Assignment(statistics7).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "990205fd-addd-4acb-aef4-9b4bfb9e3cf8",
   "metadata": {},
   "source": [
    "# Q1. Calculate the 95% confidence interval for a sample of data with a mean of 50 and a standard deviation of 5 using Python. Interpret the results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe4fb26f-d132-49e5-85da-92f0990e4a55",
   "metadata": {},
   "source": [
    "To calculate the 95% confidence interval for a sample of data with a mean of 50 and a standard deviation of 5 using Python, we can use the scipy.stats module. Specifically, we can use the t.interval() function from this module, which calculates the confidence interval for a given sample mean, sample size, and confidence level, assuming that the underlying population follows a normal distribution.\n",
    "\n",
    "Here's the Python code to calculate the 95% confidence interval for the given sample data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d7c2bdf7-a47e-4010-95ec-758831c8b3fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 95% confidence interval is (49.00789152424566, 50.99210847575434)\n"
     ]
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "sample_mean = 50\n",
    "sample_std = 5\n",
    "sample_size = 100 # assuming a sample size of 100 for illustration purposes\n",
    "\n",
    "# Calculate the 95% confidence interval using the t-distribution\n",
    "ci = stats.t.interval(0.95, df=sample_size-1, loc=sample_mean, scale=sample_std/np.sqrt(sample_size))\n",
    "\n",
    "# Print the results\n",
    "print(f\"The 95% confidence interval is {ci}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d9206bc-686b-4aa4-b6f5-bb25b558af69",
   "metadata": {},
   "source": [
    "# Q2. Conduct a chi-square goodness of fit test to determine if the distribution of colors of M&Ms in a bag matches the expected distribution of 20% blue, 20% orange, 20% green, 10% yellow, 10% red, and 20% brown. Use Python to perform the test with a significance level of 0.05."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f06e6bf-a6cc-4afc-835f-8ab91e768f68",
   "metadata": {},
   "source": [
    "To conduct a chi-square goodness of fit test in Python, we can use the scipy.stats module. Specifically, we can use the chisquare() function from this module, which calculates the chi-square test statistic and p-value for a given set of observed and expected frequencies.\n",
    "\n",
    "Here's the Python code to conduct a chi-square goodness of fit test for the distribution of colors of M&Ms in a bag:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f9289f2a-ba64-4641-a182-e9feb9994726",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The chi-square test statistic is 21.250\n",
      "The p-value is 0.001\n",
      "We reject the null hypothesis that the observed distribution of colors matches the expected distribution.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "\n",
    "# Observed frequencies\n",
    "obs = np.array([48, 27, 24, 15, 10, 16])\n",
    "\n",
    "# Expected frequencies (assuming equal distribution of colors)\n",
    "exp = np.array([0.2, 0.2, 0.2, 0.1, 0.1, 0.2]) * np.sum(obs)\n",
    "\n",
    "# Calculate the chi-square test statistic and p-value\n",
    "stat, pval = stats.chisquare(obs, f_exp=exp)\n",
    "\n",
    "# Print the results\n",
    "print(f\"The chi-square test statistic is {stat:.3f}\")\n",
    "print(f\"The p-value is {pval:.3f}\")\n",
    "\n",
    "if pval < 0.05:\n",
    "    print(\"We reject the null hypothesis that the observed distribution of colors matches the expected distribution.\")\n",
    "else:\n",
    "    print(\"We fail to reject the null hypothesis that the observed distribution of colors matches the expected distribution.\")\n"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "id": "9af173d1-ae56-4107-be5b-237bbffdda4b",
   "metadata": {},
   "source": [
    "# Q3. Use Python to calculate the chi-square statistic and p-value for a contingency table with the following data:\n",
    "![Stats3.png](attachment:7b3c2fa2-081f-474f-b880-7c0b4ff0ae33.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8146e1dc-7ed3-4266-b2fe-f81ecdebd8a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi-square statistic: 5.833333333333334\n",
      "P-value: 0.05411376622282158\n",
      "Degrees of freedom: 2\n",
      "Expected frequencies:\n",
      " [[15. 20.]\n",
      " [15. 20.]\n",
      " [15. 20.]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "# Define the contingency table\n",
    "observed = np.array([[20, 15], [10, 25], [15, 20]])\n",
    "\n",
    "# Calculate the chi-square statistic, p-value, degrees of freedom, and expected frequencies\n",
    "chi2, p, dof, expected = chi2_contingency(observed)\n",
    "\n",
    "# Print the results\n",
    "print(f\"Chi-square statistic: {chi2}\")\n",
    "print(f\"P-value: {p}\")\n",
    "print(f\"Degrees of freedom: {dof}\")\n",
    "print(\"Expected frequencies:\\n\", expected)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32bb0616-6955-4c8a-aa28-df0cabee97bd",
   "metadata": {},
   "source": [
    "# Q4. A study of the prevalence of smoking in a population of 500 individuals found that 60 individuals smoked. Use Python to calculate the 95% confidence interval for the true proportion of individuals in the population who smoke."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9d1a6c1-5f7e-4e57-95c1-6e06251ffdbb",
   "metadata": {},
   "source": [
    "To calculate the 95% confidence interval for the true proportion of individuals in the population who smoke, we can use the formula:\n",
    "\n",
    "CI = p ± z*(sqrt(p*(1-p)/n))\n",
    "\n",
    "Where:\n",
    "\n",
    "p is the sample proportion (number of individuals who smoke divided by the total sample size)\n",
    "z is the critical value from the standard normal distribution for the desired confidence level (95% confidence level corresponds to z=1.96)\n",
    "n is the sample size\n",
    "Using Python, we can calculate this as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "697fd375-c010-411f-b773-88a47e1c176b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95% Confidence Interval: (0.0915, 0.1485)\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "# sample size\n",
    "n = 500\n",
    "\n",
    "# number of individuals who smoke\n",
    "x = 60\n",
    "\n",
    "# sample proportion\n",
    "p = x/n\n",
    "\n",
    "# critical value for 95% confidence level\n",
    "z = 1.96\n",
    "\n",
    "# calculate standard error\n",
    "se = math.sqrt(p*(1-p)/n)\n",
    "\n",
    "# calculate confidence interval\n",
    "lower = p - z*se\n",
    "upper = p + z*se\n",
    "\n",
    "print(\"95% Confidence Interval: ({:.4f}, {:.4f})\".format(lower, upper))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bb71af1-4463-4ca5-b177-ecb2f9a12784",
   "metadata": {},
   "source": [
    "# Q5. Calculate the 90% confidence interval for a sample of data with a mean of 75 and a standard deviation of 12 using Python. Interpret the results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8008090f-225d-4f9c-b308-d6ea11bfbced",
   "metadata": {},
   "source": [
    "To calculate the 90% confidence interval for a sample of data with a mean of 75 and a standard deviation of 12, we can use the formula:\n",
    "\n",
    "CI = x̄ ± z*(s/√n)\n",
    "\n",
    "Where:\n",
    "\n",
    "x̄ is the sample mean\n",
    "s is the sample standard deviation\n",
    "n is the sample size\n",
    "z is the critical value from the standard normal distribution for the desired confidence level (90% confidence level corresponds to z=1.645)\n",
    "Using Python, we can calculate this as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "65927bf1-e078-42d7-ac3a-d6feafbf59a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90% Confidence Interval: (73.0260, 76.9740)\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "# sample mean\n",
    "x_bar = 75\n",
    "\n",
    "# sample standard deviation\n",
    "s = 12\n",
    "\n",
    "# sample size\n",
    "n = 100\n",
    "\n",
    "# critical value for 90% confidence level\n",
    "z = 1.645\n",
    "\n",
    "# calculate standard error\n",
    "se = s/math.sqrt(n)\n",
    "\n",
    "# calculate confidence interval\n",
    "lower = x_bar - z*se\n",
    "upper = x_bar + z*se\n",
    "\n",
    "print(\"90% Confidence Interval: ({:.4f}, {:.4f})\".format(lower, upper))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a9b465d-94f1-4a2a-bab8-1c3e00760a61",
   "metadata": {},
   "source": [
    "# Q6. Use Python to plot the chi-square distribution with 10 degrees of freedom. Label the axes and shade the area corresponding to a chi-square statistic of 15."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5cee7ec1-c067-424a-b65f-12effa394779",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import chi2\n",
    "\n",
    "# degrees of freedom\n",
    "df = 10\n",
    "\n",
    "# x-values for the plot\n",
    "x = np.linspace(0, 30, 500)\n",
    "\n",
    "# probability density function values\n",
    "pdf = chi2.pdf(x, df)\n",
    "\n",
    "# create the plot\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "ax.plot(x, pdf, color='black', linewidth=2)\n",
    "\n",
    "# shade the area corresponding to a chi-square statistic of 15\n",
    "x_shade = np.linspace(15, 30, 500)\n",
    "ax.fill_between(x_shade, chi2.pdf(x_shade, df), color='blue', alpha=0.3)\n",
    "\n",
    "# add labels and a title\n",
    "ax.set_xlabel('Chi-Square Statistic')\n",
    "ax.set_ylabel('Probability Density')\n",
    "ax.set_title('Chi-Square Distribution with 10 Degrees of Freedom')\n",
    "\n",
    "# show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd0af1c0-44e7-45f8-93eb-b646ba20d99c",
   "metadata": {},
   "source": [
    "# Q7. A random sample of 1000 people was asked if they preferred Coke or Pepsi. Of the sample, 520 preferred Coke. Calculate a 99% confidence interval for the true proportion of people in the population who prefer Coke."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "49f70af7-e7a6-4bc7-bbee-70e25958e182",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99% Confidence Interval: (0.4793, 0.5607)\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "# sample size\n",
    "n = 1000\n",
    "\n",
    "# number of individuals who prefer Coke\n",
    "x = 520\n",
    "\n",
    "# sample proportion\n",
    "p = x/n\n",
    "\n",
    "# critical value for 99% confidence level\n",
    "z = 2.576\n",
    "\n",
    "# calculate standard error\n",
    "se = math.sqrt(p*(1-p)/n)\n",
    "\n",
    "# calculate confidence interval\n",
    "lower = p - z*se\n",
    "upper = p + z*se\n",
    "\n",
    "print(\"99% Confidence Interval: ({:.4f}, {:.4f})\".format(lower, upper))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38fbabe1-c70a-4f2d-ba34-53d9ee8a73c8",
   "metadata": {},
   "source": [
    "# Q8. A researcher hypothesizes that a coin is biased towards tails. They flip the coin 100 times and observe 45 tails. Conduct a chi-square goodness of fit test to determine if the observed frequencies match the expected frequencies of a fair coin. Use a significance level of 0.05."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dbf11277-0376-4f85-b462-dad3784c44c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail to reject the null hypothesis. The coin is fair.\n"
     ]
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "\n",
    "# Define null hypothesis, alternative hypothesis, and significance level\n",
    "null_hypothesis = \"The coin is fair.\"\n",
    "alternative_hypothesis = \"The coin is biased towards tails.\"\n",
    "significance_level = 0.05\n",
    "\n",
    "# Define observed values and expected values\n",
    "observed_values = [45, 55]  # 45 tails, 55 heads\n",
    "expected_values = [50, 50]  # assuming a fair coin\n",
    "\n",
    "# Calculate the chi-square statistic and p-value\n",
    "chi_square_statistic, p_value = stats.chisquare(observed_values, expected_values)\n",
    "\n",
    "# Compare p-value to significance level to determine whether to reject null hypothesis\n",
    "if p_value < significance_level:\n",
    "    print(f\"Reject the null hypothesis. {alternative_hypothesis}\")\n",
    "else:\n",
    "    print(f\"Fail to reject the null hypothesis. {null_hypothesis}\")\n"
   ]
  },
  {
   "attachments": {
    "116fbeeb-c193-41cc-902a-03d418fb7920.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "b5c60266-612c-4849-92d0-36d3eb8e1c30",
   "metadata": {},
   "source": [
    "# Q9. A study was conducted to determine if there is an association between smoking status (smoker or non-smoker) and lung cancer diagnosis (yes or no). The results are shown in the contingency table below. Conduct a chi-square test for independence to determine if there is a significant association between smoking status and lung cancer diagnosis.\n",
    "![stats1.png](attachment:116fbeeb-c193-41cc-902a-03d418fb7920.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9f5dcadb-70a4-4742-8cbf-4f6799887274",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi-square test statistic:  12.057347670250895\n",
      "Degrees of freedom:  1\n",
      "P-value:  0.0005158863863703744\n",
      "Expected frequencies: \n",
      " [[ 45. 155.]\n",
      " [ 45. 155.]]\n"
     ]
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "\n",
    "# Create the contingency table\n",
    "obs = np.array([[60, 140], [30, 170]])\n",
    "\n",
    "# Calculate the chi-square test statistic, p-value, degrees of freedom and expected frequencies\n",
    "chi2_stat, p_val, dof, expected_freq = stats.chi2_contingency(obs)\n",
    "\n",
    "# Print the results\n",
    "print(\"Chi-square test statistic: \", chi2_stat)\n",
    "print(\"Degrees of freedom: \", dof)\n",
    "print(\"P-value: \", p_val)\n",
    "print(\"Expected frequencies: \\n\", expected_freq)\n"
   ]
  },
  {
   "attachments": {
    "99cb9102-f61f-4bda-b839-aea44a36dbb1.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "468f7126-3802-4458-96d6-eeae161a400c",
   "metadata": {},
   "source": [
    "# Q10. A study was conducted to determine if the proportion of people who prefer milk chocolate, dark chocolate, or white chocolate is different in the U.S. versus the U.K. A random sample of 500 people from the U.S. and a random sample of 500 people from the U.K. were surveyed. The results are shown in the contingency table below. Conduct a chi-square test for independence to determine if there is a significant association between chocolate preference and country of origin.\n",
    "![stats2.png](attachment:99cb9102-f61f-4bda-b839-aea44a36dbb1.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a73c5dd9-9957-4428-88f9-53794bcb8ed0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reject the null hypothesis. There is an association between chocolate preference and country of origin.\n",
      "Observed values:\n",
      "[[200 150 150]\n",
      " [225 175 100]]\n",
      "Expected values:\n",
      "[[212.5 162.5 125. ]\n",
      " [212.5 162.5 125. ]]\n",
      "Chi-square statistic: 13.393665158371041\n",
      "P-value: 0.0012348168997745918\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "\n",
    "# Define null hypothesis, alternative hypothesis, and significance level\n",
    "null_hypothesis = \"There is no association between chocolate preference and country of origin.\"\n",
    "alternative_hypothesis = \"There is an association between chocolate preference and country of origin.\"\n",
    "significance_level = 0.01\n",
    "\n",
    "# Define observed values\n",
    "observed_values = np.array([[200, 150, 150], [225, 175, 100]])\n",
    "\n",
    "# Calculate expected values\n",
    "row_totals = observed_values.sum(axis=1)\n",
    "column_totals = observed_values.sum(axis=0)\n",
    "sample_size = observed_values.sum()\n",
    "expected_values = np.outer(row_totals, column_totals) / sample_size\n",
    "\n",
    "# Calculate the chi-square statistic and p-value\n",
    "chi_square_statistic, p_value, _, _ = stats.chi2_contingency(observed_values)\n",
    "\n",
    "# Compare p-value to significance level to determine whether to reject null hypothesis\n",
    "if p_value < significance_level:\n",
    "    print(f\"Reject the null hypothesis. {alternative_hypothesis}\")\n",
    "else:\n",
    "    print(f\"Fail to reject the null hypothesis. {null_hypothesis}\")\n",
    "\n",
    "# Print the observed and expected values, and the chi-square statistic and p-value\n",
    "print(\"Observed values:\")\n",
    "print(observed_values)\n",
    "print(\"Expected values:\")\n",
    "print(expected_values)\n",
    "print(f\"Chi-square statistic: {chi_square_statistic}\")\n",
    "print(f\"P-value: {p_value}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "025e8e57-5503-4bbe-9c49-5abedd53a043",
   "metadata": {},
   "source": [
    "# Q11. A random sample of 30 people was selected from a population with an unknown mean and standard deviation. The sample mean was found to be 72 and the sample standard deviation was found to be 10. Conduct a hypothesis test to determine if the population mean is significantly different from 70. Use a significance level of 0.05."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7bd07a40-ba2b-4099-ace0-d8913353213f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail to reject the null hypothesis. The population mean is equal to 70.\n",
      "t-statistic: nan\n",
      "p-value: nan\n",
      "95% confidence interval for population mean: (68.265938632419, 75.734061367581)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_77/2784087827.py:15: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n",
      "  t_statistic, p_value = stats.ttest_1samp([sample_mean], hypothesized_mean)\n",
      "/opt/conda/lib/python3.10/site-packages/scipy/stats/_stats_py.py:1253: RuntimeWarning: divide by zero encountered in divide\n",
      "  var *= np.divide(n, n-ddof)  # to avoid error on division by zero\n",
      "/opt/conda/lib/python3.10/site-packages/scipy/stats/_stats_py.py:1253: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  var *= np.divide(n, n-ddof)  # to avoid error on division by zero\n"
     ]
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "\n",
    "# Define null hypothesis, alternative hypothesis, and significance level\n",
    "null_hypothesis = \"The population mean is equal to 70.\"\n",
    "alternative_hypothesis = \"The population mean is not equal to 70.\"\n",
    "significance_level = 0.05\n",
    "\n",
    "# Define sample mean, sample standard deviation, sample size, and hypothesized population mean\n",
    "sample_mean = 72\n",
    "sample_std_dev = 10\n",
    "sample_size = 30\n",
    "hypothesized_mean = 70\n",
    "\n",
    "# Calculate the t-statistic and p-value\n",
    "t_statistic, p_value = stats.ttest_1samp([sample_mean], hypothesized_mean)\n",
    "\n",
    "# Compare p-value to significance level to determine whether to reject null hypothesis\n",
    "if p_value < significance_level:\n",
    "    print(f\"Reject the null hypothesis. {alternative_hypothesis}\")\n",
    "else:\n",
    "    print(f\"Fail to reject the null hypothesis. {null_hypothesis}\")\n",
    "\n",
    "# Print the t-statistic, p-value, and 95% confidence interval for the population mean\n",
    "print(f\"t-statistic: {t_statistic}\")\n",
    "print(f\"p-value: {p_value}\")\n",
    "print(f\"95% confidence interval for population mean: {stats.t.interval(0.95, sample_size-1, loc=sample_mean, scale=sample_std_dev/np.sqrt(sample_size))}\")\n"
   ]
  },
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   "id": "acac7e59-4dc0-4549-a792-833cb9943069",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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