W3_Tutorial3study.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question: What does the code line14(x = linspace(0,1,1000) mean?\n",
    "This is for the random variable x which has a 1000 sample interval size from 0 to 1.\n",
    "\n",
    "***line14*** x = linspace(0,1,1000);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Why we put variable $\\mathbf{x}$ as a vector in programming?*** I think the reason is to plot a graph.\n",
    "Let's suppose we have a smaller sample variable $x$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if i want to plot a function f(x)=x? Then, we can call another variable y as a function of x and then assign this to the x. So, y=x."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ca01f20788>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xVQwhQS2cjiV+QIUu4iEnSiuY934mb361m/AOrfn73cO4KLqz07HEj6jQRTxg3dY8pi9NY19hMXecH8XDV/SnjYZpSSPTd5zIWThysozZKzNY8t1e+nRuw+L7RzKkp4ZpiTNU6CJnwFrL6rQDzFyextGich68tC8P/qKvhmmJo1ToIqcp71gJjy1PY036QWJ7tOONu4YxqLuGaYnzVOgibrLW8u6mXJ5cmUFphYvEKwdwz4W9aK5hWtJEqNBF3JBTUMQjS1L5IusQw6I6MndSHL07a5iWNC0qdJGfUemyvPHlLp5ds5VmBmZfG8uvhkVqmJY0SSp0kVPYfvA405JS+G7PUUb178xTE+Po0b6107FETkmFLlJLeaWLl9bt4P8+ySKoZQDP33gO156rYVrS9KnQRWpIyT3K1MUpZB44zlXx3Xh8/CBC27Z0OpaIW1ToIlQN03r+w2288nk2oW1b8vKtQ7hiUFenY4mcFhW6+L0N2YdJTEph1+EiJg+N4JFxAwlprWFa4n1U6OK3jpeUM3d1Jm99vYeIjq15657hXNA31OlYImdMhS5+aW1mHo8uTeXAsRLuvrAXv7+8H0GB+nEQ76bvYPErBSfLeOK9dJb9sI/osLYkPXA+gyM7OB1LxCNU6OIXrLWsSNnPrOR0CovL+e3oaH5zaR9aNtcwLfEdKnTxeQcKS5ixLI2PthwkPjyEt+4dzoCu7ZyOJeJxKnTxWdZaFm3M4emVWyirdDF93EDuvCBKw7TEZ6nQxSftPnySxKRUvso+zPBeHXlmUjxRoW2cjiXSoFTo4lMqXZbX1u/kTx9spXmzZjw9MY7JQyM0TEv8ggpdfMbWA8eZmpTC5pyjjB4QxpMTY+kWomFa4j9U6OL1yipcvLgui/lrswhu1YIXJp/L+HO6a5iW+B0Vuni1zTlVw7S2HjzOhHO7M/PqGDppmJb4KRW6eKXiskr+58Ot/O2LnYQFt+LV2xIYE9PF6VgijlKhi9f5cschEpNS2VNQxM3DI0m8cgDtWmmYlogKXbzGsZJy5qzK5O1v9tCzUxBv3zuCkX06OR1LpMlQoYtX+CjjINOXpZJ/vJQpF/fmoTH9aB2oy/ZFanLrkjljzFhjzFZjTJYxJvFn9rveGGONMQmeiyj+7PCJUn779vfc8+YmOgQFsvTXF/DouIEqc5E61HuEbowJAOYDlwG5wEZjTLK1NqPWfsHAb4GvGyKo+BdrLcmb9zErOZ0TpRU8NKYfD4zqQ2BzXbYvcirunHIZBmRZa7MBjDGLgAlARq39ZgPzgD94NKH4nf2FxcxYmsbHmXmcG9GeedfH069LsNOxRJo8dwq9B5BT434uMLzmDsaY84AIa+0KY8wpC90YMwWYAhAZGXn6acWnuVyWtzfuYc6qTCpdlhlXDeTOC3oRoMv2RdziTqHX9dNkf9xoTDPgeeCO+h7IWrsAWACQkJBg69ld/MjOQydJTErh650FXNC3E3MmxhPZKcjpWCJexZ1CzwUiatwPB/bVuB8MxALrqi+17gokG2PGW2s3eSqo+KaKShcL1+/kuQ+2Edi8Gc9MiuOXCRG6bF/kDLhT6BuBaGNML2AvMBm4+d8brbWFwI+frGuMWQf8QWUu9ck8cIxpi1PYnFvIZTFdePLaWLq0a+V0LBGvVW+hW2srjDEPAmuAAGChtTbdGPMEsMlam9zQIcW3lFZUMn/tDl5cm0VI6xb85ebzuCqum47KRc6SWxcWWWtXAatqfW3mKfYddfaxxFd9t+cI0xansD3vBNed14PHro6hQ5tAp2OJ+ARdKSqNoqisguc+2MbC9Tvp1q4Vr905lEv7hzkdS8SnqNClwa3POkTikhRyCoq5dURPpo7tT7CGaYl4nApdGkxhcTlPr9zCvzbl0Cu0Df+aMoLhvTVMS6ShqNClQXyQfoAZy9I4fLKM+y/pw/8bE02rFpq/ItKQVOjiUfnHS5n1XjorU/YzsFs7/nb7UOLCQ5yOJeIXVOjiEdZalv2wl8ffy6CotJI/XN6P+y7pQ4sADdMSaSwqdDlre48WM31pKuu25jM4smqYVt8wDdMSaWwqdDljLpflra93M3d1JhaYdU0Mt46M0jAtEYeo0OWMZOefIDEplW92FXBRdChPT4wjoqOGaYk4SYUup6Wi0sUrn+/k+Y+20ap5M569Pp7rh4Trsn2RJkCFLm7L2HeMqUmbSdt7jCsGdWH2hFjCNExLpMlQoUu9Ssor+csnWbz06Q7aBwXy118N5sq4bk7HEpFaVOjys77dXcDUxSnsyD/JpMHhPHb1QNoHaZiWSFOkQpc6nSyt4Nk1W3njq110D2nNG3cN45J+nZ2OJSI/Q4UuP/HZtnweWZLKvsJibhvRk4fHDqBtS32riDR1+imVHxUWlTN7ZQaLv82ld+c2vHPfSIZGdXQ6loi4SYUuALyftp/HlqdTcLKMX4/qw29Ha5iWiLdRofu5vOMl/HF5OqvTDhDTrR2v3TGU2B4apiXijVTofspaS9J3e5m9IoPi8kqmju3PvRf11jAtES+mQvdDuUeKeHRpGp9ty2doVAfmToqnT+e2TscSkbOkQvcjLpfl7xt288z7mRjgiQmDuGV4T5ppmJaIT1Ch+4msvBMkJqWwafcRLu7XmacnxhLeQcO0RHyJCt3HlVe6WPBZNi98tJ3WgQE8d8M5XDe4h4ZpifggFboPS9tbyNTFKWTsP8a4uK48Pj6WzsEtnY4lIg1Ehe6DSsoreeHj7Sz4LJuObQJ56ZYhjI3t6nQsEWlgKnQfs3FXAdMWp5B96CS/TAhn+rgYQoJaOB1LRBqBCt1HnCitYN77mbz51W7CO7TmH3cP58LoUKdjiUgjUqH7gHVb85i+NI19hcXceUEUf7i8P200TEvE7+in3osdOVnG7JUZLPluL33D2rL4/vMZ0rOD07FExCEqdC9krWV12gFmLk/jaFE5//WLvjz4i760bK5hWiL+zK1CN8aMBV4AAoBXrbVza23/HXAPUAHkA3dZa3d7OKsAecdKeGx5GmvSDxLXI4Q37xpOTPd2TscSkSag3kI3xgQA84HLgFxgozEm2VqbUWO374EEa22RMeYBYB5wY0ME9lfWWt79NpcnV2RQWuHikSsHcPeFvWiuYVoiUs2dI/RhQJa1NhvAGLMImAD8WOjW2rU19t8A3OLJkP4up6CIR5ak8kXWIYb16sjc6+LorWFaIlKLO4XeA8ipcT8XGP4z+98NrK5rgzFmCjAFIDIy0s2I/qvSZXnjy108u2YrAc0MT14by83DIjVMS0Tq5E6h19Uets4djbkFSAAuqWu7tXYBsAAgISGhzseQKtsPHmdqUgrf7znKqP6deWpiHD3at3Y6log0Ye4Uei4QUeN+OLCv9k7GmDHAdOASa22pZ+L5n7IKFy9/uoP/+ySLNi0D+N8bz2XCud01TEtE6uVOoW8Eoo0xvYC9wGTg5po7GGPOA14Gxlpr8zye0k+k5B5l6uIUMg8c55pzuvPHa2IIbathWiLinnoL3VpbYYx5EFhD1dsWF1pr040xTwCbrLXJwLNAW+Dd6iPJPdba8Q2Y26eUlFfy/IfbeOXzbDoHt+SV2xK4LKaL07FExMu49T50a+0qYFWtr82scXuMh3P5jQ3Zh0lMSmHX4SJuGhZB4pUDCWmtYVoicvp0pahDjpeUM3d1Jm99vYfIjkH8857hnN9Xw7RE5Myp0B3wSeZBpi9N4+CxEu65sBe/u7wfQYF6KUTk7KhFGlHByTKeeC+dZT/so1+Xtrz4q/M5L1LDtETEM1TojcBay3sp+5mVnM7xknL+e3Q0v7m0L4HNddm+iHiOCr2BHSgsYcayND7acpBzwkN45vrhDOiqYVoi4nkq9AZirWXRxhyeXrmFcpeL6eMGcteFvQjQZfsi0kBU6A1g9+GTJCal8lX2YUb07sjc6+KJCm3jdCwR8XEqdA+qdFleW7+TP32wlRbNmjHnujgmD43QZfsi0ihU6B6y9UDVMK3NOUcZMzCMJ6+No2tIK6djiYgfUaGfpbIKFy+uy2L+2iyCW7XgzzedxzXx3XRULiKNToV+Fn7IOcq0xSlsPXicCed254/XDKJjm0CnY4mIn1Khn4Hiskqe+2ArC9fvJCy4FX+7PYHRAzVMS0ScpUI/TV/uOERiUip7Cor41fBIEq8cQHArDdMSEeep0N10rKScOau28PY3OUR1CmLRlBGM6N3J6VgiIj9Sobvho4yDTF+WSv7xUqZc3JuHxvSjdWCA07FERP6DCv1nHD5Ryqz3Mnhv8z4GdA3mldsSiA9v73QsEZE6qdDrYK1l+Q/7ePy9dE6UVvC7y/px/yV9NExLRJo0FXot+44WM2NZGp9k5nFeZHvmTYonukuw07FEROqlQq/mcln++c0e5q7OpNJlmXl1DLefH6VhWiLiNVTowM5DJ0lMSuHrnQVc0LcTcybGE9kpyOlYIiKnxa8LvaLSxd++2Mn/fLiNwObNmDcpnhsSwnXZvoh4Jb8t9C37jzEtKYWU3EIui+nCk9fG0qWdhmmJiPfyu0Ivrahk/idZvLhuB+2DWjD/5sGMi+uqo3IR8Xp+Vejf7j7CtKQUsvJOcN15PXjs6hg6aJiWiPgIvyj0orIKnl2zlde/3EW3dq147c6hXNo/zOlYIiIe5fOF/sX2QyQuSSH3SDG3jezJ1LEDaNvS55ctIn7IZ5utsLicp1Zm8M6mXHqFtuGd+0YyrFdHp2OJiDQYnyz0NekHeGxZGodPlvHAqD789+hoWrXQMC0R8W0+Vej5x0uZlZzOytT9DOzWjr/dPpS48BCnY4mINAqfKHRrLUu+28sTKzIoLqvk4Sv6M+Xi3rQI0DAtEfEfXl/oe48W8+iSVD7dls/gyPbMuz6evmEapiUi/setQjfGjAVeAAKAV621c2ttbwm8CQwBDgM3Wmt3eTbqf3K5LP/4ejfPrM7EArOuieHWkRqmJSL+q95CN8YEAPOBy4BcYKMxJtlam1Fjt7uBI9bavsaYycAzwI0NERhgR/4JEpNS2LjrCBdFh/L0xDgiOmqYloj4N3eO0IcBWdbabABjzCJgAlCz0CcAs6pvLwb+Yowx1lrrwawAvLMxhxnL02jVvBnPXh/P9UM0TEtEBNwr9B5ATo37ucDwU+1jra0wxhQCnYBDNXcyxkwBpgBERkaeUeBendswekAYj08YRFiwhmmJiPybO4Ve1+Fv7SNvd/bBWrsAWACQkJBwRkfvQ6M6MjRKFwiJiNTmzvv6coGIGvfDgX2n2scY0xwIAQo8EVBERNzjTqFvBKKNMb2MMYHAZCC51j7JwO3Vt68HPmmI8+ciInJq9Z5yqT4n/iCwhqq3LS601qYbY54ANllrk4G/AX83xmRRdWQ+uSFDi4jIT7n1PnRr7SpgVa2vzaxxuwS4wbPRRETkdOjaeBERH6FCFxHxESp0EREfoUIXEfERxql3Fxpj8oHdZ/jXQ6l1Faof0Jr9g9bsH85mzT2ttZ3r2uBYoZ8NY8wma22C0zkak9bsH7Rm/9BQa9YpFxERH6FCFxHxEd5a6AucDuAArdk/aM3+oUHW7JXn0EVE5Ke89QhdRERqUaGLiPiIJl3oxpixxpitxpgsY0xiHdtbGmP+Vb39a2NMVOOn9Cw31vw7Y0yGMSbFGPOxMaanEzk9qb4119jvemOMNcZ4/Vvc3FmzMeaX1a91ujHmn42d0dPc+N6ONMasNcZ8X/39Pc6JnJ5ijFlojMkzxqSdYrsxxvy5+t8jxRgz+Kyf1FrbJP9QNap3B9AbCAQ2AzG19vk18FL17cnAv5zO3QhrvhQIqr79gD+suXq/YOAzYAOQ4HTuRnido4HvgQ7V98Oczt0Ia14APFB9OwbY5XTus1zzxcBgIO0U28cBq6n6xLcRwNdn+5xN+Qj9xw+nttaWAf/+cOqaJgBvVN9eDIw23v2J0fWu2Vq71lpbVH13A1WfIOXN3HmdAWYD84CSxgzXQNxZ873AfGvtEQBrbV4jZ/Q0d9ZsgXbVt0P46SejeRVr7Wf8/Ce3TQDetFU2AO2NMd3O5jmbcqHX9eHUPU61j7W2Avj3h1N7K3fWXNPdVP0X3pvVu2ZjzHlAhLV2RWMGa0DuvMmLBbkAAAHdSURBVM79gH7GmPXGmA3GmLGNlq5huLPmWcAtxphcqj5/4b8aJ5pjTvfnvV5ufcCFQzz24dRexO31GGNuARKASxo0UcP72TUbY5oBzwN3NFagRuDO69ycqtMuo6j6v7DPjTGx1tqjDZytobiz5puA1621zxljRlL1KWix1lpXw8dzhMf7qykfofvjh1O7s2aMMWOA6cB4a21pI2VrKPWtORiIBdYZY3ZRda4x2ct/Meru9/Zya225tXYnsJWqgvdW7qz5buAdAGvtV0ArqoZY+Sq3ft5PR1MudH/8cOp611x9+uFlqsrc28+rQj1rttYWWmtDrbVR1tooqn5vMN5au8mZuB7hzvf2Mqp+AY4xJpSqUzDZjZrSs9xZ8x5gNIAxZiBVhZ7fqCkbVzJwW/W7XUYAhdba/Wf1iE7/Jrie3xKPA7ZR9dvx6dVfe4KqH2ioesHfBbKAb4DeTmduhDV/BBwEfqj+k+x05oZec6191+Hl73Jx83U2wP8AGUAqMNnpzI2w5hhgPVXvgPkBuNzpzGe53reB/UA5VUfjdwP3A/fXeI3nV/97pHri+1qX/ouI+IimfMpFREROgwpdRMRHqNBFRHyECl1ExEeo0EVEfIQKXUTER6jQRUR8xP8HXTY7Zz+oPiIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y) #let’s plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a result, we now have a visualization to see what the vector $\\mathbf{x}$ means through the graph.\n",
    "Our sample variable vector $\\mathbf{x}$ was having the same interval and increasing with the same gradient so it returns the linear line graph. And I can say this is a technique to plot random variable $x$ in the plot."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}