diff --git a/UFCFVQ-15-M_Python_Programming_Template.ipynb b/UFCFVQ-15-M_Python_Programming_Template.ipynb
index be7125729e6ca5bf3d7e28bbe79514065e8be7a6..092515a0b5e899d1fe2da652f7be85559d247459 100644
--- a/UFCFVQ-15-M_Python_Programming_Template.ipynb
+++ b/UFCFVQ-15-M_Python_Programming_Template.ipynb
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 1,
    "metadata": {
     "deletable": false
    },
@@ -115,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 2,
    "metadata": {
     "deletable": false
    },
@@ -201,7 +201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 3,
    "metadata": {
     "deletable": false
    },
@@ -280,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 4,
    "outputs": [
     {
      "name": "stdout",
@@ -396,7 +396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 5,
    "metadata": {
     "deletable": false
    },
@@ -477,7 +477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 6,
    "metadata": {
     "deletable": false
    },
diff --git a/UFCFVQ-15-M_Python_Programming_With_Libraries_Template.ipynb b/UFCFVQ-15-M_Python_Programming_With_Libraries_Template.ipynb
index 1ae17186471ccc6aed8ecfdb1f5dff526ffbe65c..c80f0fac2ce6cb6fe429ad87a8056e1afe4bb214 100644
--- a/UFCFVQ-15-M_Python_Programming_With_Libraries_Template.ipynb
+++ b/UFCFVQ-15-M_Python_Programming_With_Libraries_Template.ipynb
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {
     "deletable": false
    },
@@ -115,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {
     "deletable": false
    },
@@ -175,7 +175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {
     "deletable": false
    },
@@ -235,12 +235,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {
-    "deletable": false,
-    "pycharm": {
-     "is_executing": true
-    }
+    "deletable": false
    },
    "outputs": [
     {
@@ -250,6 +247,14 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation Coefficient between Click Events and Scores: 0.28 \n",
+      "\n"
+     ]
     }
    ],
    "source": [
@@ -308,14 +313,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {
-    "deletable": false,
-    "pycharm": {
-     "is_executing": true
-    }
+    "deletable": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "P-value:  0.0000 \n",
+      "\n",
+      "reject H0. This means there are not enough evidence to say that Click events do not have significant effect on scores\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "# I am taking the hypothesis as follows\n",
     "#\n",
@@ -383,14 +396,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {
-    "deletable": false,
-    "pycharm": {
-     "is_executing": true
-    }
+    "deletable": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 800x600 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "try:\n",
     "    # draw box-plot for see the relationship between gender and scores\n",
@@ -446,14 +465,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {
-    "deletable": false,
-    "pycharm": {
-     "is_executing": true
-    }
+    "deletable": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "P-value: 0.5073 \n",
+      "\n",
+      "fail to reject H0. This means that there are evidence to say there is no statistically significant difference between the attainment of male and female students  \n",
+      "\n",
+      "Mann-Whitney U Test Statistic: 79066488.0\n",
+      "P-value: 2.9229042433461316e-308\n"
+     ]
+    }
+   ],
    "source": [
     "# We can use independent two-sample t-test to identify that is there any statistically significant difference between the attainment of male and female students.\n",
     "\n",
@@ -481,7 +510,20 @@
     "if p_value_for_gender > alpha2:\n",
     "    print('fail to reject H0. This means that there are evidence to say there is no statistically significant difference between the attainment of male and female students  \\n')\n",
     "else:\n",
-    "    print('reject H0. This means we have not enough evidence to reject the statement that  there is no statistically significant difference between the attainment of male and female students \\n')"
+    "    print('reject H0. This means we have not enough evidence to reject the statement that  there is no statistically significant difference between the attainment of male and female students \\n')\n",
+    "\n",
+    "# Another suitable method is the Mann-Whitney U test, which is a non-parametric test that compares two independent samples to determine if they come from the same population or not. In our case, we can divide the students into two groups based on engagement level (high and low) and compare their scores to see if there is a statistically significant difference.\n",
+    "\n",
+    "eng_limit = filtered_df['click_events'].median()\n",
+    "\n",
+    "# Split the students into high and low engagement groups based on the limit\n",
+    "high_engagement = filtered_df[filtered_df['click_events'] > eng_limit]['score']\n",
+    "low_engagement = filtered_df[filtered_df['click_events'] <= eng_limit]['score']\n",
+    "\n",
+    "stat, p_value = stats.mannwhitneyu(high_engagement, low_engagement, alternative='two-sided')\n",
+    "print(f\"Mann-Whitney U Test Statistic: {stat}\")\n",
+    "print(f\"P-value: {p_value}\")\n",
+    "\n"
    ]
   },
   {
@@ -492,7 +534,7 @@
     "##### H0 -> There is no statistically significant difference between the attainment of male and female students\n",
     "##### HA -> There is a statistically significant difference between the attainment of male and female students\n",
     "\n",
-    "Bases on the p-value from above t-test (which is 0.5073) I cannot reject the null hypothesis that there is no significant difference between the male and female students with their scores. This p-value is greater than the typical significance level of 0.05 (which is alpha2 here), which means that there is not enough evidence to conclude that there is a statistically significant difference in the attainment levels of male and female students.\n",
+    "Bases on the p-value from above t-test (which is 0.5073) and Mann-Whitney U Test p-value (which is 2.923)  I failed to reject the null hypothesis that there is no significant difference between the male and female students with their scores. I chose Mann-Whitney U Test as well because it is a non-parametric test that compares two independent samples to determine if they come from the same population or not. This p-value is greater than the typical significance level of 0.05 (which is alpha2 here), which means that there is not enough evidence to conclude that there is a statistically significant difference in the attainment levels of male and female students.\n",
     "\n",
     "After analyzing the relevant data and employing statistical assessment, it appears that an individual's gender has minimal influence on their test scores. Nevertheless, it is important to realize that in not rejecting the null hypothesis, one does not necessarily prove its validity; rather, it implies a lack of conclusive proof endorsing marked differences between groups based on present findings."
    ],