{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled102.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyND+hcfb92pBaAi7AxLcdoy",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nurfnick/Data_Viz/blob/main/Content/Data_Visualization/29_Uncertainty.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Uncertainty"
      ],
      "metadata": {
        "id": "z2wSVGyv6lCe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Uncertainty is one of the most important topics in statistics.  Visualizing that uncertainty is very critical to expressing these statistical ideas.  We'll explore several ways to display this crutical concept."
      ],
      "metadata": {
        "id": "xI8gVGSu6n7r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Waffle Charts"
      ],
      "metadata": {
        "id": "2DdA6XyabjhS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### With Random Chance"
      ],
      "metadata": {
        "id": "CNSSnaEPbmaa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "\n",
        "truths = np.random.binomial(1,.25,25)\n",
        "\n",
        "truths"
      ],
      "metadata": {
        "id": "JequsmoeI_EZ",
        "outputId": "31e4578e-b76a-4a3f-c2f4-cbdb4dcde4ca",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
              "       0, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.patches import Rectangle\n",
        "\n",
        "fig, axs = plt.subplots(5, 5, constrained_layout=True, figsize=(2, 2))\n",
        "\n",
        "\n",
        "def hatches_plot(ax,h):\n",
        "    ax.add_patch(Rectangle((0, 0), 1, 1, fill=h))\n",
        "    ax.axis('equal')\n",
        "    ax.axis('off')\n",
        "\n",
        "for ax, h in zip(axs.flat,truths):\n",
        "    hatches_plot(ax,h)\n",
        "\n",
        "fig.suptitle('25% Chance')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "g-1ZBUqcHKlG",
        "outputId": "3a457c17-4649-4a1a-c3e8-33bc27407fe0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 169
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 144x144 with 25 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJgAAACYCAYAAAAYwiAhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAGR0lEQVR4nO3dXYhcdx2H8eebxErU1GALsaWapaZNim/Ftsa3GxF8q3oTiq1eiBeiQmiLRqz19UYpKF6IIlJLBYtB40uFFi30oqUXLdr6UpoqSO2m1SbFiE03+Fbk78U5C8d1dzOz29/O7Ob5wMDsnJn5nznn2XNmljN70lpDqrJp0jOgjc3AVMrAVMrAVMrAVMrAVMrAVijJTJKWZMuk52WaTXVgSZ6b5KYkR5LMJflNkncMps+v5JODy2cH0z+R5HiSw0leObj9jUluHWH8C5Mc6p/jRJIHk3wsyeZn/9VuUK21qb0Azwe+AMzQ/TK8C5gDZvrpM0ADtizy2HOA3wNnAvuB2/rbtwD3zT/HMmO/DPgb8FXgnP623cD3gO3Lje1lsBwnPQNjzzA8COzrry8X2F7gYH99D/Bwf/0AcP0I49wC3L7M9PmxPwA8BhwHPj2Y/lrgXuAp4CjwdeCMwfQGfAT4Q3+fbwAZTP8Q8Lv+F+ph4DX97ecCPwL+AjwKXD3pdbJhAgN2AP8E9ixYyX8G/gTcDJzdTzsLeKjf2uwHDgEvAe4fruhlxjoGfHCEwG4EtgKvBv4FXNRPvwR4Xb/FnOljuXZBYLf18/fSPpi399Ou6F/TZUCAXcBOuq34A8DngDOA84E/Am+b9LpZ94EBzwHuBL41uO0FwKX9StwB/BC4YzD9KuBXwM/6FfRj4C3Ae4G7gZ8C5y0x3jPzK/wUgZ03uO0XwJVL3P9a4CcLAnvT4OcfANf11+8ArlnkOfYCjy247VPAzZNeP0td1sUnoCSbgO8C/6bbGgHQWjtJt0UCeDLJfuBokm2ttbnW2kHgYP8cl9NtYX4N/BZ4OfAe4CvAlYsM+1e693Gncmxw/e900ZPkQrr3b5cCz6P7JXhglMfSbWkfWWSsncC5SZ4a3LYZuGeE+ZyIqf4UCZAkwE10W6h9rbVnlrn7/KEh//O6kmwFvgR8HLgAeLy19jTwS+BVSzzXncC+Vcz6N+k+ZFzQWjsTuJ5udzeKx+k+ZCx2+6Otte2Dy7bW2jtXMZ+lpj4wuhV1EfDu1to/hhOS7E2yO8mmJGcBXwPuaq2dWPAcnwG+01p7gu4N+e4kO4A3072HWczngTck+XKSF/fj7UpyS5LtI8z3NuBp4GSSPcBHR3u5AHwbOJDkknR2JdlJtwueS/LJJFuTbE7yiiSXjfHca2qqA+sX6oeBi4Fjg791vb+/y/nAz+k+aT1Etwu8asFz7AHeShcfrbWjwA3AYeBquvcw/6e19gjwerr3WoeTnKD79HZ/P96pHADe19/3RuD7I73obuxDwBfp/iQyB9wKvKi19h+6P9VcTPcJ8jhdjC8c9bnXWvo3ilKJqd6Caf0zMJUyMJUyMJUyMJUyMJUyMJUyMJUyMJUyMJUyMJUyMJUyMJUyMJUa+ZDpJLN0h+yu1JHW2sw4D5i57vZVHUs0e8Plox5BOtExJ7Fs12rMcY7J39laG3vhzUvigWdLm8SyXZMx3UWqlIGplIGplIGplIGplIGplIGplIGplIGplIGplIGplIGplIGplIGplIGplP8fTKXcgqmUgamUgamUgamUgamUganWGOcKmqU7k8ZKL7MrOD+RY67zMUf+O1iSttrv0Y37eMdc/2O6i1QpA1MpA1MpA1MpA1MpA1MpA1MpA1MpA1MpA1MpA1MpA1MpA1MpA1Opcf6N+ZFV/ivyIyt5jGOu7zH9XqRKuYtUKQNTKQNTKQNTKQNTKQNTqak+X+Tpco7Kjbxsp/18kafLOSo37LJ1F6lSBqZSBqZSBqZSBqZSBqZSBqZSBqZSBqZSBqZSBqZSBqZSBqZSBqZSfi9ygZUcz/Us2LDL1u9FqpS7SJUyMJUyMJUyMJUyMJUyMNWatvMLOubGGtPzRTpm6ZjuIlXKwFTKwFTKwFTKwFTKwFTKwFTKwFTKwFTKwFTKwFTKwFTKwFTKwFTK70U6ZumYfi9SpdxFqpSBqZSBqZSBqZSBqZSBqdRUny/SczfWjblWy3bazxc5CRv23I2T4C5SpQxMpQxMpQxMpQxMpQxMpQxMpQxMpQxMpQxMpQxMpQxMpQxMpQxMpcb5N+azrPExS5NwuhwPtlb8XqRKuYtUKQNTKQNTKQNTKQNTKQNTqf8CzXZv5vP5PBcAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### With Package"
      ],
      "metadata": {
        "id": "0h2onFyzbrn3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pywaffle"
      ],
      "metadata": {
        "id": "bK3nuBz-btsH",
        "outputId": "e64e2649-7bb8-4799-c0fa-d3e5668421db",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pywaffle\n",
            "  Downloading pywaffle-0.6.4-py2.py3-none-any.whl (565 kB)\n",
            "\u001b[?25l\r\u001b[K     |▋                               | 10 kB 21.6 MB/s eta 0:00:01\r\u001b[K     |█▏                              | 20 kB 11.3 MB/s eta 0:00:01\r\u001b[K     |█▊                              | 30 kB 8.8 MB/s eta 0:00:01\r\u001b[K     |██▎                             | 40 kB 7.9 MB/s eta 0:00:01\r\u001b[K     |███                             | 51 kB 6.0 MB/s eta 0:00:01\r\u001b[K     |███▌                            | 61 kB 7.1 MB/s eta 0:00:01\r\u001b[K     |████                            | 71 kB 6.6 MB/s eta 0:00:01\r\u001b[K     |████▋                           | 81 kB 6.5 MB/s eta 0:00:01\r\u001b[K     |█████▏                          | 92 kB 7.3 MB/s eta 0:00:01\r\u001b[K     |█████▉                          | 102 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |██████▍                         | 112 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |███████                         | 122 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |███████▌                        | 133 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |████████                        | 143 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |████████▊                       | 153 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |█████████▎                      | 163 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |█████████▉                      | 174 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |██████████▍                     | 184 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |███████████                     | 194 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |███████████▋                    | 204 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |████████████▏                   | 215 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |████████████▊                   | 225 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |█████████████▎                  | 235 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |██████████████                  | 245 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |██████████████▌                 | 256 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |███████████████                 | 266 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |███████████████▋                | 276 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |████████████████▏               | 286 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |████████████████▉               | 296 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |█████████████████▍              | 307 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |██████████████████              | 317 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |██████████████████▌             | 327 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |███████████████████             | 337 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |███████████████████▊            | 348 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |████████████████████▎           | 358 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |████████████████████▉           | 368 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▍          | 378 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |██████████████████████          | 389 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▋         | 399 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▏        | 409 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▊        | 419 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▎       | 430 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████       | 440 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▌      | 450 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████      | 460 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▋     | 471 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▏    | 481 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▉    | 491 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▍   | 501 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████   | 512 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▌  | 522 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▏ | 532 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▊ | 542 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▎| 552 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▉| 563 kB 7.0 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 565 kB 7.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from pywaffle) (3.2.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pywaffle) (1.4.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pywaffle) (3.0.8)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pywaffle) (0.11.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pywaffle) (2.8.2)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pywaffle) (1.21.6)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->pywaffle) (4.2.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->pywaffle) (1.15.0)\n",
            "Installing collected packages: pywaffle\n",
            "Successfully installed pywaffle-0.6.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pywaffle import Waffle\n",
        "\n",
        "fig = plt.figure(\n",
        "    FigureClass=Waffle, \n",
        "    rows=5, \n",
        "    columns=10, \n",
        "    values=[48, 46, 6],\n",
        "    figsize=(5, 3)\n",
        ")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "WlnLx72Lb03u",
        "outputId": "840dcfac-2b28-4b87-dcc8-a62426308d5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Waffle size 360x216 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`pywaffle` is actually a pretty cool package for doing this sort of graphic!  For my 25% chance above, I'd do something like:"
      ],
      "metadata": {
        "id": "i6OxkHKQb8sn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(FigureClass = Waffle,\n",
        "           rows= 5,\n",
        "           columns = 5,\n",
        "           values = [5,20],\n",
        "           colors = ['blue','white'],\n",
        "           vertical = True,\n",
        "           facecolor='#DDDDDD' )\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "dCilhhvmcKj3",
        "outputId": "1b479968-a22c-4b62-da96-b28f66f36d29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Waffle size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The package has lots of options to include some really cool graphics.  Here is how many times I biked versus driving this week."
      ],
      "metadata": {
        "id": "lu1xvp3leDT0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(FigureClass=Waffle,\n",
        "           rows = 5,\n",
        "           values = [2,3],\n",
        "           icons = ['bicycle','car'])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "6KZSPOeldi6P",
        "outputId": "f52fc6ea-04e5-48d7-fd6b-f13836a831d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Waffle size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Error Bars for Mean"
      ],
      "metadata": {
        "id": "q3ws6HY065Xe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Perhaps the most fundamental concept in error is the mean.  We know that the mean will be approximately distributed as normal for a large enough sample size with standard deviation $\\frac s{\\sqrt n}$ where $s$ is the sample standard deviation and $n$ the sample size.  Let's see that visualized in a bar graph."
      ],
      "metadata": {
        "id": "xglswNA76-Cq"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "id": "GkYz6MMH6kQ7",
        "outputId": "c967a726-e616-4d73-d0aa-62c51400b370"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2a5c169fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import pandas as pa\n",
        "\n",
        "df = pa.read_csv('https://raw.githubusercontent.com/nurfnick/Data_Viz/main/Data_Sets/iris.csv')\n",
        "\n",
        "dfgrouped = df.groupby('Class').agg(['mean','std', 'count'])\n",
        "\n",
        "dfgrouped.SepalLength.plot.bar(y = 'mean',yerr = 'std', legend = False, color = ['purple','red', 'blue'], title = \"Error of Standard Deviation\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from scipy.stats import t\n",
        "\n",
        "def SE(std,n):\n",
        "  return std/np.sqrt(n)\n",
        "\n",
        "\n",
        "\n",
        "dfgroupedSepalLength = dfgrouped.SepalLength\n",
        "\n",
        "dfgroupedSepalLength['SE'] = dfgroupedSepalLength.apply(lambda x: SE(x['std'],x['count']), axis = 1)\n",
        "\n",
        "\n",
        "dfgroupedSepalLength.loc[:,'95%'] = dfgroupedSepalLength.loc[:,'SE']*t.ppf(.975,49)\n",
        "\n",
        "dfgroupedSepalLength\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "zV1Ym-sE9sYW",
        "outputId": "4fcae12f-3508-4f62-ee4a-276857fba34b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  # This is added back by InteractiveShellApp.init_path()\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1667: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self.obj[key] = value\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  mean       std  count        SE       95%\n",
              "Class                                                      \n",
              "Iris-setosa      5.006  0.352490     50  0.049850  0.100176\n",
              "Iris-versicolor  5.936  0.516171     50  0.072998  0.146694\n",
              "Iris-virginica   6.588  0.635880     50  0.089927  0.180715"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3eb798e8-b185-49b6-b12b-8af6174a0a16\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>count</th>\n",
              "      <th>SE</th>\n",
              "      <th>95%</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Class</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Iris-setosa</th>\n",
              "      <td>5.006</td>\n",
              "      <td>0.352490</td>\n",
              "      <td>50</td>\n",
              "      <td>0.049850</td>\n",
              "      <td>0.100176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iris-versicolor</th>\n",
              "      <td>5.936</td>\n",
              "      <td>0.516171</td>\n",
              "      <td>50</td>\n",
              "      <td>0.072998</td>\n",
              "      <td>0.146694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iris-virginica</th>\n",
              "      <td>6.588</td>\n",
              "      <td>0.635880</td>\n",
              "      <td>50</td>\n",
              "      <td>0.089927</td>\n",
              "      <td>0.180715</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3eb798e8-b185-49b6-b12b-8af6174a0a16')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3eb798e8-b185-49b6-b12b-8af6174a0a16 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3eb798e8-b185-49b6-b12b-8af6174a0a16');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dfgroupedSepalLength.plot.bar(y = 'mean',yerr = 'SE', title = 'Graphed with Standard Error' )"
      ],
      "metadata": {
        "id": "3n6oqPbuBOnH",
        "outputId": "bfeed471-5413-47d4-b8b5-0c81138b1292",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2a5bb85d10>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dfgroupedSepalLength.plot.bar(y = 'mean',yerr = '95%', title = 'Graphed with 95% Confidence Interval' )"
      ],
      "metadata": {
        "id": "mOiVkOd6EjrH",
        "outputId": "0d9b47d9-694d-430f-e95b-3bfad6f1e20d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2a5bb0ce90>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Confidence Interval for Regression"
      ],
      "metadata": {
        "id": "_TrTteh49cEc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "It is automatically generated with `seaborn`.  It is computed with a bootstrap(!)."
      ],
      "metadata": {
        "id": "YlUEjP4d99r_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "sns.lmplot(data = df, \n",
        "            x = 'SepalLength', \n",
        "            y = 'SepalWidth',\n",
        "            hue = 'Class')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "C8kEOB9as8uq",
        "outputId": "3f2949a1-e8e9-4de1-9a7f-0fcabb97db4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 463.25x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Hypothesis Testing"
      ],
      "metadata": {
        "id": "jNz1ZH_FPiar"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy import stats\n",
        "\n",
        "x = [x for x in np.arange(-4,4,.1)]\n",
        "x_trunk = [i for i in x if i<2]\n",
        "\n",
        "plt.plot(x, stats.norm.pdf(x, 0, 1))\n",
        "plt.fill_between(x_trunk, 0, stats.norm.pdf(x_trunk,0,1))\n",
        "plt.annotate(r'$p$ value', xy = [2.5,.01],\n",
        "            xytext = [3,.15],\n",
        "            arrowprops = dict(facecolor = 'black', width = 3, headwidth = 12, headlength = 6))\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "JRVSC8sf9shx",
        "outputId": "7352ff27-ae74-4e4d-950f-c26ad89fdbd6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Your Turn\n",
        "\n",
        "1. Explain the difference between standard error and confidence intervals.\n",
        "2. Use the workout data and graph the average calories by workout type and include the 95% confidence interval."
      ],
      "metadata": {
        "id": "cGRTC-tVnmHR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "39r774BroJvy"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}