a-comprehensive-guide-to-inferential-statistics.ipynb

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    "## Let's dive into inferential statistics.\n",
    "\n",
    "**What is Inferential Statistics?**\n",
    "\n",
    "Inferential statistics allows you to make predictions and inferences about a larger population based on samples of data. It helps in hypothesis testing, estimating population parameters, and making forecasts. Unlike descriptive statistics, which describes what the data shows, inferential statistics allows for broader conclusions.\n",
    "\n",
    "## Key Concepts in Inferential Statistics\n",
    "* Population vs. Sample: The population is the entire group you're interested in, while a sample is a subset of the population used for analysis.\n",
    "* Parameter vs. Statistic: A parameter is a characteristic of a population (like the mean age of all people in a city), while a statistic is a characteristic of a sample (like the mean age of people in a survey from that city).\n",
    "* Sampling Distribution: This is the distribution of a statistic over many samples drawn from the same population. It is fundamental in determining how well a statistic represents a parameter.\n",
    "* Confidence Intervals: These are used to estimate the value of a population parameter. A 95% confidence interval means you can be 95% certain the population parameter lies within this interval.\n",
    "* Hypothesis Testing: This is a method of making decisions using data, whether it's rejecting or not rejecting a hypothesis. It often uses test statistics and p-values to determine results.\n",
    "## Common Statistical Tests\n",
    "* T-test: Used to compare the means of two groups (e.g., comparing the effectiveness of two medications).\n",
    "* ANOVA (Analysis of Variance): Used to compare the means of three or more groups.\n",
    "* Chi-square Test: Used for categorical data to see if distributions of categorical variables differ from one another.\n",
    "* Regression Analysis: Used to understand relationships between variables. Linear regression is a common method for predicting a quantitative response."
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   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import statsmodels.api as sm "
   ]
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    "# Generating a population and a sample\n",
    "population = np.random.normal(loc=50, scale=10, size=10000)  # Large population\n",
    "sample = np.random.choice(population, size=100)  # Smaller sample"
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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting both distributions\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.hist(population, bins=50, alpha=0.6, label='Population')\n",
    "plt.hist(sample, bins=50, alpha=0.8, label='Sample')\n",
    "plt.title('Population vs. Sample Distribution')\n",
    "plt.xlabel('Values')\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "090e6860",
   "metadata": {
    "papermill": {
     "duration": 0.004355,
     "end_time": "2024-06-01T17:53:15.865002",
     "exception": false,
     "start_time": "2024-06-01T17:53:15.860647",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Confidence Intervals\n",
    "Confidence intervals are a range of values that are believed to encompass the true population parameter with a certain level of confidence. Here's how to calculate and visualize a 95% confidence interval for our sample mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f9d60a0d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-01T17:53:15.876231Z",
     "iopub.status.busy": "2024-06-01T17:53:15.875845Z",
     "iopub.status.idle": "2024-06-01T17:53:16.091808Z",
     "shell.execute_reply": "2024-06-01T17:53:16.090704Z"
    },
    "papermill": {
     "duration": 0.22474,
     "end_time": "2024-06-01T17:53:16.094502",
     "exception": false,
     "start_time": "2024-06-01T17:53:15.869762",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95% Confidence Interval: 47.78 to 51.75\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the sample mean and standard error of the mean\n",
    "sample_mean = np.mean(sample)\n",
    "sample_se = stats.sem(sample)\n",
    "\n",
    "# Define the confidence level and calculate the confidence interval\n",
    "confidence_interval = stats.t.interval(0.95, len(sample)-1, loc=sample_mean, scale=sample_se)\n",
    "\n",
    "# Output the confidence interval\n",
    "print(f\"95% Confidence Interval: {confidence_interval[0]:.2f} to {confidence_interval[1]:.2f}\")\n",
    "\n",
    "# Visualizing the confidence interval\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.errorbar(0, sample_mean, yerr=[sample_mean - confidence_interval[0]], fmt='o')\n",
    "plt.title('Sample Mean and 95% Confidence Interval')\n",
    "plt.ylabel('Mean Value')\n",
    "plt.xticks([])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "925b09e3",
   "metadata": {
    "papermill": {
     "duration": 0.004801,
     "end_time": "2024-06-01T17:53:16.104453",
     "exception": false,
     "start_time": "2024-06-01T17:53:16.099652",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Point Estimates\n",
    "A point estimate is a single value used to approximate a population parameter. The most common point estimates are the sample mean, sample variance, and sample proportion, which estimate the population mean, variance, and proportion, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "80c871be",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-01T17:53:16.117433Z",
     "iopub.status.busy": "2024-06-01T17:53:16.116282Z",
     "iopub.status.idle": "2024-06-01T17:53:16.420081Z",
     "shell.execute_reply": "2024-06-01T17:53:16.419055Z"
    },
    "papermill": {
     "duration": 0.312449,
     "end_time": "2024-06-01T17:53:16.422391",
     "exception": false,
     "start_time": "2024-06-01T17:53:16.109942",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Point Estimate of Mean: 49.76\n",
      "Point Estimate of Variance: 100.29\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate point estimates for the mean and variance\n",
    "sample_mean = np.mean(sample)\n",
    "sample_variance = np.var(sample, ddof=1)  # ddof=1 for an unbiased estimate\n",
    "\n",
    "# Display the point estimates\n",
    "print(f\"Point Estimate of Mean: {sample_mean:.2f}\")\n",
    "print(f\"Point Estimate of Variance: {sample_variance:.2f}\")\n",
    "\n",
    "# Visualizing the point estimate of the mean\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.hist(sample, bins=30, color='skyblue', alpha=0.7)\n",
    "plt.axvline(sample_mean, color='r', linestyle='dashed', linewidth=2)\n",
    "plt.title('Sample Distribution with Point Estimate of Mean')\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "574da5de",
   "metadata": {
    "papermill": {
     "duration": 0.005194,
     "end_time": "2024-06-01T17:53:16.433154",
     "exception": false,
     "start_time": "2024-06-01T17:53:16.427960",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Performing Statistical Tests\n",
    "**T-Test Example**\n",
    "Let's perform a T-test to determine if there are statistically significant differences between two independent groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5db2ac07",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-01T17:53:16.445892Z",
     "iopub.status.busy": "2024-06-01T17:53:16.445187Z",
     "iopub.status.idle": "2024-06-01T17:53:16.846417Z",
     "shell.execute_reply": "2024-06-01T17:53:16.845300Z"
    },
    "papermill": {
     "duration": 0.41018,
     "end_time": "2024-06-01T17:53:16.848815",
     "exception": false,
     "start_time": "2024-06-01T17:53:16.438635",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T-Statistic: 2.02, P-value: 0.046\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating data for two groups\n",
    "group1 = np.random.normal(55, 10, 50)\n",
    "group2 = np.random.normal(50, 10, 50)\n",
    "\n",
    "# Performing a T-test\n",
    "t_stat, p_value = stats.ttest_ind(group1, group2)\n",
    "\n",
    "# Outputting T-test results\n",
    "print(f\"T-Statistic: {t_stat:.2f}, P-value: {p_value:.3f}\")\n",
    "\n",
    "# Plotting the results\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.hist(group1, bins=30, alpha=0.6, label='Group 1')\n",
    "plt.hist(group2, bins=30, alpha=0.6, label='Group 2')\n",
    "plt.title('Group Comparisons for T-test')\n",
    "plt.xlabel('Values')\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bc3757e",
   "metadata": {
    "papermill": {
     "duration": 0.005822,
     "end_time": "2024-06-01T17:53:16.860731",
     "exception": false,
     "start_time": "2024-06-01T17:53:16.854909",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## ANOVA Test Example\n",
    "ANOVA is used to compare the means of three or more groups to see if at least one group mean is different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2d01b6e9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-01T17:53:16.874346Z",
     "iopub.status.busy": "2024-06-01T17:53:16.873956Z",
     "iopub.status.idle": "2024-06-01T17:53:17.320244Z",
     "shell.execute_reply": "2024-06-01T17:53:17.319094Z"
    },
    "papermill": {
     "duration": 0.455909,
     "end_time": "2024-06-01T17:53:17.322714",
     "exception": false,
     "start_time": "2024-06-01T17:53:16.866805",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F-Statistic: 37.51, P-value: 0.000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating data for three groups\n",
    "group1 = np.random.normal(50, 10, 50)\n",
    "group2 = np.random.normal(55, 10, 50)\n",
    "group3 = np.random.normal(60, 10, 50)\n",
    "\n",
    "# Performing ANOVA\n",
    "f_stat, p_value = stats.f_oneway(group1, group2, group3)\n",
    "\n",
    "# Outputting ANOVA results\n",
    "print(f\"F-Statistic: {f_stat:.2f}, P-value: {p_value:.3f}\")\n",
    "\n",
    "# Plotting the results\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.hist([group1, group2, group3], bins=30, alpha=0.7, label=['Group 1', 'Group 2', 'Group 3'])\n",
    "plt.title('Group Comparisons for ANOVA Test')\n",
    "plt.xlabel('Values')\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aeef83ff",
   "metadata": {
    "papermill": {
     "duration": 0.006277,
     "end_time": "2024-06-01T17:53:17.335609",
     "exception": false,
     "start_time": "2024-06-01T17:53:17.329332",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Chi-square Test Example\n",
    "The Chi-square test is used for categorical data to test the hypothesis about the association between categorical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c975442f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-01T17:53:17.350300Z",
     "iopub.status.busy": "2024-06-01T17:53:17.349913Z",
     "iopub.status.idle": "2024-06-01T17:53:17.588065Z",
     "shell.execute_reply": "2024-06-01T17:53:17.587044Z"
    },
    "papermill": {
     "duration": 0.247943,
     "end_time": "2024-06-01T17:53:17.590105",
     "exception": false,
     "start_time": "2024-06-01T17:53:17.342162",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi-square Statistic: 48.50, P-value: 0.000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import chisquare  # Importing the chisquare function\n",
    "\n",
    "# Generating categorical data\n",
    "data = np.random.choice(['Red', 'Blue', 'Green'], size=300, p=[0.3, 0.5, 0.2])\n",
    "observed = [sum(data == 'Red'), sum(data == 'Blue'), sum(data == 'Green')]\n",
    "\n",
    "# Performing Chi-square test\n",
    "chi_stat, p_value = chisquare(f_obs=observed)\n",
    "\n",
    "# Outputting Chi-square test results\n",
    "print(f\"Chi-square Statistic: {chi_stat:.2f}, P-value: {p_value:.3f}\")\n",
    "\n",
    "# Plotting the results\n",
    "colors = ['Red', 'Blue', 'Green']\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.bar(colors, observed, color=colors, alpha=0.7)\n",
    "plt.title('Chi-square Test for Categorical Data')\n",
    "plt.xlabel('Categories')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baea3648",
   "metadata": {
    "papermill": {
     "duration": 0.006545,
     "end_time": "2024-06-01T17:53:17.603621",
     "exception": false,
     "start_time": "2024-06-01T17:53:17.597076",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Regression Analysis Example\n",
    "Regression analysis is used to understand the relationship between dependent and independent variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "562dae8d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-06-01T17:53:17.618727Z",
     "iopub.status.busy": "2024-06-01T17:53:17.618357Z",
     "iopub.status.idle": "2024-06-01T17:53:17.949047Z",
     "shell.execute_reply": "2024-06-01T17:53:17.948011Z"
    },
    "papermill": {
     "duration": 0.34564,
     "end_time": "2024-06-01T17:53:17.956041",
     "exception": false,
     "start_time": "2024-06-01T17:53:17.610401",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.824\n",
      "Model:                            OLS   Adj. R-squared:                  0.822\n",
      "Method:                 Least Squares   F-statistic:                     458.8\n",
      "Date:                Sat, 01 Jun 2024   Prob (F-statistic):           9.48e-39\n",
      "Time:                        17:53:17   Log-Likelihood:                -209.11\n",
      "No. Observations:                 100   AIC:                             422.2\n",
      "Df Residuals:                      98   BIC:                             427.4\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          1.6521      0.957      1.727      0.087      -0.246       3.551\n",
      "x1             1.8748      0.088     21.420      0.000       1.701       2.048\n",
      "==============================================================================\n",
      "Omnibus:                        4.022   Durbin-Watson:                   1.988\n",
      "Prob(Omnibus):                  0.134   Jarque-Bera (JB):                3.454\n",
      "Skew:                           0.333   Prob(JB):                        0.178\n",
      "Kurtosis:                       3.621   Cond. No.                         53.3\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Generating some data\n",
    "x = np.random.normal(10, 2, 100)\n",
    "y = 2 * x + np.random.normal(0, 2, 100)\n",
    "\n",
    "# Adding a constant term for the intercept\n",
    "x = sm.add_constant(x)  # This adds a constant column to input features\n",
    "\n",
    "# Fit the regression model\n",
    "model = sm.OLS(y, x).fit()\n",
    "\n",
    "# Plotting the regression\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.scatter(x[:, 1], y, alpha=0.5, label='Data points')\n",
    "plt.plot(x[:, 1], model.predict(x), 'r', label='Regression Line')\n",
    "plt.title('Linear Regression Analysis')\n",
    "plt.xlabel('Independent Variable')\n",
    "plt.ylabel('Dependent Variable')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Printing the summary of the regression model\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "986d1cf4",
   "metadata": {
    "papermill": {
     "duration": 0.007386,
     "end_time": "2024-06-01T17:53:17.972108",
     "exception": false,
     "start_time": "2024-06-01T17:53:17.964722",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
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   "end_time": "2024-06-01T17:53:18.600917",
   "environment_variables": {},
   "exception": null,
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   "output_path": "__notebook__.ipynb",
   "parameters": {},
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