3.1-Correlation.ipynb

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   "source": [
    "# <font color = \"orange\">Correlation</font>"
   ]
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    "## 1.0 General Explanation about <font color =\"orange\">Correlation</font>\n",
    "\n",
    "-  Correlation shows the strength of relationship that occurs between two variables <font color = blue>(independent and dependent variable)</font> linearly.\n",
    "-  Correlation is not causation - it does not implies the influence/impact of the variables to another variables.\n",
    "<img src=\"https://drive.google.com/uc?id=14oYb2z_xyM73dJ8IyZfF7dsEJJBGZgJ7\" width = 800 height = 300>\n",
    "\n"
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   "source": [
    "### 1.1 Further explanation in <font color = \"orange\">Correlation</font>"
   ]
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    "%%HTML\n",
    "\n",
    "<iframe width=\"700\" height=\"350\" src=\"https://www.youtube.com/embed/Ypgo4qUBt5o\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
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    "### 1.2 Type of relation in <font color = \"orange\">Correlation</font>\n",
    "\n",
    "<img src=\"https://drive.google.com/uc?id=1WoMj4UrKl-a5qlw_yamgKo-2jWYrOWGV\" width = 800 height = 300>\n",
    "\n",
    "\n",
    "**<font color=\"blue\">1.  Positive linear correlation</font>** \n",
    "    -  when x is increasing, y is also increasing.\n",
    "    -  shows a positive gradient.\n",
    "    -  shows the direction of line in a positive way.\n",
    "\n",
    "**<font color=\"blue\">2.  Negative linear correlation</font>** \n",
    "    -  when x is increasing, y is decreasing.\n",
    "    -  shows a negative gradient.\n",
    "    -  shows the direction of line in a negative way.\n",
    "\n",
    "**<font color=\"blue\">3.  Zero Correlation</font>**\n",
    "    -  when x increases or decreases, there will be no change to y.\n",
    "    -  x is not affecting y or x has no relationship with y.\n",
    "    -  shows a zero gradient, m = 0."
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    "### 1.3 Formula for <font color = \"orange\">Correlation</font>\n",
    "\n",
    "<img src=\"https://drive.google.com/uc?id=100L1Ib8MgBgWkSCmxI-IVyopCXY_gUZ2\" width = 600 height = 200>\n",
    "\n",
    "<img src=\"https://drive.google.com/uc?id=1szgAtSv0d55aSr06rPJcrJpIb3BS30xH\" width = 800 height = 300>\n",
    "\n",
    "-   r = correlation coefficient\n",
    "-   N = number of sample\n",
    "-  ∑x = summation of x\n",
    "-  ∑y = summation of y\n",
    "- ∑x2 = summation of x2\n",
    "- ∑y2 = summation of y2\n",
    "- (∑x)2 = summation of x-squared\n",
    "- (∑y)2 = summation of y-squared"
   ]
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    "## 2.0 Example 1 for <font color=\"orange\">Correlation</font>\n",
    "\n",
    "The local ice cream shop keeps track of how much ice cream they sell versus the temperature on that day, here are their figures for the last 12 days:"
   ]
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       "            text-align:  center;\n",
       "        }</style><table id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Temperature</th>        <th class=\"col_heading level0 col1\" >Sales</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002row0_col0\" class=\"data row0 col0\" >14.200000</td>\n",
       "                        <td id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002row0_col1\" class=\"data row0 col1\" >215</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002row1_col0\" class=\"data row1 col0\" >16.400000</td>\n",
       "                        <td id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002row1_col1\" class=\"data row1 col1\" >325</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002row2_col0\" class=\"data row2 col0\" >11.900000</td>\n",
       "                        <td id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002row2_col1\" class=\"data row2 col1\" >185</td>\n",
       "            </tr>\n",
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       "                        <th id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002row3_col0\" class=\"data row3 col0\" >15.200000</td>\n",
       "                        <td id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002row3_col1\" class=\"data row3 col1\" >332</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002row4_col0\" class=\"data row4 col0\" >18.500000</td>\n",
       "                        <td id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002row4_col1\" class=\"data row4 col1\" >406</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002row5_col0\" class=\"data row5 col0\" >22.100000</td>\n",
       "                        <td id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002row5_col1\" class=\"data row5 col1\" >522</td>\n",
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       "                        <th id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002row6_col0\" class=\"data row6 col0\" >19.400000</td>\n",
       "                        <td id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002row6_col1\" class=\"data row6 col1\" >412</td>\n",
       "            </tr>\n",
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       "                        <th id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002row7_col0\" class=\"data row7 col0\" >25.100000</td>\n",
       "                        <td id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002row7_col1\" class=\"data row7 col1\" >614</td>\n",
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       "                        <th id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002row8_col0\" class=\"data row8 col0\" >23.400000</td>\n",
       "                        <td id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002row8_col1\" class=\"data row8 col1\" >544</td>\n",
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       "                        <th id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002row9_col0\" class=\"data row9 col0\" >18.100000</td>\n",
       "                        <td id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002row9_col1\" class=\"data row9 col1\" >421</td>\n",
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       "                        <td id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002row10_col0\" class=\"data row10 col0\" >22.600000</td>\n",
       "                        <td id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002row10_col1\" class=\"data row10 col1\" >445</td>\n",
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       "                        <th id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002row11_col0\" class=\"data row11 col0\" >17.200000</td>\n",
       "                        <td id=\"T_126b937a_1414_11eb_8b45_0242ac1c0002row11_col1\" class=\"data row11 col1\" >408</td>\n",
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    "#creating table using pandas library by inserting data\n",
    "import pandas as pd\n",
    "\n",
    "temperature = [14.2, 16.4, 11.9, 15.2, 18.5, 22.1, 19.4, 25.1, 23.4, 18.1, 22.6, 17.2]\n",
    "sales = [215, 325, 185, 332, 406, 522, 412, 614, 544, 421, 445, 408]\n",
    "\n",
    "question_table = pd.DataFrame(temperature, columns = ['Temperature']);question_table['Sales'] = sales\n",
    "question_table\n",
    "question_table.style.set_properties(**{'text-align': 'center'})"
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    "<img src=\"https://drive.google.com/uc?id=1aKjWp55tQHn-ApiP90rsIuYv4RTMvnh-\" width = 600 height = 300>\n",
    "\n",
    "<img src=\"https://drive.google.com/uc?id=1Nb7525F94d21-ut0MUd1-urgKF4be8E4\" width = 600 height = 500>\n"
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   "source": [
    "### 2.1 Calculating the <font color = \"orange\">Correlation</font> coefficient step by step:"
   ]
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     "text": [
      "1. Mean of Temperature (celcius) is  18.675\n",
      "2. Mean of Ice Cream Sales ($) is  402.4166666666667\n",
      "3. Summation of (a x b) is  5325.0250000000015\n",
      "4. Summation of a-squared is  176.98250000000002\n",
      "5. Summation of b-squared is  174754.91666666672\n",
      "6. The correlation coefficient, r is  0.9575066230015952\n"
     ]
    }
   ],
   "source": [
    "x = [14.2, 16.4, 11.9, 15.2, 18.5, 22.1, 19.4, 25.1, 23.4, 18.1, 22.6, 17.2]\n",
    "y = [215, 325, 185, 332, 406, 522, 412, 614, 544, 421, 445, 408]\n",
    "\n",
    "#1st Step: Find the mean of x and mean of y\n",
    "meanX = sum(x) / len(x)\n",
    "meanY = sum(y) / len(y)\n",
    "\n",
    "print( \"1. Mean of Temperature (celcius) is \", meanX)\n",
    "print (\"2. Mean of Ice Cream Sales ($) is \", meanY)\n",
    "\n",
    "#2nd Step: Subtract the mean of x from every x value (assign as a), and the mean of y from y value (assign as as b)\n",
    "a = []\n",
    "for i in x:\n",
    "    differentFromMeanX = i - meanX\n",
    "    a.append(differentFromMeanX)\n",
    "    #print \"List of a is \", a\n",
    "\n",
    "b = []\n",
    "for i in y:\n",
    "    differentFromMeanY = i - meanY\n",
    "    b.append(differentFromMeanY)\n",
    "    #print \"List of b is \", b\n",
    "\n",
    "#3rd Step: Calculate (a x b), summation of a-squared and summation of b-squared\n",
    "ab = []\n",
    "for i in range(len(a)):\n",
    "    ab.append(a[i]*b[i])\n",
    "    #print \"List of (a x b) is \", ab\n",
    "\n",
    "aSquared = []\n",
    "for i in x:\n",
    "    differentFromMeanX = i - meanX\n",
    "    aSquared.append((differentFromMeanX)**2)\n",
    "    #print \"List of a-squared is \", aSquared\n",
    "\n",
    "bSquared = []\n",
    "for i in y:\n",
    "    differentFromMeanY = i - meanY\n",
    "    bSquared.append((differentFromMeanY)**2)\n",
    "    #print \"List of b-squared is \", bSquared\n",
    "\n",
    "#4th Step: Sum up ab, sum up a-squared and sum up b-squared\n",
    "print( \"3. Summation of (a x b) is \", sum(ab))\n",
    "print (\"4. Summation of a-squared is \", sum(aSquared))\n",
    "print (\"5. Summation of b-squared is \", sum(bSquared))\n",
    "\n",
    "#5th Step: Divide the sum of ab by the square root of [(sum of a-squared) x (sum of b-squared)]\n",
    "AB = ((sum(aSquared)) * (sum(bSquared)))\n",
    "sqrtAB = AB ** (0.5)\n",
    "\n",
    "r = sum(ab) / sqrtAB\n",
    "print (\"6. The correlation coefficient, r is \", r)"
   ]
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   "source": [
    "### 2.2 Straight away <font color = \"orange\">USE NUMPY</font>!"
   ]
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       "array([[1.        , 0.95750662],\n",
       "       [0.95750662, 1.        ]])"
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   "source": [
    "import numpy as np \n",
    "np.corrcoef(x, y)"
   ]
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = [14.2, 16.4, 11.9, 15.2, 18.5, 22.1, 19.4, 25.1, 23.4, 18.1, 22.6, 17.2]\n",
    "y = [215, 325, 185, 332, 406, 522, 412, 614, 544, 421, 445, 408]\n",
    "\n",
    "plt.scatter(x, y, c = \"blue\", s = 100, marker = \"o\")\n",
    "plt.xlabel(\"Temperature (Celcius)\", fontsize = 12); plt.ylabel(\"Ice Cream Sales ($)\", fontsize = 12)\n",
    "plt.xticks(size = 10); plt.yticks(size = 10)\n",
    "plt.title(\"Temperature (Celcius) versus Ice Cream Sales ($)\", fontsize = 15)\n",
    "plt.legend(fontsize = 12, loc = (1, 1))\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OIg4g5zQQpBn"
   },
   "source": [
    "-  We can easily see that <font color=\"red\">**the warmer the weather, the higher sales go together**</font>.\n",
    "-  Or mathematically we can say that, <font color=\"red\">**as temperature increase, sales are also increase**</font>.\n",
    "-  The relationship is good but not perfect. In fact the correlation is 0.9575."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jNuvvpEtQpBq"
   },
   "source": [
    "## Example 2 for <font color = \"orange\">Correlation</font>\n",
    "\n",
    "Study was conducted to investigate the relationship between <font color = \"red\">score in Data Science</font> and <font color = \"red\">weight in kilogram</font> among students."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 460
    },
    "executionInfo": {
     "elapsed": 933,
     "status": "ok",
     "timestamp": 1603338664042,
     "user": {
      "displayName": "Ummu Amirah",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjP1NlpYoc_oI22yqs_mrDA1sN2KGzfgGN0kht0=s64",
      "userId": "01871118759517508516"
     },
     "user_tz": -480
    },
    "id": "jKHZa5PNQpBr",
    "outputId": "b982925f-8df5-47e7-949c-3ba8bad7b0a9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_cecd23ee_1419_11eb_9395_0242ac1c0002row0_col0,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row0_col1,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row1_col0,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row1_col1,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row2_col0,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row2_col1,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row3_col0,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row3_col1,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row4_col0,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row4_col1,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row5_col0,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row5_col1,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row6_col0,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row6_col1,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row7_col0,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row7_col1,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row8_col0,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row8_col1,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row9_col0,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row9_col1,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row10_col0,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row10_col1,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row11_col0,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row11_col1,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row12_col0,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row12_col1,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row13_col0,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row13_col1,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row14_col0,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row14_col1,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row15_col0,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row15_col1,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row16_col0,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row16_col1,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row17_col0,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row17_col1,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row18_col0,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row18_col1,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row19_col0,#T_cecd23ee_1419_11eb_9395_0242ac1c0002row19_col1{\n",
       "            text-align:  center;\n",
       "        }</style><table id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Score ( /50)</th>        <th class=\"col_heading level0 col1\" >Weight (kg)</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row0_col0\" class=\"data row0 col0\" >3</td>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row0_col1\" class=\"data row0 col1\" >30</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row1_col0\" class=\"data row1 col0\" >5</td>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row1_col1\" class=\"data row1 col1\" >32</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row2_col0\" class=\"data row2 col0\" >8</td>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row2_col1\" class=\"data row2 col1\" >34</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row3_col0\" class=\"data row3 col0\" >10</td>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row3_col1\" class=\"data row3 col1\" >35</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row4_col0\" class=\"data row4 col0\" >13</td>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row4_col1\" class=\"data row4 col1\" >38</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row5_col0\" class=\"data row5 col0\" >15</td>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row5_col1\" class=\"data row5 col1\" >39</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row6_col0\" class=\"data row6 col0\" >18</td>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row6_col1\" class=\"data row6 col1\" >41</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row7_col0\" class=\"data row7 col0\" >20</td>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row7_col1\" class=\"data row7 col1\" >43</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row8_col0\" class=\"data row8 col0\" >28</td>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row8_col1\" class=\"data row8 col1\" >44</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row9_col0\" class=\"data row9 col0\" >32</td>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row9_col1\" class=\"data row9 col1\" >46</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row10_col0\" class=\"data row10 col0\" >35</td>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row10_col1\" class=\"data row10 col1\" >47</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row11_col0\" class=\"data row11 col0\" >39</td>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row11_col1\" class=\"data row11 col1\" >50</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row12_col0\" class=\"data row12 col0\" >40</td>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row12_col1\" class=\"data row12 col1\" >51</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row13_col0\" class=\"data row13 col0\" >40</td>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row13_col1\" class=\"data row13 col1\" >53</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row14_col0\" class=\"data row14 col0\" >42</td>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row14_col1\" class=\"data row14 col1\" >54</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row15_col0\" class=\"data row15 col0\" >43</td>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row15_col1\" class=\"data row15 col1\" >58</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row16_col0\" class=\"data row16 col0\" >45</td>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row16_col1\" class=\"data row16 col1\" >60</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row17_col0\" class=\"data row17 col0\" >46</td>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row17_col1\" class=\"data row17 col1\" >65</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row18_col0\" class=\"data row18 col0\" >47</td>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row18_col1\" class=\"data row18 col1\" >66</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row19_col0\" class=\"data row19 col0\" >48</td>\n",
       "                        <td id=\"T_cecd23ee_1419_11eb_9395_0242ac1c0002row19_col1\" class=\"data row19 col1\" >70</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f4d1e1a8898>"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "score = [3, 5, 8, 10, 13, 15, 18, 20, 28, 32, 35, 39, 40, 40, 42, 43, 45, 46, 47, 48]\n",
    "weight = [30, 32, 34, 35, 38, 39, 41, 43, 44, 46, 47, 50, 51, 53, 54, 58, 60, 65, 66, 70]\n",
    "\n",
    "table = pd.DataFrame(score, columns = ['Score ( /50)']); table['Weight (kg)'] = weight\n",
    "table\n",
    "table.style.set_properties(**{'text-align':'center'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 315
    },
    "executionInfo": {
     "elapsed": 993,
     "status": "ok",
     "timestamp": 1603338667373,
     "user": {
      "displayName": "Ummu Amirah",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjP1NlpYoc_oI22yqs_mrDA1sN2KGzfgGN0kht0=s64",
      "userId": "01871118759517508516"
     },
     "user_tz": -480
    },
    "id": "mW2Vfee-QpB1",
    "outputId": "0d6a6208-e501-41f7-c15a-aa4e5463d71b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = [3, 5, 8, 10, 13, 15, 18, 20, 28, 32, 35, 39, 40, 40, 42, 43, 45, 46, 47, 48]\n",
    "y = [30, 32, 34, 35, 38, 39, 41, 43, 44, 46, 47, 50, 51, 53, 54, 58, 60, 65, 66, 70]\n",
    "\n",
    "plt.scatter(x, y, c =\"red\", s = 100, marker = \"o\")\n",
    "plt.xlabel('Score in Data Science', fontsize = 12); plt.ylabel('Weight in kilograms', fontsize = 12)\n",
    "plt.xticks(size = 10); plt.yticks(size = 10)\n",
    "plt.title('Relationship between Score in Data Science and Weight in kg', fontsize = 12)\n",
    "plt.legend(fontsize = 12, loc = (1, 1))\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "executionInfo": {
     "elapsed": 1021,
     "status": "ok",
     "timestamp": 1603338670665,
     "user": {
      "displayName": "Ummu Amirah",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjP1NlpYoc_oI22yqs_mrDA1sN2KGzfgGN0kht0=s64",
      "userId": "01871118759517508516"
     },
     "user_tz": -480
    },
    "id": "7DWgCZBiQpB8",
    "outputId": "cae21cac-6304-4198-afd2-3794ed864190"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.95040736],\n",
       "       [0.95040736, 1.        ]])"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.corrcoef(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WWzcHON9QpCC"
   },
   "source": [
    "## 3.0 Question for <font color = \"orange\">Correlation</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0mGKEs-tQpCD"
   },
   "source": [
    "### <font color = \"maroon\">Question 1</font>\n",
    "\n",
    "Taller people tend to weigh more and shorter people tend to weigh less. By referring to table below, create a list of Height (as x) and a list of Weight (as y). Predict type of relation between these two lists of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 355
    },
    "executionInfo": {
     "elapsed": 905,
     "status": "ok",
     "timestamp": 1603338675330,
     "user": {
      "displayName": "Ummu Amirah",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjP1NlpYoc_oI22yqs_mrDA1sN2KGzfgGN0kht0=s64",
      "userId": "01871118759517508516"
     },
     "user_tz": -480
    },
    "id": "rCCGrc7CQpCE",
    "outputId": "c3576235-99c6-4b22-e8f1-775ac34a1927"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "#T_d58398c6_1419_11eb_9395_0242ac1c0002row0_col0,#T_d58398c6_1419_11eb_9395_0242ac1c0002row0_col1,#T_d58398c6_1419_11eb_9395_0242ac1c0002row1_col0,#T_d58398c6_1419_11eb_9395_0242ac1c0002row1_col1,#T_d58398c6_1419_11eb_9395_0242ac1c0002row2_col0,#T_d58398c6_1419_11eb_9395_0242ac1c0002row2_col1,#T_d58398c6_1419_11eb_9395_0242ac1c0002row3_col0,#T_d58398c6_1419_11eb_9395_0242ac1c0002row3_col1,#T_d58398c6_1419_11eb_9395_0242ac1c0002row4_col0,#T_d58398c6_1419_11eb_9395_0242ac1c0002row4_col1,#T_d58398c6_1419_11eb_9395_0242ac1c0002row5_col0,#T_d58398c6_1419_11eb_9395_0242ac1c0002row5_col1,#T_d58398c6_1419_11eb_9395_0242ac1c0002row6_col0,#T_d58398c6_1419_11eb_9395_0242ac1c0002row6_col1,#T_d58398c6_1419_11eb_9395_0242ac1c0002row7_col0,#T_d58398c6_1419_11eb_9395_0242ac1c0002row7_col1,#T_d58398c6_1419_11eb_9395_0242ac1c0002row8_col0,#T_d58398c6_1419_11eb_9395_0242ac1c0002row8_col1,#T_d58398c6_1419_11eb_9395_0242ac1c0002row9_col0,#T_d58398c6_1419_11eb_9395_0242ac1c0002row9_col1,#T_d58398c6_1419_11eb_9395_0242ac1c0002row10_col0,#T_d58398c6_1419_11eb_9395_0242ac1c0002row10_col1,#T_d58398c6_1419_11eb_9395_0242ac1c0002row11_col0,#T_d58398c6_1419_11eb_9395_0242ac1c0002row11_col1,#T_d58398c6_1419_11eb_9395_0242ac1c0002row12_col0,#T_d58398c6_1419_11eb_9395_0242ac1c0002row12_col1,#T_d58398c6_1419_11eb_9395_0242ac1c0002row13_col0,#T_d58398c6_1419_11eb_9395_0242ac1c0002row13_col1,#T_d58398c6_1419_11eb_9395_0242ac1c0002row14_col0,#T_d58398c6_1419_11eb_9395_0242ac1c0002row14_col1{\n",
       "            text-align:  center;\n",
       "        }</style><table id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Height (cm)</th>        <th class=\"col_heading level0 col1\" >Weight (kg)</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row0_col0\" class=\"data row0 col0\" >173.000000</td>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row0_col1\" class=\"data row0 col1\" >50</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row1_col0\" class=\"data row1 col0\" >173.000000</td>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row1_col1\" class=\"data row1 col1\" >73</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row2_col0\" class=\"data row2 col0\" >162.000000</td>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row2_col1\" class=\"data row2 col1\" >53</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row3_col0\" class=\"data row3 col0\" >163.000000</td>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row3_col1\" class=\"data row3 col1\" >85</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row4_col0\" class=\"data row4 col0\" >152.000000</td>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row4_col1\" class=\"data row4 col1\" >74</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row5_col0\" class=\"data row5 col0\" >172.000000</td>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row5_col1\" class=\"data row5 col1\" >62</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row6_col0\" class=\"data row6 col0\" >170.000000</td>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row6_col1\" class=\"data row6 col1\" >75</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row7_col0\" class=\"data row7 col0\" >171.000000</td>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row7_col1\" class=\"data row7 col1\" >68</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row8_col0\" class=\"data row8 col0\" >163.900000</td>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row8_col1\" class=\"data row8 col1\" >65</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row9_col0\" class=\"data row9 col0\" >151.000000</td>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row9_col1\" class=\"data row9 col1\" >78</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row10_col0\" class=\"data row10 col0\" >149.900000</td>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row10_col1\" class=\"data row10 col1\" >50</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row11_col0\" class=\"data row11 col0\" >149.900000</td>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row11_col1\" class=\"data row11 col1\" >62</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row12_col0\" class=\"data row12 col0\" >164.000000</td>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row12_col1\" class=\"data row12 col1\" >57</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row13_col0\" class=\"data row13 col0\" >180.000000</td>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row13_col1\" class=\"data row13 col1\" >85</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row14_col0\" class=\"data row14 col0\" >167.000000</td>\n",
       "                        <td id=\"T_d58398c6_1419_11eb_9395_0242ac1c0002row14_col1\" class=\"data row14 col1\" >51</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f4d1da01940>"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "height = [173.0, 173.0, 162.0, 163.0, 152.0, 172.0, 170.0, 171.0, 163.9, 151.0, 149.9, 149.9, 164.0, 180.0, 167.0]\n",
    "weight = [50, 73, 53, 85, 74, 62, 75, 68, 65, 78, 50, 62, 57, 85, 51]\n",
    "\n",
    "table = pd.DataFrame(height, columns = ['Height (cm)']); table['Weight (kg)'] = weight\n",
    "table\n",
    "table.style.set_properties(**{'text-align' : 'center'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y5UKcRXAQpCL"
   },
   "source": [
    "### <font color = \"maroon\">Question 2</font>\n",
    "\n",
    "By referring to table in Question 1, import **matplotlib.pyplot** to draw a scatter plot graph of Height (cm) versus Weight (kg). Label of both axes, ticks, title and legend should be in your graph.\n",
    "\n",
    "From your plotted graph, write a conclusion about these two variables and the type of relation exists between them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "executionInfo": {
     "elapsed": 1132,
     "status": "ok",
     "timestamp": 1603338678584,
     "user": {
      "displayName": "Ummu Amirah",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjP1NlpYoc_oI22yqs_mrDA1sN2KGzfgGN0kht0=s64",
      "userId": "01871118759517508516"
     },
     "user_tz": -480
    },
    "id": "1qrkoCr6QpCN",
    "outputId": "15f413b9-770c-4771-df63-c7db014549f0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "height = [173.0, 173.0, 162.0, 163.0, 152.0, 172.0, 170.0, 171.0, 163.9, 151.0, 149.9, 149.9, 164.0, 180.0, 167.0]\n",
    "weight = [50, 73, 53, 85, 74, 62, 75, 68, 65, 78, 50, 62, 57, 85, 51]\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter( height, weight, c = \"maroon\", s = 100, marker = \"o\")\n",
    "plt.xlabel(\"Height (cm)\", fontsize = 12); plt.ylabel(\"Weight (kg)\", fontsize = 12)\n",
    "plt.xticks(size = 10); plt.yticks(size = 10)\n",
    "plt.title(\"Height (cm) versus Weight (kg)\", fontsize = 15)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fv4p3eK6QpCU"
   },
   "source": [
    "### <font color = \"maroon\">Question 3</font>\n",
    "\n",
    "By referring to table in Question 1, calculate the **correlation coefficient, r** step by step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "executionInfo": {
     "elapsed": 943,
     "status": "ok",
     "timestamp": 1603338711378,
     "user": {
      "displayName": "Ummu Amirah",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjP1NlpYoc_oI22yqs_mrDA1sN2KGzfgGN0kht0=s64",
      "userId": "01871118759517508516"
     },
     "user_tz": -480
    },
    "id": "B1BZ-lOUQpCW",
    "outputId": "327246fc-d749-409f-f713-1ab7810c0e41"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. Mean of Height (cm) is  164.11333333333334\n",
      "2. Mean of Weight (kg) is  65.86666666666666\n",
      "3. Summation of (a x b) is  251.32666666666648\n",
      "4. Summation of a-squared is  1291.437333333333\n",
      "5. Summation od b-squared is  2083.733333333333\n",
      "6. The correlation coefficient, r is  0.15320778477451918\n"
     ]
    }
   ],
   "source": [
    "height = [173.0, 173.0, 162.0, 163.0, 152.0, 172.0, 170.0, 171.0, 163.9, 151.0, 149.9, 149.9, 164.0, 180.0, 167.0]\n",
    "weight = [50, 73, 53, 85, 74, 62, 75, 68, 65, 78, 50, 62, 57, 85, 51]\n",
    "\n",
    "#Step 1: Find the mean of height and mean of weight\n",
    "meanHeight = sum(height) / len(height)\n",
    "meanWeight = sum(weight) / len(weight)\n",
    "\n",
    "print (\"1. Mean of Height (cm) is \", meanHeight)\n",
    "print (\"2. Mean of Weight (kg) is \", meanWeight)\n",
    "\n",
    "#Step 2: Subtract the mean of height from every height value (assign as a) and mean of weight from every weight value (assign as b)\n",
    "a = []\n",
    "for i in height:\n",
    "    a.append(i - meanHeight)\n",
    "\n",
    "b = []\n",
    "for j in weight:\n",
    "    b.append(j - meanWeight)\n",
    "\n",
    "#Step 3: Calculate (a x b), summation of a-squared and summation of b-squared\n",
    "ab = []\n",
    "for k in range(len(a)):\n",
    "    ab.append(a[k] * b[k])\n",
    "\n",
    "aSquared = []\n",
    "for l in height:\n",
    "    aSquared.append((l - meanHeight) ** 2)\n",
    "\n",
    "bSquared = []\n",
    "for m in weight:\n",
    "    bSquared.append((m - meanWeight) ** 2)\n",
    "\n",
    "#Step 4: Sum up ab, sum up aSquared and bSquared\n",
    "\n",
    "print (\"3. Summation of (a x b) is \", sum(ab))\n",
    "print (\"4. Summation of a-squared is \", sum(aSquared))\n",
    "print (\"5. Summation od b-squared is \", sum(bSquared))\n",
    "\n",
    "#Step 5: Find the correlation coefficient, r\n",
    "AB = ((sum(aSquared)) * (sum(bSquared)))\n",
    "sqrtAB = AB ** (0.5)\n",
    "\n",
    "r = sum(ab) / sqrtAB\n",
    "print (\"6. The correlation coefficient, r is \", r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7SadmKFJQpCe"
   },
   "source": [
    "### <font color = \"maroon\">Question 4</font>\n",
    "\n",
    "By referring to table in Question 1, import **numpy** and use it to calculate **the correlation coefficient, r** and compare with your previous **r**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "executionInfo": {
     "elapsed": 940,
     "status": "ok",
     "timestamp": 1603338714828,
     "user": {
      "displayName": "Ummu Amirah",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjP1NlpYoc_oI22yqs_mrDA1sN2KGzfgGN0kht0=s64",
      "userId": "01871118759517508516"
     },
     "user_tz": -480
    },
    "id": "8LRawYW5QpCf",
    "outputId": "7a643578-0c65-4e1b-cd02-13d6ba99406d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.15320778],\n",
       "       [0.15320778, 1.        ]])"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.corrcoef(height, weight)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iEINgYHWQpC_"
   },
   "source": [
    "### <font color = \"orange\">Question 5</font>\n",
    "\n",
    "Table below shows a list of 50 students with the days they present to class in one semester and the GPA they got for that semester. Studies show, a student who has many absence has a decrease in GPA. By referring to table below, create list for Days Present (as x) and GPA (as y). Predict type of relation between these two lists of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 945,
     "status": "ok",
     "timestamp": 1603338718323,
     "user": {
      "displayName": "Ummu Amirah",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjP1NlpYoc_oI22yqs_mrDA1sN2KGzfgGN0kht0=s64",
      "userId": "01871118759517508516"
     },
     "user_tz": -480
    },
    "id": "oo3G96rnQpDD",
    "outputId": "1e36c51d-09c6-4426-a71f-1b85ed0a7138"
   },
   "outputs": [
    {
     "data": {
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       "            text-align:  center;\n",
       "        }</style><table id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Days Present</th>        <th class=\"col_heading level0 col1\" >GPA</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row0_col0\" class=\"data row0 col0\" >180</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row0_col1\" class=\"data row0 col1\" >4.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row1_col0\" class=\"data row1 col0\" >150</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row1_col1\" class=\"data row1 col1\" >2.500000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row2_col0\" class=\"data row2 col0\" >170</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row2_col1\" class=\"data row2 col1\" >4.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row3_col0\" class=\"data row3 col0\" >180</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row3_col1\" class=\"data row3 col1\" >3.900000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row4_col0\" class=\"data row4 col0\" >177</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row4_col1\" class=\"data row4 col1\" >3.750000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row5_col0\" class=\"data row5 col0\" >180</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row5_col1\" class=\"data row5 col1\" >3.800000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row6_col0\" class=\"data row6 col0\" >140</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row6_col1\" class=\"data row6 col1\" >2.900000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row7_col0\" class=\"data row7 col0\" >169</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row7_col1\" class=\"data row7 col1\" >3.100000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row8_col0\" class=\"data row8 col0\" >168</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row8_col1\" class=\"data row8 col1\" >3.250000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row9_col0\" class=\"data row9 col0\" >152</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row9_col1\" class=\"data row9 col1\" >3.400000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row10_col0\" class=\"data row10 col0\" >150</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row10_col1\" class=\"data row10 col1\" >3.300000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row11_col0\" class=\"data row11 col0\" >170</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row11_col1\" class=\"data row11 col1\" >3.900000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row12_col0\" class=\"data row12 col0\" >109</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row12_col1\" class=\"data row12 col1\" >1.350000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row13_col0\" class=\"data row13 col0\" >180</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row13_col1\" class=\"data row13 col1\" >4.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row14_col0\" class=\"data row14 col0\" >108</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row14_col1\" class=\"data row14 col1\" >1.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row15_col0\" class=\"data row15 col0\" >175</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row15_col1\" class=\"data row15 col1\" >3.850000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row16_col0\" class=\"data row16 col0\" >144</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row16_col1\" class=\"data row16 col1\" >2.980000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row17_col0\" class=\"data row17 col0\" >120</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row17_col1\" class=\"data row17 col1\" >2.750000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row18_col0\" class=\"data row18 col0\" >133</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row18_col1\" class=\"data row18 col1\" >2.750000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row19_col0\" class=\"data row19 col0\" >160</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row19_col1\" class=\"data row19 col1\" >3.600000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row20_col0\" class=\"data row20 col0\" >160</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row20_col1\" class=\"data row20 col1\" >3.500000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row21_col0\" class=\"data row21 col0\" >159</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row21_col1\" class=\"data row21 col1\" >3.500000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row22_col0\" class=\"data row22 col0\" >165</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row22_col1\" class=\"data row22 col1\" >3.500000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row23_col0\" class=\"data row23 col0\" >180</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row23_col1\" class=\"data row23 col1\" >3.850000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row24_col0\" class=\"data row24 col0\" >149</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row24_col1\" class=\"data row24 col1\" >2.950000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row25_col0\" class=\"data row25 col0\" >180</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row25_col1\" class=\"data row25 col1\" >3.950000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row26_col0\" class=\"data row26 col0\" >160</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row26_col1\" class=\"data row26 col1\" >3.650000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row27_col0\" class=\"data row27 col0\" >155</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row27_col1\" class=\"data row27 col1\" >3.550000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row28_col0\" class=\"data row28 col0\" >156</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row28_col1\" class=\"data row28 col1\" >3.580000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row29_col0\" class=\"data row29 col0\" >145</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row29_col1\" class=\"data row29 col1\" >2.980000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row30_col0\" class=\"data row30 col0\" >122</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row30_col1\" class=\"data row30 col1\" >1.500000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row31_col0\" class=\"data row31 col0\" >131</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row31_col1\" class=\"data row31 col1\" >1.750000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row32_col0\" class=\"data row32 col0\" >156</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row32_col1\" class=\"data row32 col1\" >2.200000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row33_col0\" class=\"data row33 col0\" >166</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row33_col1\" class=\"data row33 col1\" >3.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row34_col0\" class=\"data row34 col0\" >170</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row34_col1\" class=\"data row34 col1\" >3.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row35_col0\" class=\"data row35 col0\" >155</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row35_col1\" class=\"data row35 col1\" >3.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row36_col0\" class=\"data row36 col0\" >158</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row36_col1\" class=\"data row36 col1\" >3.150000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row37_col0\" class=\"data row37 col0\" >170</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row37_col1\" class=\"data row37 col1\" >3.900000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row38_col0\" class=\"data row38 col0\" >160</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row38_col1\" class=\"data row38 col1\" >3.150000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row39_col0\" class=\"data row39 col0\" >165</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row39_col1\" class=\"data row39 col1\" >3.850000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row40_col0\" class=\"data row40 col0\" >159</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row40_col1\" class=\"data row40 col1\" >2.700000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row41_col0\" class=\"data row41 col0\" >119</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row41_col1\" class=\"data row41 col1\" >1.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row42_col0\" class=\"data row42 col0\" >168</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row42_col1\" class=\"data row42 col1\" >3.250000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row43_col0\" class=\"data row43 col0\" >175</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row43_col1\" class=\"data row43 col1\" >3.900000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row44_col0\" class=\"data row44 col0\" >161</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row44_col1\" class=\"data row44 col1\" >2.800000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row45_col0\" class=\"data row45 col0\" >160</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row45_col1\" class=\"data row45 col1\" >3.500000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row46_col0\" class=\"data row46 col0\" >160</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row46_col1\" class=\"data row46 col1\" >3.400000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row47_col0\" class=\"data row47 col0\" >150</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row47_col1\" class=\"data row47 col1\" >2.300000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row48_col0\" class=\"data row48 col0\" >140</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row48_col1\" class=\"data row48 col1\" >2.500000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row49_col0\" class=\"data row49 col0\" >148</td>\n",
       "                        <td id=\"T_ef1f4fd2_1419_11eb_9395_0242ac1c0002row49_col1\" class=\"data row49 col1\" >2.350000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f4d1d8e8358>"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "days = [180, 150, 170, 180, 177, 180, 140, 169, 168, 152, 150, 170, 109, 180, 108, 175, 144, 120, 133, 160, 160, 159, 165, 180, 149, 180, 160, 155, 156, 145, 122, 131, 156, 166, 170, 155, 158, 170, 160, 165, 159, 119, 168, 175, 161, 160, 160, 150, 140, 148]\n",
    "gpa = [4.00, 2.50, 4.00, 3.90, 3.75, 3.80, 2.90, 3.10, 3.25, 3.40, 3.30, 3.90, 1.35, 4.00, 1.00, 3.85, 2.98, 2.75, 2.75, 3.60, 3.50, 3.50, 3.50, 3.85, 2.95, 3.95, 3.65, 3.55, 3.58, 2.98, 1.50, 1.75, 2.20, 3.00, 3.00, 3.00, 3.15, 3.90, 3.15, 3.85, 2.70, 1.00, 3.25, 3.90, 2.80, 3.50, 3.40, 2.30, 2.50, 2.35]\n",
    "\n",
    "table = pd.DataFrame(days, columns = ['Days Present']); table['GPA'] = gpa\n",
    "table\n",
    "table.style.set_properties(**{'text-align' : 'center'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "executionInfo": {
     "elapsed": 1021,
     "status": "ok",
     "timestamp": 1603338723389,
     "user": {
      "displayName": "Ummu Amirah",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjP1NlpYoc_oI22yqs_mrDA1sN2KGzfgGN0kht0=s64",
      "userId": "01871118759517508516"
     },
     "user_tz": -480
    },
    "id": "KSif6nQYQpDT",
    "outputId": "d1203e13-cf1f-4809-b3bc-b7e9ba0088c2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [180, 150, 170, 180, 177, 180, 140, 169, 168, 152, 150, 170, 109, 180, 108, 175, 144, 120, 133, 160, 160, 159, 165, 180, 149, 180, 160, 155, 156, 145, 122, 131, 156, 166, 170, 155, 158, 170, 160, 165, 159, 119, 168, 175, 161, 160, 160, 150, 140, 148]\n",
    "y = [4.00, 2.50, 4.00, 3.90, 3.75, 3.80, 2.90, 3.10, 3.25, 3.40, 3.30, 3.90, 1.35, 4.00, 1.00, 3.85, 2.98, 2.75, 2.75, 3.60, 3.50, 3.50, 3.50, 3.85, 2.95, 3.95, 3.65, 3.55, 3.58, 2.98, 1.50, 1.75, 2.20, 3.00, 3.00, 3.00, 3.15, 3.90, 3.15, 3.85, 2.70, 1.00, 3.25, 3.90, 2.80, 3.50, 3.40, 2.30, 2.50, 2.35]\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(x, y, c = \"darkorange\", s = 100, marker = \"o\")\n",
    "plt.xlabel(\"Days Present\", fontsize = 12); plt.ylabel(\"GPA\", fontsize = 12)\n",
    "plt.xticks(size = 10); plt.yticks(size = 10)\n",
    "plt.title(\"Days Present versus GPA\", fontsize = 15)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ctBOa5_QQpDi"
   },
   "source": [
    "**Conclusion:** The graph shown, as the number of days present in class increase, the GPA also increases.\n",
    "\n",
    "**Type of Relation:** Positive Linear Correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DPS20LesQpDj"
   },
   "source": [
    "### <font color = \"orange\">Question 7</font>\n",
    "\n",
    "By referring to table in Question 5, calculate the **correlation coefficient, r** step by step. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "executionInfo": {
     "elapsed": 1073,
     "status": "ok",
     "timestamp": 1603338756131,
     "user": {
      "displayName": "Ummu Amirah",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjP1NlpYoc_oI22yqs_mrDA1sN2KGzfgGN0kht0=s64",
      "userId": "01871118759517508516"
     },
     "user_tz": -480
    },
    "id": "jB_9kKjiQpDl",
    "outputId": "6250a107-fa5f-4fcc-8c34-1f9ea5d912be"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. Mean of Height (cm) is  156.34\n",
      "2. Mean of Weight (kg) is  3.105800000000001\n",
      "3. Summation of (a x b) is  612.3613999999999\n",
      "4. Summation of a-squared is  16763.219999999994\n",
      "5. Summation od b-squared is  29.820018000000008\n",
      "6. The correlation coefficient, r is  0.8661141508214935\n"
     ]
    }
   ],
   "source": [
    "x = [180, 150, 170, 180, 177, 180, 140, 169, 168, 152, 150, 170, 109, 180, 108, 175, 144, 120, 133, 160, 160, 159, 165, 180, 149, 180, 160, 155, 156, 145, 122, 131, 156, 166, 170, 155, 158, 170, 160, 165, 159, 119, 168, 175, 161, 160, 160, 150, 140, 148]\n",
    "y = [4.00, 2.50, 4.00, 3.90, 3.75, 3.80, 2.90, 3.10, 3.25, 3.40, 3.30, 3.90, 1.35, 4.00, 1.00, 3.85, 2.98, 2.75, 2.75, 3.60, 3.50, 3.50, 3.50, 3.85, 2.95, 3.95, 3.65, 3.55, 3.58, 2.98, 1.50, 1.75, 2.20, 3.00, 3.00, 3.00, 3.15, 3.90, 3.15, 3.85, 2.70, 1.00, 3.25, 3.90, 2.80, 3.50, 3.40, 2.30, 2.50, 2.35]\n",
    "\n",
    "#Step 1: Find the mean of height and mean of weight\n",
    "meanX = sum(x) / len(x)\n",
    "meanY = sum(y) / len(y)\n",
    "\n",
    "print (\"1. Mean of Height (cm) is \", meanX)\n",
    "print (\"2. Mean of Weight (kg) is \", meanY)\n",
    "\n",
    "#Step 2: Subtract the mean of x from every x value (assign as a) and mean of y from every y value (assign as b)\n",
    "a = []\n",
    "for i in x:\n",
    "    a.append(i - meanX)\n",
    "\n",
    "b = []\n",
    "for j in y:\n",
    "    b.append(j - meanY)\n",
    "\n",
    "#Step 3: Calculate (a x b), summation of a-squared and summation of b-squared\n",
    "ab = []\n",
    "for k in range(len(a)):\n",
    "    ab.append(a[k] * b[k])\n",
    "\n",
    "aSquared = []\n",
    "for l in x:\n",
    "    aSquared.append((l - meanX) ** 2)\n",
    "\n",
    "bSquared = []\n",
    "for m in y:\n",
    "    bSquared.append((m - meanY) ** 2)\n",
    "\n",
    "#Step 4: Sum up ab, sum up aSquared and bSquared\n",
    "\n",
    "print (\"3. Summation of (a x b) is \", sum(ab))\n",
    "print (\"4. Summation of a-squared is \", sum(aSquared))\n",
    "print (\"5. Summation od b-squared is \", sum(bSquared))\n",
    "\n",
    "#Step 5: Find the correlation coefficient, r\n",
    "AB = ((sum(aSquared)) * (sum(bSquared)))\n",
    "sqrtAB = AB ** (0.5)\n",
    "\n",
    "r = sum(ab) / sqrtAB\n",
    "print (\"6. The correlation coefficient, r is \", r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NLeJvKV5QpDr"
   },
   "source": [
    "### <font color = \"orange\">Question 8</font>\n",
    "\n",
    "By referring to table in Question 5, import **numpy** and use it to calculate **the correlation coefficient, r** and compare with your previous **r**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "executionInfo": {
     "elapsed": 965,
     "status": "ok",
     "timestamp": 1603338766227,
     "user": {
      "displayName": "Ummu Amirah",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjP1NlpYoc_oI22yqs_mrDA1sN2KGzfgGN0kht0=s64",
      "userId": "01871118759517508516"
     },
     "user_tz": -480
    },
    "id": "B5S_5OhXQpDs",
    "outputId": "b82565e1-d525-4015-8059-68aac03b3e5f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient is \n",
      "[[1.         0.86611415]\n",
      " [0.86611415 1.        ]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "print( \"The correlation coefficient is \")\n",
    "print (np.corrcoef(x, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XRIiNfIbQpDy"
   },
   "source": [
    "### <font color = \"darkorchid\">Question 9</font>\n",
    "\n",
    "A study was conducted to investigate the relationship between Height and Shoe Size. Create the lists for Height (as x) and Shoe Size (as y)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "kK1NwP6uQpDz"
   },
   "outputs": [],
   "source": [
    "height = [173.0, 173.0, 162.0, 163.0, 172.0, 170.0, 171.0, 167.0, 162.0, 151.0, 149.9, 149.9, 170.0, 175.0, 167.0]\n",
    "shoesize = [6.0, 9.0, 7.0, 8.0, 11.0, 10.0, 8.0, 7.0, 7.0, 6.0, 4.0, 4.0, 10.0, 10.5, 6]\n",
    "\n",
    "table = pd.DataFrame(height, columns = ['Height (cm)']); table['Shoe Size'] = shoesize\n",
    "table\n",
    "table.style.set_properties(**{'text-align' : 'center'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "executionInfo": {
     "elapsed": 965,
     "status": "ok",
     "timestamp": 1603338774685,
     "user": {
      "displayName": "Ummu Amirah",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjP1NlpYoc_oI22yqs_mrDA1sN2KGzfgGN0kht0=s64",
      "userId": "01871118759517508516"
     },
     "user_tz": -480
    },
    "id": "Kqq5COanQpEB",
    "outputId": "bd05558f-f2b1-4efb-e9c5-06bffc92640e"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "height = [173.0, 173.0, 162.0, 163.0, 172.0, 170.0, 171.0, 167.0, 162.0, 151.0, 149.9, 149.9, 170.0, 175.0, 167.0]\n",
    "shoesize = [6.0, 9.0, 7.0, 8.0, 11.0, 10.0, 8.0, 7.0, 7.0, 6.0, 4.0, 4.0, 10.0, 10.5, 6]\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(height, shoesize, c = \"darkorchid\", s = 100, marker = \"o\")\n",
    "plt.xlabel(\"Height (cm)\", fontsize = 12); plt.ylabel(\"Shoe Size\", fontsize = 12)\n",
    "plt.xticks(size = 10); plt.yticks(size = 10)\n",
    "plt.title(\"Height (cm) versus Shoe Size\", fontsize = 15)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jobJx2eaQpEM"
   },
   "source": [
    "### <font color = \"darkorchid\">Question 10</font>\n",
    "\n",
    "By referring to table in Question 9, import **numpy** and use it to calculate **the correlation coefficient, r** and write down a short conclusion with your r value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "executionInfo": {
     "elapsed": 916,
     "status": "ok",
     "timestamp": 1603338782558,
     "user": {
      "displayName": "Ummu Amirah",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjP1NlpYoc_oI22yqs_mrDA1sN2KGzfgGN0kht0=s64",
      "userId": "01871118759517508516"
     },
     "user_tz": -480
    },
    "id": "QBiL_QOaQpEN",
    "outputId": "d00bb237-e8ad-477c-b50a-87219f9a816d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.        0.7746235]\n",
      " [0.7746235 1.       ]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "print ((np.corrcoef(height, shoesize)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wYTQbdzBQpEW"
   },
   "source": [
    "**Conclusion:** The correlation coefficient, r = 0.78 shown that the relation between height and shoe size is low positive correlation as well as the graph plotted."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "colab": {
   "name": "3.1-Correlation.ipynb",
   "provenance": []
  },
  "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": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}