diff --git a/.ipynb_checkpoints/UFCFVQ-15-M Programming Task 2 Template (1)-checkpoint.ipynb b/.ipynb_checkpoints/UFCFVQ-15-M Programming Task 2 Template (1)-checkpoint.ipynb
index eafb96515951db336335199896294208858c8ade..c6c9dc9b89687c0cc3b8afff040fbe87adf9dae3 100644
--- a/.ipynb_checkpoints/UFCFVQ-15-M Programming Task 2 Template (1)-checkpoint.ipynb	
+++ b/.ipynb_checkpoints/UFCFVQ-15-M Programming Task 2 Template (1)-checkpoint.ipynb	
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -222,7 +222,7 @@
        "[26746 rows x 9 columns]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -246,7 +246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -352,7 +352,7 @@
        "[26074 rows x 2 columns]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -375,7 +375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -603,7 +603,7 @@
        "[26721 rows x 10 columns]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -627,7 +627,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -855,7 +855,7 @@
        "[25332 rows x 10 columns]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -878,7 +878,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -1106,7 +1106,7 @@
        "[25259 rows x 10 columns]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1129,7 +1129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -1357,7 +1357,7 @@
        "[25259 rows x 10 columns]"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1379,7 +1379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -1558,7 +1558,7 @@
        "[25259 rows x 7 columns]"
       ]
      },
-     "execution_count": 143,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1601,17 +1601,6 @@
    "execution_count": 9,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "          final_mark  click_events\n",
-      "age_band                          \n",
-      "0-35       72.503923   1616.472655\n",
-      "35-55      75.035810   2193.000267\n",
-      "55<=       77.718919   3574.864865\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -1693,7 +1682,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -1714,6 +1703,7 @@
     "#each set of data. It shows the average of both final marks and click events for each age group for easy visualisation.\n",
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",
+    "df9.reset_index(inplace=True)\n",
     "f, axs = plt.subplots(1,2,figsize=(10,5),sharex=True)\n",
     "\n",
     "g1 =sns.barplot(y=\"final_mark\", x = \"age_band\",data=df9, ax=axs[0])\n",
@@ -1723,7 +1713,8 @@
     "g1.set_title(\"The effects of age groups on marks and engagement\")\n",
     "plt.show()    \n",
     "    \n",
-    "#(How to Combine Two Seaborn plots with Shared y-axis? - Data Viz with Python and R, 2021)"
+    "#(How to Combine Two Seaborn plots with Shared y-axis? - Data Viz with Python and R, 2021)\n",
+    "#(Plot with seaborn after groupby command in pandas | Data Science and Machine Learning, 2021)"
    ]
   },
   {
@@ -1735,14 +1726,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 12,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1756,7 +1747,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The correlation coefficient is: 0.27423573401899426 ,there is a weak positive linear relationship between the two variables.\n"
+      "The correlation coefficient is: 0.2742357340189947 ,there is a weak positive linear relationship between the two variables.\n"
      ]
     }
    ],
@@ -1787,7 +1778,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 16,
    "metadata": {
     "scrolled": true
    },
@@ -1796,8 +1787,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "LinregressResult(slope=37.095925643680886, intercept=-917.2096355710828, rvalue=0.2742357340189943, pvalue=0.0, stderr=0.818528803329568)\n",
-      "R-Squared: 0.0752052378129366\n"
+      "LinregressResult(slope=37.095925643681, intercept=-917.209635571091, rvalue=0.2742357340189947, pvalue=0.0, stderr=0.8185288033295693)\n",
+      "(0.2742357340189971, 0.0)\n",
+      "R-Squared: 0.07520523781293681\n"
      ]
     }
    ],
@@ -1811,10 +1803,15 @@
     "from scipy import stats\n",
     "\n",
     "res = stats.linregress(df7[\"final_mark\"],df7[\"click_events\"])\n",
+    "pearsonr = stats.pearsonr(df7[\"final_mark\"],df7[\"click_events\"])\n",
     "print(res)\n",
+    "print(pearsonr)\n",
     "print(\"R-Squared:\", res.rvalue**2)\n",
     "\n",
-    "#(scipy.stats.linregress, 2021)"
+    "\n",
+    "\n",
+    "#(scipy.stats.linregress, 2021)\n",
+    "#(scipy.stats.pearsonr, 2021)"
    ]
   },
   {
@@ -1849,8 +1846,12 @@
     "\n",
     "docs.scipy.org. 2021. scipy.stats.linregress. [online] Available at:<https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html> [Accessed 19 April 2021].\n",
     "\n",
+    "Docs.scipy.org. 2021. scipy.stats.pearsonr. [online] Available at: <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.pearsonr.html> [Accessed 21 April 2021].\n",
+    "\n",
     "Hayden, A., 2021. Pandas sum by groupby, but exclude certain columns. [online] Stack Overflow. Available at: <https://stackoverflow.com/questions/32751229/pandas-sum-by-groupby-but-exclude-certain-columns> [Accessed 16 April 2021].\n",
     "\n",
+    "Kaggle.com. 2021. Plot with seaborn after groupby command in pandas | Data Science and Machine Learning. [online] Available at: <https://www.kaggle.com/questions-and-answers/55356> [Accessed 21 April 2021].\n",
+    "\n",
     "Pandas.pydata.org. 2021. pandas.DataFrame.rename — pandas 1.2.4 documentation. [online] Available at: <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rename.html> [Accessed 15 April 2021].\n",
     "\n",
     "Seaborn.pydata.org. 2021. Visualizing regression models — seaborn 0.11.1 documentation. [online] Available at: <https://seaborn.pydata.org/tutorial/regression.html> [Accessed 19 April 2021].\n"
diff --git a/UFCFVQ-15-M Programming Task 2 Template (1).ipynb b/UFCFVQ-15-M Programming Task 2 Template (1).ipynb
index eafb96515951db336335199896294208858c8ade..c6c9dc9b89687c0cc3b8afff040fbe87adf9dae3 100644
--- a/UFCFVQ-15-M Programming Task 2 Template (1).ipynb	
+++ b/UFCFVQ-15-M Programming Task 2 Template (1).ipynb	
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -222,7 +222,7 @@
        "[26746 rows x 9 columns]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -246,7 +246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -352,7 +352,7 @@
        "[26074 rows x 2 columns]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -375,7 +375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -603,7 +603,7 @@
        "[26721 rows x 10 columns]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -627,7 +627,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -855,7 +855,7 @@
        "[25332 rows x 10 columns]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -878,7 +878,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -1106,7 +1106,7 @@
        "[25259 rows x 10 columns]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1129,7 +1129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -1357,7 +1357,7 @@
        "[25259 rows x 10 columns]"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1379,7 +1379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -1558,7 +1558,7 @@
        "[25259 rows x 7 columns]"
       ]
      },
-     "execution_count": 143,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1601,17 +1601,6 @@
    "execution_count": 9,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "          final_mark  click_events\n",
-      "age_band                          \n",
-      "0-35       72.503923   1616.472655\n",
-      "35-55      75.035810   2193.000267\n",
-      "55<=       77.718919   3574.864865\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -1693,7 +1682,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -1714,6 +1703,7 @@
     "#each set of data. It shows the average of both final marks and click events for each age group for easy visualisation.\n",
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",
+    "df9.reset_index(inplace=True)\n",
     "f, axs = plt.subplots(1,2,figsize=(10,5),sharex=True)\n",
     "\n",
     "g1 =sns.barplot(y=\"final_mark\", x = \"age_band\",data=df9, ax=axs[0])\n",
@@ -1723,7 +1713,8 @@
     "g1.set_title(\"The effects of age groups on marks and engagement\")\n",
     "plt.show()    \n",
     "    \n",
-    "#(How to Combine Two Seaborn plots with Shared y-axis? - Data Viz with Python and R, 2021)"
+    "#(How to Combine Two Seaborn plots with Shared y-axis? - Data Viz with Python and R, 2021)\n",
+    "#(Plot with seaborn after groupby command in pandas | Data Science and Machine Learning, 2021)"
    ]
   },
   {
@@ -1735,14 +1726,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 12,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1756,7 +1747,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The correlation coefficient is: 0.27423573401899426 ,there is a weak positive linear relationship between the two variables.\n"
+      "The correlation coefficient is: 0.2742357340189947 ,there is a weak positive linear relationship between the two variables.\n"
      ]
     }
    ],
@@ -1787,7 +1778,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 16,
    "metadata": {
     "scrolled": true
    },
@@ -1796,8 +1787,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "LinregressResult(slope=37.095925643680886, intercept=-917.2096355710828, rvalue=0.2742357340189943, pvalue=0.0, stderr=0.818528803329568)\n",
-      "R-Squared: 0.0752052378129366\n"
+      "LinregressResult(slope=37.095925643681, intercept=-917.209635571091, rvalue=0.2742357340189947, pvalue=0.0, stderr=0.8185288033295693)\n",
+      "(0.2742357340189971, 0.0)\n",
+      "R-Squared: 0.07520523781293681\n"
      ]
     }
    ],
@@ -1811,10 +1803,15 @@
     "from scipy import stats\n",
     "\n",
     "res = stats.linregress(df7[\"final_mark\"],df7[\"click_events\"])\n",
+    "pearsonr = stats.pearsonr(df7[\"final_mark\"],df7[\"click_events\"])\n",
     "print(res)\n",
+    "print(pearsonr)\n",
     "print(\"R-Squared:\", res.rvalue**2)\n",
     "\n",
-    "#(scipy.stats.linregress, 2021)"
+    "\n",
+    "\n",
+    "#(scipy.stats.linregress, 2021)\n",
+    "#(scipy.stats.pearsonr, 2021)"
    ]
   },
   {
@@ -1849,8 +1846,12 @@
     "\n",
     "docs.scipy.org. 2021. scipy.stats.linregress. [online] Available at:<https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html> [Accessed 19 April 2021].\n",
     "\n",
+    "Docs.scipy.org. 2021. scipy.stats.pearsonr. [online] Available at: <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.pearsonr.html> [Accessed 21 April 2021].\n",
+    "\n",
     "Hayden, A., 2021. Pandas sum by groupby, but exclude certain columns. [online] Stack Overflow. Available at: <https://stackoverflow.com/questions/32751229/pandas-sum-by-groupby-but-exclude-certain-columns> [Accessed 16 April 2021].\n",
     "\n",
+    "Kaggle.com. 2021. Plot with seaborn after groupby command in pandas | Data Science and Machine Learning. [online] Available at: <https://www.kaggle.com/questions-and-answers/55356> [Accessed 21 April 2021].\n",
+    "\n",
     "Pandas.pydata.org. 2021. pandas.DataFrame.rename — pandas 1.2.4 documentation. [online] Available at: <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rename.html> [Accessed 15 April 2021].\n",
     "\n",
     "Seaborn.pydata.org. 2021. Visualizing regression models — seaborn 0.11.1 documentation. [online] Available at: <https://seaborn.pydata.org/tutorial/regression.html> [Accessed 19 April 2021].\n"