{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "view-in-github",
    "colab_type": "text"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/ebatty/MathToolsforNeuroscience/blob/jupyterbook/Week7/Video72.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r-xn3WLqP2wM"
   },
   "source": [
    "# Video 7.2: Overview of Statistical Inference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T9ckFFHNP2wQ"
   },
   "source": [
    "## Video"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "fTCqwicVP2wQ",
    "cellView": "form",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "outputId": "f09ea793-a2f7-4c76-bd4c-9fd68e2fcbd3",
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Video available at https://youtube.com/watch?v=VZDJfR99yAo\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7ff07798aad0>"
      ],
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"730\"\n",
       "            height=\"410\"\n",
       "            src=\"https://www.youtube.com/embed/VZDJfR99yAo?fs=1\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "image/jpeg": "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\n"
     },
     "metadata": {},
     "execution_count": 1
    }
   ],
   "source": [
    "# @markdown\n",
    "from IPython.display import YouTubeVideo\n",
    "from ipywidgets import widgets\n",
    "out = widgets.Output()\n",
    "with out:\n",
    "    display(YouTubeVideo(id=f\"VZDJfR99yAo\", width=730, height=410, fs=1))\n",
    "display(out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XMhhhdFCP2wS"
   },
   "source": [
    "## Notes\n",
    "\n",
    "<a href=\"https://osf.io/ufsa7/download\">\n",
    "    Click here to download notes\n",
    "</a>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "GGct1SKRP2wZ",
    "outputId": "de9de0b7-f766-449a-bb76-8b6804aeefed",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "cellView": "form",
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7ff072f28190>"
      ],
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"730\"\n",
       "            height=\"1000\"\n",
       "            src=\"https://mfr.osf.io/render?url=https://osf.io/ufsa7/?direct%26mode=render%26action=download%26mode=render\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ]
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "# @markdown\n",
    "from IPython.display import IFrame\n",
    "from ipywidgets import widgets\n",
    "out = widgets.Output()\n",
    "with out:\n",
    "    display(IFrame(src=f\"https://mfr.osf.io/render?url=https://osf.io/ufsa7/?direct%26mode=render%26action=download%26mode=rende\", width=730, height=410))\n",
    "display(out)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.13 ('mathtools')",
   "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": "3.8.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "2f26a17d6a9624f855b5f9e02ffdc0ae71c8577a2562f495dada4b6f29d8bdd3"
   }
  },
  "colab": {
   "provenance": [],
   "include_colab_link": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}