer satisfaction scores for two different branches. Write a program to analyze the scores, calculate the t-statistic, and determine if there's a statistically significant difference in customer satisfaction between the branches.\n",
    "\"\"\"Use the below data of scores:\n",
    "python\n",
    "branch_a_scores = [4, 5, 3, 4, 5, 4, 5, 3, 4, 4, 5, 4, 4, 3, 4, 5, 5, 4, 3, 4, 5, 4, 3, 5, 4, 4, 5, 3, 4, 5, 4]\n",
    "branch_b_scores = [3, 4, 2, 3, 4, 3, 4, 2, 3, 3, 4, 3, 3, 2, 3, 4, 4, 3, 2, 3, 4, 3, 2, 4, 3, 3, 4, 2, 3, 4, 3]\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "\n",
    "# Data\n",
    "branch_a_scores = [4, 5, 3, 4, 5, 4, 5, 3, 4, 4, 5, 4, 4, 3, 4, 5, 5, 4, 3, 4, 5, 4, 3, 5, 4, 4, 5, 3, 4, 5, 4]\n",
    "branch_b_scores = [3, 4, 2, 3, 4, 3, 4, 2, 3, 3, 4, 3, 3, 2, 3, 4, 4, 3, 2, 3, 4, 3, 2, 4, 3, 3, 4, 2, 3, 4, 3]\n",
    "\n",
    "# Calculate the t-statistic and p-value\n",
    "t_statistic, p_value = stats.ttest_ind(branch_a_scores, branch_b_scores)\n",
    "\n",
    "# Output the results\n",
    "print(f\"T-Statistic: {t_statistic}\")\n",
    "print(f\"P-Value: {p_value}\")\n",
    "\n",
    "# Interpret the results\n",
    "alpha = 0.05  # significance level\n",
    "if p_value < alpha:\n",
    "    print(\"There is a statistically significant difference in customer satisfaction between the branches.\")\n",
    "else:\n",
    "    print(\"There is no statistically significant difference in customer satisfaction between the branches.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d1ccf815",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Contingency Table:\n",
      "[[95 87]\n",
      " [87 82]\n",
      " [84 65]]\n",
      "\n",
      "Chi-Square Statistic: 0.8779923945254768\n",
      "P-Value: 0.6446832311860852\n",
      "Degrees of Freedom: 2\n",
      "Expected Frequencies:\n",
      "[[96.824 85.176]\n",
      " [89.908 79.092]\n",
      " [79.268 69.732]]\n",
      "\n",
      "There is no significant association between age groups and voter preferences.\n"
     ]
    }
   ],
   "source": [
    "#21 A political analyst wants to determine if there is a significant association between age groups and voter preferences (Candidate A or Candidate B). They collect data from a sample of 500 voters and classify them into different age groups and candidate preferences. Perform a Chi-Square test to determine if there is a significant association between age groups and voter preferences.\n",
    "\"\"\"Use the below code to generate data:\n",
    "```python\n",
    "np.random.seed(0)\n",
    "age_groups = np.random.choice([ 18 30 , 31 50 , 51+', 51+'], size=30)\n",
    "voter_preferences = np.random.choice(['Candidate A', 'Candidate B'], size=30)\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "np.random.seed(0)\n",
    "\n",
    "# Generate random data\n",
    "age_groups = np.random.choice(['18-30', '31-50', '51+'], size=500)\n",
    "voter_preferences = np.random.choice(['Candidate A', 'Candidate B'], size=500)\n",
    "\n",
    "# Create a contingency table (cross-tabulation)\n",
    "contingency_table = np.zeros((3, 2), dtype=int)\n",
    "\n",
    "for i in range(len(age_groups)):\n",
    "    if age_groups[i] == '18-30':\n",
    "        row = 0\n",
    "    elif age_groups[i] == '31-50':\n",
    "        row = 1\n",
    "    elif age_groups[i] == '51+':\n",
    "        row = 2\n",
    "    \n",
    "    if voter_preferences[i] == 'Candidate A':\n",
    "        col = 0\n",
    "    elif voter_preferences[i] == 'Candidate B':\n",
    "        col = 1\n",
    "    \n",
    "    contingency_table[row, col] += 1\n",
    "\n",
    "print(\"Contingency Table:\")\n",
    "print(contingency_table)\n",
    "\n",
    "# Perform Chi-Square test\n",
    "chi2_stat, p_val, dof, expected = chi2_contingency(contingency_table)\n",
    "\n",
    "print(\"\\nChi-Square Statistic:\", chi2_stat)\n",
    "print(\"P-Value:\", p_val)\n",
    "print(\"Degrees of Freedom:\", dof)\n",
    "print(\"Expected Frequencies:\")\n",
    "print(expected)\n",
    "\n",
    "# Interpret the results\n",
    "alpha = 0.05\n",
    "if p_val < alpha:\n",
    "    print(\"\\nThere is a significant association between age groups and voter preferences.\")\n",
    "else:\n",
    "    print(\"\\nThere is no significant association between age groups and voter preferences.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5c87c846",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi-Square Statistic: 27.777056277056275\n",
      "P-Value: 0.00010349448486004387\n",
      "Degrees of Freedom: 6\n",
      "Expected Frequencies:\n",
      "[[34.14634146 34.14634146 37.56097561 34.14634146]\n",
      " [36.58536585 36.58536585 40.24390244 36.58536585]\n",
      " [29.26829268 29.26829268 32.19512195 29.26829268]]\n",
      "\n",
      "There is a significant relationship between product satisfaction levels and customer regions.\n"
     ]
    }
   ],
   "source": [
    "#22 A company conducted a customer satisfaction survey to determine if there is a significant relationship between product satisfaction levels (Satisfied, Neutral, Dissatisfied) and the region where customers are located (East, West, North, South). The survey data is summarized in a contingency table. Conduct a ChiSquare test to determine if there is a significant relationship between product satisfaction levels and\n",
    "#customer regions.\n",
    "\"\"\"Sample data:\n",
    "python\n",
    "#Sample data: Product satisfaction levels (rows) vs. Customer regions (columns)\n",
    "data = np.array([[50, 30, 40, 20], [30, 40, 30, 50], [20, 30, 40, 30]])\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "# Sample data: Product satisfaction levels (rows) vs. Customer regions (columns)\n",
    "data = np.array([[50, 30, 40, 20],   # Satisfied\n",
    "                 [30, 40, 30, 50],   # Neutral\n",
    "                 [20, 30, 40, 30]])  # Dissatisfied\n",
    "\n",
    "# Perform Chi-Square test\n",
    "chi2_stat, p_val, dof, expected = chi2_contingency(data)\n",
    "\n",
    "print(\"Chi-Square Statistic:\", chi2_stat)\n",
    "print(\"P-Value:\", p_val)\n",
    "print(\"Degrees of Freedom:\", dof)\n",
    "print(\"Expected Frequencies:\")\n",
    "print(expected)\n",
    "\n",
    "# Interpret the results\n",
    "alpha = 0.05\n",
    "if p_val < alpha:\n",
    "    print(\"\\nThere is a significant relationship between product satisfaction levels and customer regions.\")\n",
    "else:\n",
    "    print(\"\\nThere is no significant relationship between product satisfaction levels and customer regions.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f114b219",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi-Square Statistic: 22.161728395061726\n",
      "P-Value: 0.00018609719479882557\n",
      "Degrees of Freedom: 4\n",
      "Expected Frequencies:\n",
      "[[34.48275862 34.48275862 31.03448276]\n",
      " [34.48275862 34.48275862 31.03448276]\n",
      " [31.03448276 31.03448276 27.93103448]]\n",
      "\n",
      "There is a significant difference between job performance levels before and after training.\n"
     ]
    }
   ],
   "source": [
    "#23 A company implemented an employee training program to improve job performance (Effective, Neutral, Ineffective). After the training, they collected data from a sample of employees and classified them based on their job performance before and after the training. Perform a Chi-Square test to determine if there is a significant difference between job performance levels before and after the training.\n",
    "\n",
    "\"\"\"Sample data:\n",
    "python\n",
    "# Sample data: Job performance levels before (rows) and after (columns) training\n",
    "data = np.array([[50, 30, 20], [30, 40, 30], [20, 30, 40]])\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "# Sample data: Job performance levels before (rows) and after (columns) training\n",
    "data = np.array([[50, 30, 20],   # Effective\n",
    "                 [30, 40, 30],   # Neutral\n",
    "                 [20, 30, 40]])  # Ineffective\n",
    "\n",
    "# Perform Chi-Square test\n",
    "chi2_stat, p_val, dof, expected = chi2_contingency(data)\n",
    "\n",
    "print(\"Chi-Square Statistic:\", chi2_stat)\n",
    "print(\"P-Value:\", p_val)\n",
    "print(\"Degrees of Freedom:\", dof)\n",
    "print(\"Expected Frequencies:\")\n",
    "print(expected)\n",
    "\n",
    "# Interpret the results\n",
    "alpha = 0.05\n",
    "if p_val < alpha:\n",
    "    print(\"\\nThere is a significant difference between job performance levels before and after training.\")\n",
    "else:\n",
    "    print(\"\\nThere is no significant difference between job performance levels before and after training.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bed95033",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F-Statistic: 27.03556231003039\n",
      "P-Value: 3.5786328857349003e-07\n",
      "\n",
      "There is a significant difference in customer satisfaction scores among the product versions.\n"
     ]
    }
   ],
   "source": [
    " #24. A company produces three different versions of a product: Standard, Premium, and Deluxe. The company wants to determine if there is a significant difference in customer satisfaction scores among the three product versions. They conducted a survey and collected customer satisfaction scores for each version from a random sample of customers. \n",
    " #Perform an ANOVA test to determine if there is a significant difference in customer satisfaction scores.\n",
    "\"\"\" Use the following data:\n",
    "  ```python\n",
    "  # Sample data: Customer satisfaction scores for each product version\n",
    "  standard_scores = [80, 85, 90, 78, 88, 82, 92, 78, 85, 87]\n",
    "  premium_scores = [90, 92, 88, 92, 95, 91, 96, 93, 89, 93]\n",
    "  deluxe_scores = [95, 98, 92, 97, 96, 94, 98, 97, 92, 99]\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "from scipy.stats import f_oneway\n",
    "\n",
    "# Sample data: Customer satisfaction scores for each product version\n",
    "standard_scores = [80, 85, 90, 78, 88, 82, 92, 78, 85, 87]\n",
    "premium_scores = [90, 92, 88, 92, 95, 91, 96, 93, 89, 93]\n",
    "deluxe_scores = [95, 98, 92, 97, 96, 94, 98, 97, 92, 99]\n",
    "\n",
    "# Perform one-way ANOVA\n",
    "f_statistic, p_value = f_oneway(standard_scores, premium_scores, deluxe_scores)\n",
    "\n",
    "print(\"F-Statistic:\", f_statistic)\n",
    "print(\"P-Value:\", p_value)\n",
    "\n",
    "# Interpret the results\n",
    "alpha = 0.05\n",
    "if p_value < alpha:\n",
    "    print(\"\\nThere is a significant difference in customer satisfaction scores among the product versions.\")\n",
    "else:\n",
    "    print(\"\\nThere is no significant difference in customer satisfaction scores among the product versions.\")\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "eab10059",
   "metadata": {},
   "outputs": [],
   "source": []
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