diff --git a/.ipynb_checkpoints/argument-checkpoint.ipynb b/.ipynb_checkpoints/argument-checkpoint.ipynb
index dd521f4..3c8826a 100644
--- a/.ipynb_checkpoints/argument-checkpoint.ipynb
+++ b/.ipynb_checkpoints/argument-checkpoint.ipynb
@@ -122,7 +122,7 @@
        "      <th>0</th>\n",
        "      <td>2</td>\n",
        "      <td>EN</td>\n",
-       "      <td>77</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "      <td>2</td>\n",
@@ -146,7 +146,7 @@
        "      <th>1</th>\n",
        "      <td>7</td>\n",
        "      <td>EN</td>\n",
-       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>2</td>\n",
        "      <td>1</td>\n",
        "      <td>3</td>\n",
@@ -170,7 +170,7 @@
        "      <th>2</th>\n",
        "      <td>9</td>\n",
        "      <td>EN</td>\n",
-       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>2</td>\n",
        "      <td>1</td>\n",
        "      <td>3</td>\n",
@@ -194,7 +194,7 @@
        "      <th>3</th>\n",
        "      <td>10</td>\n",
        "      <td>EN</td>\n",
-       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>4</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
@@ -218,7 +218,7 @@
        "      <th>4</th>\n",
        "      <td>13</td>\n",
        "      <td>EN</td>\n",
-       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>4</td>\n",
        "      <td>4</td>\n",
        "      <td>2</td>\n",
@@ -245,11 +245,11 @@
       ],
       "text/plain": [
        "   CASEID SURV_LANG  FITIN  FRIEND1  FRIEND2  FRIEND3  FRIEND4_1  FRIEND4_2  \\\n",
-       "0       2        EN     77        3        1        2          0          1   \n",
-       "1       7        EN      2        2        1        3          1          1   \n",
-       "2       9        EN      2        2        1        3          0          0   \n",
-       "3      10        EN      1        4        3        2          0          1   \n",
-       "4      13        EN      2        4        4        2          0          1   \n",
+       "0       2        EN    NaN        3        1        2          0          1   \n",
+       "1       7        EN    2.0        2        1        3          1          1   \n",
+       "2       9        EN    2.0        2        1        3          0          0   \n",
+       "3      10        EN    1.0        4        3        2          0          1   \n",
+       "4      13        EN    2.0        4        4        2          0          1   \n",
        "\n",
        "   FRIEND4_3  FRIEND4_4  ...  HH25  HH612  HH1317 HH18OV   CO_DATE   DURATION  \\\n",
        "0          0          1  ...     1      3       2      4  20180322  22.866667   \n",
@@ -306,7 +306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 4,
    "id": "basic-canadian",
    "metadata": {},
    "outputs": [
@@ -342,36 +342,36 @@
        "      <th>0</th>\n",
        "      <td>14</td>\n",
        "      <td>5</td>\n",
-       "      <td>77</td>\n",
-       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>15</td>\n",
        "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>13</td>\n",
        "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>17</td>\n",
        "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>15</td>\n",
        "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -379,14 +379,14 @@
       ],
       "text/plain": [
        "   AGE  INTREQ  FITIN  SOC1\n",
-       "0   14       5     77     3\n",
-       "1   15       1      2     2\n",
-       "2   13       2      2     3\n",
-       "3   17       1      1     2\n",
-       "4   15       2      2     2"
+       "0   14       5    NaN   3.0\n",
+       "1   15       1    2.0   2.0\n",
+       "2   13       2    2.0   3.0\n",
+       "3   17       1    1.0   2.0\n",
+       "4   15       2    2.0   2.0"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -404,7 +404,7 @@
     "AGE indicates participant age\n",
     "INTREQ indicates frequency of which a teen uses technology (further defined below)\n",
     "FITIN indicates how well a teen thinks he or she fits in\n",
-    "SOCI indicates what effect teens think social media has on teens their age."
+    "SOC1 indicates what effect teens think social media has on teens their age."
    ]
   },
   {
@@ -434,7 +434,25 @@
     "  - *What data science tools/functions will you use and why?* \n",
     "  \n",
     "✏️ *Write your answer below:*\n",
-    "\n"
+    "\n",
+    "Teens in this survery were grouped into 5 age categories ranging incluisviely from age 13 to age 17. The number of participants in each category was roughly equal as indicated in the table below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "def017b8-7537-4963-9c50-2f9087260cfb",
+   "metadata": {},
+   "source": [
+    "Figure 1 shows a countplot of number of teens vs. age, and broken down based on their answer to the question \"How frequently do you use technology?\" A countplot was selected here in order to indicate how many participants fit in a certain category.\n",
+    "\n",
+    "The numeric scores on the legend correspond as follows:\n",
+    "1- Almost constantly\n",
+    "2- Several Times a Day\n",
+    "3- About Once a Day\n",
+    "4- Several times a week\n",
+    "5- Less often\n",
+    "\n",
+    "Figure 2 focuses on the most frequent response (almost constantly and several times a day) and then sorts those responses by age to provide another way to look at the most frequent responses in the data."
    ]
   },
   {
@@ -445,13 +463,19 @@
     "### Results "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "405135b6-700d-40b8-bc0e-3ed338190f74",
+   "metadata": {},
+   "source": [
+    "Table 1: Percent of Respondents in Each Age Category"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 65,
-   "id": "39209c3f-7c3f-4a3e-abbd-7904e485e9e3",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 5,
+   "id": "bdf7dd26-d0c1-469e-9458-bf014cd02200",
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -464,20 +488,28 @@
        "Name: AGE, dtype: float64"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "new_df.AGE.value_counts(normalize=True)\n",
-    "\n",
-    "\n"
+    "new_df.AGE.value_counts(normalize=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7297da9-bfaf-415d-aa60-b9353564d256",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Figure 1: Number of Respondents in Each Age Category Bassed on How Frequently They Utilize Technology"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "negative-highlight",
    "metadata": {},
    "outputs": [],
@@ -488,7 +520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 7,
    "id": "victorian-burning",
    "metadata": {},
    "outputs": [
@@ -498,7 +530,7 @@
        "<Axes: xlabel='AGE', ylabel='count'>"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -523,9 +555,17 @@
     "#legend(handles, new_labels)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "096c0ad1-c7f0-4984-ad86-1b07e4ef6478",
+   "metadata": {},
+   "source": [
+    "Figure 2: Number of Teens That Use Technology Almost Constantly or Several Times Per Day Sorted by Age"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 8,
    "id": "2eff3a2b-30ca-4cf3-b365-d41acc121ece",
    "metadata": {
     "tags": []
@@ -535,7 +575,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/c3/lngy07lx6hx3_kdztwrlr7j80000gn/T/ipykernel_6406/2489872726.py:3: SettingWithCopyWarning: \n",
+      "/var/folders/c3/lngy07lx6hx3_kdztwrlr7j80000gn/T/ipykernel_12977/2489872726.py:3: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
@@ -552,7 +592,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 9,
    "id": "fa39e859-5e46-4592-a0ac-d4ffd00a5038",
    "metadata": {
     "tags": []
@@ -564,7 +604,7 @@
        "<Axes: xlabel='Tech Frequency', ylabel='count'>"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -588,7 +628,7 @@
    "id": "collectible-puppy",
    "metadata": {},
    "source": [
-    "## Second Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n"
+    "## Second Research Question: How does frequency of using technology correspond with how well a teen perceives he or she fits in? and How does frequency of using social media correspond with how much a teen thinks social media has an effect on people their age?\n"
    ]
   },
   {
@@ -609,7 +649,13 @@
     "  - *How will you reorganize/store the data?* \n",
     "  - *What data science tools/functions will you use and why?* \n",
     "\n",
-    "✏️ *Write your answer below:*\n"
+    "✏️ *Write your answer below:*\n",
+    "This question will continue to look at the responses sorted by age, but this time will focus on the responses to 2 questions.\n",
+    "\n",
+    "The first, \"In general, which of the following statements comes closest to describing how you see yourself compared with other people your age where you live?\" where the choices were 1-I tend to fit in pretty easily or 2-I tend to stand out.\n",
+    "\n",
+    "And the second, \"Overall, what effect would you say social media has had on people your age?\", where the choices were 1-mostly positive, 2- mostly negative, or 3- neither positive or negative.\n",
+    "\n"
    ]
   },
   {
@@ -622,26 +668,64 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "id": "pursuant-surrey",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='INTREQ', ylabel='count'>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "#######################################################################\n",
-    "### 💻 YOUR WORK GOES HERE TO ANSWER THE SECOND RESEARCH QUESTION 💻 \n",
-    "###\n",
-    "### Your data analysis may include a statistic and/or a data visualization\n",
-    "#######################################################################"
+    "sns.countplot(data=new_df, x=\"INTREQ\", hue=\"FITIN\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "id": "located-night",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='INTREQ', ylabel='count'>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
+    "sns.countplot(data=new_df, x=\"INTREQ\", hue=\"SOC1\")\n"
    ]
   },
   {
diff --git a/argument.ipynb b/argument.ipynb
index dd521f4..3c8826a 100644
--- a/argument.ipynb
+++ b/argument.ipynb
@@ -122,7 +122,7 @@
        "      <th>0</th>\n",
        "      <td>2</td>\n",
        "      <td>EN</td>\n",
-       "      <td>77</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "      <td>2</td>\n",
@@ -146,7 +146,7 @@
        "      <th>1</th>\n",
        "      <td>7</td>\n",
        "      <td>EN</td>\n",
-       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>2</td>\n",
        "      <td>1</td>\n",
        "      <td>3</td>\n",
@@ -170,7 +170,7 @@
        "      <th>2</th>\n",
        "      <td>9</td>\n",
        "      <td>EN</td>\n",
-       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>2</td>\n",
        "      <td>1</td>\n",
        "      <td>3</td>\n",
@@ -194,7 +194,7 @@
        "      <th>3</th>\n",
        "      <td>10</td>\n",
        "      <td>EN</td>\n",
-       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>4</td>\n",
        "      <td>3</td>\n",
        "      <td>2</td>\n",
@@ -218,7 +218,7 @@
        "      <th>4</th>\n",
        "      <td>13</td>\n",
        "      <td>EN</td>\n",
-       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>4</td>\n",
        "      <td>4</td>\n",
        "      <td>2</td>\n",
@@ -245,11 +245,11 @@
       ],
       "text/plain": [
        "   CASEID SURV_LANG  FITIN  FRIEND1  FRIEND2  FRIEND3  FRIEND4_1  FRIEND4_2  \\\n",
-       "0       2        EN     77        3        1        2          0          1   \n",
-       "1       7        EN      2        2        1        3          1          1   \n",
-       "2       9        EN      2        2        1        3          0          0   \n",
-       "3      10        EN      1        4        3        2          0          1   \n",
-       "4      13        EN      2        4        4        2          0          1   \n",
+       "0       2        EN    NaN        3        1        2          0          1   \n",
+       "1       7        EN    2.0        2        1        3          1          1   \n",
+       "2       9        EN    2.0        2        1        3          0          0   \n",
+       "3      10        EN    1.0        4        3        2          0          1   \n",
+       "4      13        EN    2.0        4        4        2          0          1   \n",
        "\n",
        "   FRIEND4_3  FRIEND4_4  ...  HH25  HH612  HH1317 HH18OV   CO_DATE   DURATION  \\\n",
        "0          0          1  ...     1      3       2      4  20180322  22.866667   \n",
@@ -306,7 +306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 4,
    "id": "basic-canadian",
    "metadata": {},
    "outputs": [
@@ -342,36 +342,36 @@
        "      <th>0</th>\n",
        "      <td>14</td>\n",
        "      <td>5</td>\n",
-       "      <td>77</td>\n",
-       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>15</td>\n",
        "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>13</td>\n",
        "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>17</td>\n",
        "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>15</td>\n",
        "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -379,14 +379,14 @@
       ],
       "text/plain": [
        "   AGE  INTREQ  FITIN  SOC1\n",
-       "0   14       5     77     3\n",
-       "1   15       1      2     2\n",
-       "2   13       2      2     3\n",
-       "3   17       1      1     2\n",
-       "4   15       2      2     2"
+       "0   14       5    NaN   3.0\n",
+       "1   15       1    2.0   2.0\n",
+       "2   13       2    2.0   3.0\n",
+       "3   17       1    1.0   2.0\n",
+       "4   15       2    2.0   2.0"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -404,7 +404,7 @@
     "AGE indicates participant age\n",
     "INTREQ indicates frequency of which a teen uses technology (further defined below)\n",
     "FITIN indicates how well a teen thinks he or she fits in\n",
-    "SOCI indicates what effect teens think social media has on teens their age."
+    "SOC1 indicates what effect teens think social media has on teens their age."
    ]
   },
   {
@@ -434,7 +434,25 @@
     "  - *What data science tools/functions will you use and why?* \n",
     "  \n",
     "✏️ *Write your answer below:*\n",
-    "\n"
+    "\n",
+    "Teens in this survery were grouped into 5 age categories ranging incluisviely from age 13 to age 17. The number of participants in each category was roughly equal as indicated in the table below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "def017b8-7537-4963-9c50-2f9087260cfb",
+   "metadata": {},
+   "source": [
+    "Figure 1 shows a countplot of number of teens vs. age, and broken down based on their answer to the question \"How frequently do you use technology?\" A countplot was selected here in order to indicate how many participants fit in a certain category.\n",
+    "\n",
+    "The numeric scores on the legend correspond as follows:\n",
+    "1- Almost constantly\n",
+    "2- Several Times a Day\n",
+    "3- About Once a Day\n",
+    "4- Several times a week\n",
+    "5- Less often\n",
+    "\n",
+    "Figure 2 focuses on the most frequent response (almost constantly and several times a day) and then sorts those responses by age to provide another way to look at the most frequent responses in the data."
    ]
   },
   {
@@ -445,13 +463,19 @@
     "### Results "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "405135b6-700d-40b8-bc0e-3ed338190f74",
+   "metadata": {},
+   "source": [
+    "Table 1: Percent of Respondents in Each Age Category"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 65,
-   "id": "39209c3f-7c3f-4a3e-abbd-7904e485e9e3",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 5,
+   "id": "bdf7dd26-d0c1-469e-9458-bf014cd02200",
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -464,20 +488,28 @@
        "Name: AGE, dtype: float64"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "new_df.AGE.value_counts(normalize=True)\n",
-    "\n",
-    "\n"
+    "new_df.AGE.value_counts(normalize=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7297da9-bfaf-415d-aa60-b9353564d256",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Figure 1: Number of Respondents in Each Age Category Bassed on How Frequently They Utilize Technology"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "negative-highlight",
    "metadata": {},
    "outputs": [],
@@ -488,7 +520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 7,
    "id": "victorian-burning",
    "metadata": {},
    "outputs": [
@@ -498,7 +530,7 @@
        "<Axes: xlabel='AGE', ylabel='count'>"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -523,9 +555,17 @@
     "#legend(handles, new_labels)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "096c0ad1-c7f0-4984-ad86-1b07e4ef6478",
+   "metadata": {},
+   "source": [
+    "Figure 2: Number of Teens That Use Technology Almost Constantly or Several Times Per Day Sorted by Age"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 8,
    "id": "2eff3a2b-30ca-4cf3-b365-d41acc121ece",
    "metadata": {
     "tags": []
@@ -535,7 +575,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/c3/lngy07lx6hx3_kdztwrlr7j80000gn/T/ipykernel_6406/2489872726.py:3: SettingWithCopyWarning: \n",
+      "/var/folders/c3/lngy07lx6hx3_kdztwrlr7j80000gn/T/ipykernel_12977/2489872726.py:3: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
@@ -552,7 +592,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 9,
    "id": "fa39e859-5e46-4592-a0ac-d4ffd00a5038",
    "metadata": {
     "tags": []
@@ -564,7 +604,7 @@
        "<Axes: xlabel='Tech Frequency', ylabel='count'>"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -588,7 +628,7 @@
    "id": "collectible-puppy",
    "metadata": {},
    "source": [
-    "## Second Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n"
+    "## Second Research Question: How does frequency of using technology correspond with how well a teen perceives he or she fits in? and How does frequency of using social media correspond with how much a teen thinks social media has an effect on people their age?\n"
    ]
   },
   {
@@ -609,7 +649,13 @@
     "  - *How will you reorganize/store the data?* \n",
     "  - *What data science tools/functions will you use and why?* \n",
     "\n",
-    "✏️ *Write your answer below:*\n"
+    "✏️ *Write your answer below:*\n",
+    "This question will continue to look at the responses sorted by age, but this time will focus on the responses to 2 questions.\n",
+    "\n",
+    "The first, \"In general, which of the following statements comes closest to describing how you see yourself compared with other people your age where you live?\" where the choices were 1-I tend to fit in pretty easily or 2-I tend to stand out.\n",
+    "\n",
+    "And the second, \"Overall, what effect would you say social media has had on people your age?\", where the choices were 1-mostly positive, 2- mostly negative, or 3- neither positive or negative.\n",
+    "\n"
    ]
   },
   {
@@ -622,26 +668,64 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "id": "pursuant-surrey",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='INTREQ', ylabel='count'>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "#######################################################################\n",
-    "### 💻 YOUR WORK GOES HERE TO ANSWER THE SECOND RESEARCH QUESTION 💻 \n",
-    "###\n",
-    "### Your data analysis may include a statistic and/or a data visualization\n",
-    "#######################################################################"
+    "sns.countplot(data=new_df, x=\"INTREQ\", hue=\"FITIN\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "id": "located-night",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='INTREQ', ylabel='count'>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
+    "sns.countplot(data=new_df, x=\"INTREQ\", hue=\"SOC1\")\n"
    ]
   },
   {
diff --git a/data/March 7-April 10, 2018 - Teens and Tech Survey - CSV.csv b/data/March 7-April 10, 2018 - Teens and Tech Survey - CSV.csv
index 713a57d..21b0bb8 100644
--- a/data/March 7-April 10, 2018 - Teens and Tech Survey - CSV.csv	
+++ b/data/March 7-April 10, 2018 - Teens and Tech Survey - CSV.csv	
@@ -1,12 +1,12 @@
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@@ -35,7 +35,7 @@ CASEID,SURV_LANG,FITIN,FRIEND1,FRIEND2,FRIEND3,FRIEND4_1,FRIEND4_2,FRIEND4_3,FRI
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@@ -113,7 +113,7 @@ CASEID,SURV_LANG,FITIN,FRIEND1,FRIEND2,FRIEND3,FRIEND4_1,FRIEND4_2,FRIEND4_3,FRI
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 432,EN,2,2,2,3,0,0,1,0,1,0,2,0,1,1,0,1,1,0,1,2,1,1,4,3,3,1,1,1,1,0,0,1,0,1,0,6,3, , , , , , , , , , , , , , , , ,4,4,4,3,4,0,0,0,0,0,0,0,1,3,2,3,2,3,3,3,3,1,1,1,1,2,3,4,2,0,0,0,0,1,0,1,77,1,2,1,3,1,1,2,1,0,0,0,0,0,1,1,2,1,2,2,3,4,3,1,0,0,1,0,0,1,0,0,0,0,0,3,3,3,3,1,1,1,2,4,3,2,2,3,2,0,0,0,0,0,0,1,4,2,1,3,1,1,1,14,13,1,1,1,16,1,4,1,1,4,0,0,1,1,2,20180322,10.46666667,2,2,Smartphone,0.468697357
 460,EN,1,1,1,3,0,0,1,1,0,0,3,1,0,0,0,0,1,0,1,2,1,1,4,4,4,5,1,0,0,1,1,1,0,0,0,4,3, , , , , , , , , , , , , , , , ,4,2,4,4,4,0,0,1,0,0,0,0,0,3,3,2,1,3,3,3,3,1,1,1,1,4,3,4,4, , , , , , ,4,4,3,3,2,3,1,2,2,1,0,0,0,0,0,1,2,2,2,2,4,4,4,1,4, , , , , , , , , , , , , , , ,2,1,2,2,4,2,2,2,4,4,1,0,0,0,0,1,0,4,1,4,1,4,1,1,13,11,1,1,1,3,1,4,0,2,4,0,0,1,1,2,20180318,17.11666667,1,1,Desktop,0.430646059
-462,EN,77,3,4,3,1,0,0,0,0,0,2,1,1,0,0,0,1,0,1,2,1,1,4,2,4,2,1,0,0,0,0,0,0,0,1, ,3, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,2,2,1,1,3, ,3,1, ,1,0,0,0,0,0,1,1,1,2,3,1,1,4,4,2,1,1,1,1,1,1,1,1,1,1,0,1,77,1,1,1,1,2,2,2,2,2,3,77,2,1,0,0,0,0,0,0,2,2,1,3,3,2,2,13,1,1,1,1,14,1,2,1,1,4,0,0,2,0,2,20180402,17.96666667,1,1,Desktop,2.345811827
+462,EN,,3,4,3,1,0,0,0,0,0,2,1,1,0,0,0,1,0,1,2,1,1,4,2,4,2,1,0,0,0,0,0,0,0,1, ,3, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,2,2,1,1,3, ,3,1, ,1,0,0,0,0,0,1,1,1,2,3,1,1,4,4,2,1,1,1,1,1,1,1,1,1,1,0,1,77,1,1,1,1,2,2,2,2,2,3,77,2,1,0,0,0,0,0,0,2,2,1,3,3,2,2,13,1,1,1,1,14,1,2,1,1,4,0,0,2,0,2,20180402,17.96666667,1,1,Desktop,2.345811827
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@@ -133,7 +133,7 @@ CASEID,SURV_LANG,FITIN,FRIEND1,FRIEND2,FRIEND3,FRIEND4_1,FRIEND4_2,FRIEND4_3,FRI
 516,EN,1,4,1,3,0,0,0,0,0,1,2,1,1,0,1,0,1,0,1,2,1,1,4,1,4,2,2,0,1,0,1,0,0,0,0,2,2, , , , , , , , , , , , ,1, , , ,4,4,3,4,3,1,0,1,1,0,0,0,0,1,1,1,2,2,2,1,1,1,2,2,1,2,4,4,3, , , , , , ,1,2,1,1,1,1, ,2,2, ,0,0,0,1,0,0,1,1,1,3,1,2,4,3,3,0,1,0,0,1,1,0,0,1,0,0,3,2,3,2,1,1,1,1,4,4,3,2,4,2,0,0,0,0,0,0,1,3,1,1,4,1,2,2,15,14,1,1,1,18,1,2,1,1,3,0,0,0,1,2,20180319,10.46666667,2,2,Smartphone,0.641279623
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-524,EN,98,1,1,3,0,1,1,0,0,0,2,1,1,0,1,1,1,0,1,2,1,1,2,2,3,1,1,0,1,1,1,1,0,0,0,4,3, , , , , , , , , , , , , , , , ,1,2,4,2,2,1,0,0,0,0,0,0,0,2,2,1,2,3,2,3,2,1,1,2,1,4,2,2,1,1,1,0,1,0,0,2,3,2,2,1,1,2,1,1,2,0,0,1,1,0,0,1,2,2,2,3,4,2,1,1,0,1,0,0,0,0,0,0,0,1,0,2,2,2,1,1,1,1,1,4,4,2,2,3,3,1,0,1,0,1,0,0,1,2,2,4,4,1,2,13,8,1,2,1,3,0,4,1,1,6,0,0,3,0,3,20180322,17.31666667,1,1,Desktop,4.352687553
+524,EN,,1,1,3,0,1,1,0,0,0,2,1,1,0,1,1,1,0,1,2,1,1,2,2,3,1,1,0,1,1,1,1,0,0,0,4,3, , , , , , , , , , , , , , , , ,1,2,4,2,2,1,0,0,0,0,0,0,0,2,2,1,2,3,2,3,2,1,1,2,1,4,2,2,1,1,1,0,1,0,0,2,3,2,2,1,1,2,1,1,2,0,0,1,1,0,0,1,2,2,2,3,4,2,1,1,0,1,0,0,0,0,0,0,0,1,0,2,2,2,1,1,1,1,1,4,4,2,2,3,3,1,0,1,0,1,0,0,1,2,2,4,4,1,2,13,8,1,2,1,3,0,4,1,1,6,0,0,3,0,3,20180322,17.31666667,1,1,Desktop,4.352687553
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@@ -338,7 +338,7 @@ CASEID,SURV_LANG,FITIN,FRIEND1,FRIEND2,FRIEND3,FRIEND4_1,FRIEND4_2,FRIEND4_3,FRI
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-1326,EN,1,4,1,2,1,0,0,0,0,0,2,0,1,0,0,0,1,0,1,2,1,1,4,4,4,1,1,0,1,0,1,1,0,0,0,4,98, , , , , , , , , , , , , , , , ,1,1,1,1,3,1,0,1,0,0,0,1,0,2,2,2,2,3,3,3,3,1,1,2,1,2,4,4,2,1,0,0,0,0,0,2,2,1,2,1,1,2,2,2,1,1,0,1,0,1,0,1,2,1,3,2,1,4,2,3,0,1,0,0,0,1,0,0,1,0,0,3,3,3,3,1,2,2,1,3,4,3,2,2,3,1,0,0,0,0,0,0,2,2,1,2,2,1,2,13,10,4,3,2,10,1,4,1,2,6,0,2,0,2,2,20180308,10.18333333,2,2,Smartphone,0.402282477
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