Statistics/Statistics-Can-Mislead.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b423bf25",
   "metadata": {},
   "source": [
    "# Daily-Dose-of-Data-Science\n",
    "\n",
    "[Daily Dose of Data Science](https://avichawla.substack.com) is a publication on Substack that brings together intriguing frameworks, libraries, technologies, and tips that make the life cycle of a Data Science project effortless. \n",
    "\n",
    "Author: Avi Chawla\n",
    "\n",
    "[Medium](https://medium.com/@avi_chawla) | [LinkedIn](https://www.linkedin.com/in/avi-chawla/)\n",
    "\n",
    "# Why Correlation (and Other Statistics) Can Be Misleading.\n",
    "\n",
    "Post Link: [Substack](https://avichawla.substack.com/p/why-correlation-and-other-statistics)\n",
    "\n",
    "LinkedIn Post: [LinkedIn](https://www.linkedin.com/posts/avi-chawla_python-datascience-statistics-activity-7003686582763294720-sphP?utm_source=share&utm_medium=member_desktop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "950da48d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-02T08:09:31.306364Z",
     "start_time": "2022-12-02T08:09:29.758481Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5df5f84b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-02T08:09:31.323732Z",
     "start_time": "2022-12-02T08:09:31.307304Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.704806</td>\n",
       "      <td>-0.468064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.308381</td>\n",
       "      <td>-3.565576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.190285</td>\n",
       "      <td>-2.504855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.664763</td>\n",
       "      <td>0.643189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.291005</td>\n",
       "      <td>0.873306</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x         y\n",
       "0  0.704806 -0.468064\n",
       "1 -1.308381 -3.565576\n",
       "2 -0.190285 -2.504855\n",
       "3  0.664763  0.643189\n",
       "4  0.291005  0.873306"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_outliers = pd.read_csv(\"../_Datasets/Correlation_example.csv\")\n",
    "df_with_outliers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4ea0189c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-02T08:09:38.389842Z",
     "start_time": "2022-12-02T08:09:38.383071Z"
    }
   },
   "outputs": [],
   "source": [
    "df_without_outliers = df_with_outliers.loc[:47]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b3c24f95",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-02T08:09:40.027628Z",
     "start_time": "2022-12-02T08:09:39.890401Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='x', ylabel='y'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_without_outliers.plot(kind='scatter',x='x', y='y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "22775284",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-02T08:09:50.233680Z",
     "start_time": "2022-12-02T08:09:50.056058Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7feb9925bd90>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x = \"x\", y = \"y\", data = df_without_outliers, ci = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a20de38b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-02T08:09:58.850362Z",
     "start_time": "2022-12-02T08:09:58.673475Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7febac44d2b0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x = \"x\", y = \"y\", data = df_with_outliers, ci = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0c791fc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}