diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 11ab621..ddb14f4 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -23,6 +23,20 @@
     "Use either or both of the lbraries in this exercise."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy\n",
+    "from scipy.stats import uniform\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -35,9 +49,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2.17292012 2.73740455 2.93017991 2.93132215 2.30918891 2.22749142\n",
+      " 2.19968188 2.62536416 2.14657739 2.1802302 ]\n"
+     ]
+    }
+   ],
    "source": [
     "from scipy.stats import uniform\n",
     "x = uniform.rvs(size=10)\n",
@@ -73,11 +96,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "def uniformly_distributed(bottom, ceiling, count):\n",
+    "    return np.random.uniform(bottom, ceiling, count)\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 2, sharey=True)\n",
+    "\n",
+    "arr_one = uniformly_distributed(10, 15, 100)\n",
+    "ax[0].hist(arr_one, bins=10)\n",
+    "ax[0].set_xticks(np.arange(10, 16, 1))\n",
+    "\n",
+    "arr_two = uniformly_distributed(10, 60, 1000)\n",
+    "ax[1].hist(arr_two, bins=10)\n",
+    "ax[1].set_xticks(np.arange(10, max(arr_two) + 10, 10))\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
@@ -114,11 +162,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "def normal_distributed(mean, stdev, count):\n",
+    "    return np.random.normal(mean, stdev, count)\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 2, sharey=True)\n",
+    "\n",
+    "arr_one = normal_distributed(10, 1, 1000)\n",
+    "ax[0].hist(arr_one, bins=50)\n",
+    "\n",
+    "arr_two = normal_distributed(10, 50, 1000)\n",
+    "ax[1].hist(arr_two, bins=50)\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
@@ -158,11 +229,160 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Make</th>\n",
+       "      <th>Model</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Engine Displacement</th>\n",
+       "      <th>Cylinders</th>\n",
+       "      <th>Transmission</th>\n",
+       "      <th>Drivetrain</th>\n",
+       "      <th>Vehicle Class</th>\n",
+       "      <th>Fuel Type</th>\n",
+       "      <th>Fuel Barrels/Year</th>\n",
+       "      <th>City MPG</th>\n",
+       "      <th>Highway MPG</th>\n",
+       "      <th>Combined MPG</th>\n",
+       "      <th>CO2 Emission Grams/Mile</th>\n",
+       "      <th>Fuel Cost/Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>AM General</td>\n",
+       "      <td>DJ Po Vehicle 2WD</td>\n",
+       "      <td>1984</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>Automatic 3-spd</td>\n",
+       "      <td>2-Wheel Drive</td>\n",
+       "      <td>Special Purpose Vehicle 2WD</td>\n",
+       "      <td>Regular</td>\n",
+       "      <td>19.388824</td>\n",
+       "      <td>18</td>\n",
+       "      <td>17</td>\n",
+       "      <td>17</td>\n",
+       "      <td>522.764706</td>\n",
+       "      <td>1950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AM General</td>\n",
+       "      <td>FJ8c Post Office</td>\n",
+       "      <td>1984</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>Automatic 3-spd</td>\n",
+       "      <td>2-Wheel Drive</td>\n",
+       "      <td>Special Purpose Vehicle 2WD</td>\n",
+       "      <td>Regular</td>\n",
+       "      <td>25.354615</td>\n",
+       "      <td>13</td>\n",
+       "      <td>13</td>\n",
+       "      <td>13</td>\n",
+       "      <td>683.615385</td>\n",
+       "      <td>2550</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AM General</td>\n",
+       "      <td>Post Office DJ5 2WD</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>Automatic 3-spd</td>\n",
+       "      <td>Rear-Wheel Drive</td>\n",
+       "      <td>Special Purpose Vehicle 2WD</td>\n",
+       "      <td>Regular</td>\n",
+       "      <td>20.600625</td>\n",
+       "      <td>16</td>\n",
+       "      <td>17</td>\n",
+       "      <td>16</td>\n",
+       "      <td>555.437500</td>\n",
+       "      <td>2100</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Make                Model  Year  Engine Displacement  Cylinders  \\\n",
+       "0  AM General    DJ Po Vehicle 2WD  1984                  2.5        4.0   \n",
+       "1  AM General     FJ8c Post Office  1984                  4.2        6.0   \n",
+       "2  AM General  Post Office DJ5 2WD  1985                  2.5        4.0   \n",
+       "\n",
+       "      Transmission        Drivetrain                Vehicle Class Fuel Type  \\\n",
+       "0  Automatic 3-spd     2-Wheel Drive  Special Purpose Vehicle 2WD   Regular   \n",
+       "1  Automatic 3-spd     2-Wheel Drive  Special Purpose Vehicle 2WD   Regular   \n",
+       "2  Automatic 3-spd  Rear-Wheel Drive  Special Purpose Vehicle 2WD   Regular   \n",
+       "\n",
+       "   Fuel Barrels/Year  City MPG  Highway MPG  Combined MPG  \\\n",
+       "0          19.388824        18           17            17   \n",
+       "1          25.354615        13           13            13   \n",
+       "2          20.600625        16           17            16   \n",
+       "\n",
+       "   CO2 Emission Grams/Mile  Fuel Cost/Year  \n",
+       "0               522.764706            1950  \n",
+       "1               683.615385            2550  \n",
+       "2               555.437500            2100  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vehicles = pd.read_csv('vehicles.csv')\n",
+    "vehicles.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "vehicles['Fuel Barrels/Year']\n",
+    "sns.histplot(data=vehicles, x=\"Fuel Barrels/Year\", bins=50)\n",
+    "plt.axvline(vehicles['Fuel Barrels/Year'].mean(), c=\"r\")\n",
+    "plt.ylabel(\"Years\");"
    ]
   },
   {
@@ -174,11 +394,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "sns.histplot(data=vehicles, x=\"CO2 Emission Grams/Mile\", bins=50)\n",
+    "plt.axvline(vehicles['CO2 Emission Grams/Mile'].mean(), c=\"r\")\n",
+    "plt.ylabel(\"Years\");"
    ]
   },
   {
@@ -190,11 +424,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "sns.histplot(data=vehicles, x=\"Combined MPG\", bins=50)\n",
+    "plt.axvline(vehicles['Combined MPG'].mean(), c=\"r\")\n",
+    "plt.ylabel(\"Years\");"
    ]
   },
   {
@@ -237,11 +485,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "def exp_distributed(mean, count):\n",
+    "    return np.random.exponential(mean, count)\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 2, sharey=True)\n",
+    "\n",
+    "arr_one = exp_distributed(2, 1000)\n",
+    "ax[0].hist(arr_one, bins=100)\n",
+    "ax[0].set_xticks(np.arange(0, max(arr_one), 2))\n",
+    "\n",
+    "arr_two = exp_distributed(200, 1000)\n",
+    "ax[1].hist(arr_two, bins=100)\n",
+    "ax[1].set_xticks(np.arange(0, max(arr_two), 300))\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
@@ -273,12 +546,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7768698398515702"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# your answer here\n",
-    "# Hint: This is same as saying P(x<15)"
+    "# Hint: This is same as saying P(x<15)\n",
+    "\n",
+    "def exponential_cdf(x, rate):\n",
+    "    return 1 - np.exp(-rate * x)\n",
+    "\n",
+    "mean_t = 10\n",
+    "rate = 1 / mean_t\n",
+    "time_thresh = 15\n",
+    "\n",
+    "p_less_than_15 = exponential_cdf(time_thresh, rate)\n",
+    "p_less_than_15"
    ]
   },
   {
@@ -290,11 +584,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.2231301601484298"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your answer here"
+    "# your answer here\n",
+    "\n",
+    "p_more_than_15 = 1 - p_less_than_15\n",
+    "p_more_than_15"
    ]
   },
   {
@@ -321,7 +629,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,