From ec1c95cc83762bfb64043f2e22deb38d109b5137 Mon Sep 17 00:00:00 2001
From: Tiago Mateus <tapmateus@gmail.com>
Date: Sun, 19 Nov 2023 18:44:21 +0000
Subject: [PATCH]  lab done

---
 your-code/continuous.ipynb | 612 ++++++++++++++++++++++++++++++++++---
 your-code/discrete.ipynb   | 188 ++++++++++--
 2 files changed, 742 insertions(+), 58 deletions(-)

diff --git a/your-code/continuous.ipynb b/your-code/continuous.ipynb
index 32218e9..8e079d1 100644
--- a/your-code/continuous.ipynb
+++ b/your-code/continuous.ipynb
@@ -23,6 +23,20 @@
     "Use either or both of the lbraries in this exercise."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import uniform\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.stats import bernoulli, binom, geom, poisson, uniform, expon, norm\n",
+    "import seaborn as sns"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -35,11 +49,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2.29119908 2.69720775 2.12999683 2.3371262  2.01903311 2.19973723\n",
+      " 2.97016527 2.11813382 2.05099553 2.29635188]\n"
+     ]
+    }
+   ],
    "source": [
-    "from scipy.stats import uniform\n",
     "x = uniform.rvs(size=10)\n",
     "a = 2\n",
     "b = 3\n",
@@ -73,11 +95,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here\n"
+    "#1.\n",
+    "\n",
+    "def rand_numbers(bottom, ceiling, count):\n",
+    "    randoms = bottom + (ceiling-bottom)*(uniform.rvs(size=count))\n",
+    "    return randoms\n",
+    "\n",
+    "#2. \n",
+    "x1 = rand_numbers(10,15,100)\n",
+    "x2 = rand_numbers(10,60,1000)"
    ]
   },
   {
@@ -89,11 +119,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your answer here:\n"
+    "sns.histplot(x1, bins=10)\n",
+    "plt.show()\n",
+    "\n",
+    "sns.histplot(x2,bins=10)\n",
+    "plt.show()"
    ]
   },
   {
@@ -114,11 +169,73 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here\n"
+    "normal_dist = norm(10, 1)\n",
+    "\n",
+    "norm = normal_dist.rvs(1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(norm, 100, density=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'numpy.ndarray' object is not callable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[8], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m normal_dist \u001b[38;5;241m=\u001b[39m \u001b[43mnorm\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m norm \u001b[38;5;241m=\u001b[39m normal_dist\u001b[38;5;241m.\u001b[39mrvs(\u001b[38;5;241m100\u001b[39m)\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'numpy.ndarray' object is not callable"
+     ]
+    }
+   ],
+   "source": [
+    "normal_dist = norm(10, 50)\n",
+    "\n",
+    "norm = normal_dist.rvs(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "plt.hist(norm2, 100, density = True)\n",
+    "plt.show()"
    ]
   },
   {
@@ -158,11 +275,345 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "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",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>AM General</td>\n",
+       "      <td>Post Office DJ8 2WD</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>6.0</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>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>4</th>\n",
+       "      <td>ASC Incorporated</td>\n",
+       "      <td>GNX</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>Automatic 4-spd</td>\n",
+       "      <td>Rear-Wheel Drive</td>\n",
+       "      <td>Midsize Cars</td>\n",
+       "      <td>Premium</td>\n",
+       "      <td>20.600625</td>\n",
+       "      <td>14</td>\n",
+       "      <td>21</td>\n",
+       "      <td>16</td>\n",
+       "      <td>555.437500</td>\n",
+       "      <td>2550</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35947</th>\n",
+       "      <td>smart</td>\n",
+       "      <td>fortwo coupe</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Auto(AM5)</td>\n",
+       "      <td>Rear-Wheel Drive</td>\n",
+       "      <td>Two Seaters</td>\n",
+       "      <td>Premium</td>\n",
+       "      <td>9.155833</td>\n",
+       "      <td>34</td>\n",
+       "      <td>38</td>\n",
+       "      <td>36</td>\n",
+       "      <td>244.000000</td>\n",
+       "      <td>1100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35948</th>\n",
+       "      <td>smart</td>\n",
+       "      <td>fortwo coupe</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Auto(AM5)</td>\n",
+       "      <td>Rear-Wheel Drive</td>\n",
+       "      <td>Two Seaters</td>\n",
+       "      <td>Premium</td>\n",
+       "      <td>9.155833</td>\n",
+       "      <td>34</td>\n",
+       "      <td>38</td>\n",
+       "      <td>36</td>\n",
+       "      <td>243.000000</td>\n",
+       "      <td>1100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35949</th>\n",
+       "      <td>smart</td>\n",
+       "      <td>fortwo coupe</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Auto(AM5)</td>\n",
+       "      <td>Rear-Wheel Drive</td>\n",
+       "      <td>Two Seaters</td>\n",
+       "      <td>Premium</td>\n",
+       "      <td>9.155833</td>\n",
+       "      <td>34</td>\n",
+       "      <td>38</td>\n",
+       "      <td>36</td>\n",
+       "      <td>244.000000</td>\n",
+       "      <td>1100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35950</th>\n",
+       "      <td>smart</td>\n",
+       "      <td>fortwo coupe</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Auto(AM6)</td>\n",
+       "      <td>Rear-Wheel Drive</td>\n",
+       "      <td>Two Seaters</td>\n",
+       "      <td>Premium</td>\n",
+       "      <td>9.155833</td>\n",
+       "      <td>34</td>\n",
+       "      <td>39</td>\n",
+       "      <td>36</td>\n",
+       "      <td>246.000000</td>\n",
+       "      <td>1100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35951</th>\n",
+       "      <td>smart</td>\n",
+       "      <td>fortwo coupe</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Manual 5-spd</td>\n",
+       "      <td>Rear-Wheel Drive</td>\n",
+       "      <td>Two Seaters</td>\n",
+       "      <td>Premium</td>\n",
+       "      <td>9.417429</td>\n",
+       "      <td>32</td>\n",
+       "      <td>39</td>\n",
+       "      <td>35</td>\n",
+       "      <td>255.000000</td>\n",
+       "      <td>1150</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>35952 rows × 15 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Make                Model  Year  Engine Displacement  \\\n",
+       "0            AM General    DJ Po Vehicle 2WD  1984                  2.5   \n",
+       "1            AM General     FJ8c Post Office  1984                  4.2   \n",
+       "2            AM General  Post Office DJ5 2WD  1985                  2.5   \n",
+       "3            AM General  Post Office DJ8 2WD  1985                  4.2   \n",
+       "4      ASC Incorporated                  GNX  1987                  3.8   \n",
+       "...                 ...                  ...   ...                  ...   \n",
+       "35947             smart         fortwo coupe  2013                  1.0   \n",
+       "35948             smart         fortwo coupe  2014                  1.0   \n",
+       "35949             smart         fortwo coupe  2015                  1.0   \n",
+       "35950             smart         fortwo coupe  2016                  0.9   \n",
+       "35951             smart         fortwo coupe  2016                  0.9   \n",
+       "\n",
+       "       Cylinders     Transmission        Drivetrain  \\\n",
+       "0            4.0  Automatic 3-spd     2-Wheel Drive   \n",
+       "1            6.0  Automatic 3-spd     2-Wheel Drive   \n",
+       "2            4.0  Automatic 3-spd  Rear-Wheel Drive   \n",
+       "3            6.0  Automatic 3-spd  Rear-Wheel Drive   \n",
+       "4            6.0  Automatic 4-spd  Rear-Wheel Drive   \n",
+       "...          ...              ...               ...   \n",
+       "35947        3.0        Auto(AM5)  Rear-Wheel Drive   \n",
+       "35948        3.0        Auto(AM5)  Rear-Wheel Drive   \n",
+       "35949        3.0        Auto(AM5)  Rear-Wheel Drive   \n",
+       "35950        3.0        Auto(AM6)  Rear-Wheel Drive   \n",
+       "35951        3.0     Manual 5-spd  Rear-Wheel Drive   \n",
+       "\n",
+       "                     Vehicle Class Fuel Type  Fuel Barrels/Year  City MPG  \\\n",
+       "0      Special Purpose Vehicle 2WD   Regular          19.388824        18   \n",
+       "1      Special Purpose Vehicle 2WD   Regular          25.354615        13   \n",
+       "2      Special Purpose Vehicle 2WD   Regular          20.600625        16   \n",
+       "3      Special Purpose Vehicle 2WD   Regular          25.354615        13   \n",
+       "4                     Midsize Cars   Premium          20.600625        14   \n",
+       "...                            ...       ...                ...       ...   \n",
+       "35947                  Two Seaters   Premium           9.155833        34   \n",
+       "35948                  Two Seaters   Premium           9.155833        34   \n",
+       "35949                  Two Seaters   Premium           9.155833        34   \n",
+       "35950                  Two Seaters   Premium           9.155833        34   \n",
+       "35951                  Two Seaters   Premium           9.417429        32   \n",
+       "\n",
+       "       Highway MPG  Combined MPG  CO2 Emission Grams/Mile  Fuel Cost/Year  \n",
+       "0               17            17               522.764706            1950  \n",
+       "1               13            13               683.615385            2550  \n",
+       "2               17            16               555.437500            2100  \n",
+       "3               13            13               683.615385            2550  \n",
+       "4               21            16               555.437500            2550  \n",
+       "...            ...           ...                      ...             ...  \n",
+       "35947           38            36               244.000000            1100  \n",
+       "35948           38            36               243.000000            1100  \n",
+       "35949           38            36               244.000000            1100  \n",
+       "35950           39            36               246.000000            1100  \n",
+       "35951           39            35               255.000000            1150  \n",
+       "\n",
+       "[35952 rows x 15 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "vehicles = pd.read_csv('vehicles.csv')\n",
+    "vehicles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: ylabel='Count'>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(vehicles['Fuel Barrels/Year'].values,bins=50)"
    ]
   },
   {
@@ -174,11 +625,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: ylabel='Count'>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "sns.histplot(vehicles['CO2 Emission Grams/Mile'].values,bins=50)"
    ]
   },
   {
@@ -190,11 +662,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: ylabel='Count'>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "sns.histplot(vehicles['Combined MPG'].values,bins=50)"
    ]
   },
   {
@@ -206,11 +699,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# you answer here:\n"
+    "# combined mpg\n"
    ]
   },
   {
@@ -237,11 +730,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "expon_dist = np.random.exponential(1, 10000) \n",
+    "plt.hist(expon_dist, 100, density = True) \n",
+    "plt.show()\n",
+    "\n",
+    "expon_dist = np.random.exponential(100, 10000) \n",
+    "plt.hist(expon_dist, 100, density = True) \n",
+    "plt.show()"
    ]
   },
   {
@@ -253,7 +773,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -273,12 +793,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7768698398515702"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your answer here\n",
-    "# Hint: This is same as saying P(x<15)\n"
+    "lambda_inv = 10\n",
+    "expon_dist = expon(scale = lambda_inv)\n",
+    "\n",
+    "expon_dist.cdf(15)"
    ]
   },
   {
@@ -290,17 +823,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.2231301601484298"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your answer here\n"
+    "1 - expon_dist.cdf(15)"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -314,7 +858,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,
diff --git a/your-code/discrete.ipynb b/your-code/discrete.ipynb
index 310a3c6..633df2a 100644
--- a/your-code/discrete.ipynb
+++ b/your-code/discrete.ipynb
@@ -17,6 +17,19 @@
     "- Round the final answer to three decimal places."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from scipy.stats import bernoulli, binom, geom, poisson, uniform, expon, norm"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -33,17 +46,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
+    "\n",
     "\"\"\"\n",
     "Calculate:\n",
     "p = probability that the fruit is an apple \n",
     "q = probability that the fruit is an orange\n",
     "\"\"\"\n",
+    "#1. \n",
+    "p = 0.6\n",
+    "bernoulli_dist = bernoulli(p)\n",
     "\n",
-    "# your code here\n"
+    "p = bernoulli_dist.pmf(1)\n",
+    "\n",
+    "#1.\n",
+    "\n",
+    "q = bernoulli_dist.pmf(0)"
    ]
   },
   {
@@ -61,11 +82,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 73,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.07775999999999998\n",
+      "8.349416423424006e-08\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "\n",
+    "#1.\n",
+    "p=0.6\n",
+    "n = 5\n",
+    "binomial_dist = binom(n,p)\n",
+    "\n",
+    "apples_5 = binomial_dist.pmf(n)\n",
+    "print(apples_5)\n",
+    "#2.\n",
+    "\n",
+    "a5_o15 = (0.6**5) * (0.4**15)\n",
+    "\n",
+    "print(a5_o15)"
    ]
   },
   {
@@ -83,11 +125,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 77,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0012944935222876587"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "p = 0.6\n",
+    "n = 20\n",
+    "\n",
+    "binomial_dist = binom(n,p)\n",
+    "\n",
+    "binomial_dist.pmf(5)"
    ]
   },
   {
@@ -101,11 +159,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 68,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6171726543871046"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "p = 0.25\n",
+    "n = 20\n",
+    "binomial_dist = binom(n,p)\n",
+    "\n",
+    "binomial_dist.cdf(5)"
    ]
   },
   {
@@ -119,12 +192,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 78,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1381dbb50>]"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here\n",
-    "# Please label the axes and give a title to the plot\n"
+    "x = np.arange(1,20)\n",
+    "\n",
+    "plt.plot(x, binomial_dist.cdf(x), 'o')\n",
+    "plt.plot(x, binomial_dist.pmf(x), 'o')"
    ]
   },
   {
@@ -151,11 +247,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 80,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.053775025581946814"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "mu = 2.3\n",
+    "poisson_dist = poisson(mu)\n",
+    "\n",
+    "poisson_dist.pmf(5)"
    ]
   },
   {
@@ -167,18 +277,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 82,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x138380580>]"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here\n",
-    "# Please label the axes and give a title to the plot \n"
+    "x = np.arange(0,10)\n",
+    "\n",
+    "plt.plot(x,poisson_dist.pmf(x), 'o')\n",
+    "plt.plot(x,poisson_dist.cdf(x), 'o')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -192,7 +332,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,