diff --git a/your-code/continuous.ipynb b/your-code/continuous.ipynb
index 32218e9..aaeae1e 100644
--- a/your-code/continuous.ipynb
+++ b/your-code/continuous.ipynb
@@ -35,9 +35,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2.69274251 2.48232246 2.41319903 2.04824595 2.98565967 2.98658439\n",
+      " 2.70705101 2.84253839 2.67005187 2.84780364]\n"
+     ]
+    }
+   ],
    "source": [
     "from scipy.stats import uniform\n",
     "x = uniform.rvs(size=10)\n",
@@ -73,11 +82,80 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here\n"
+    "def generate_uniform_random(bottom, ceiling, count):\n",
+    "    x = uniform.rvs(size=count)\n",
+    "    randoms = bottom + (ceiling - bottom) * x\n",
+    "    return randoms\n",
+    "\n",
+    "data1 = generate_uniform_random(10, 15, 100)\n",
+    "data2 = generate_uniform_random(10, 60, 1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt \n",
+    "plt.hist(data1, bins=10)\n",
+    "plt.xlabel('Value')\n",
+    "plt.ylabel('Count')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(data2, bins=10)\n",
+    "plt.xlabel('Value')\n",
+    "plt.ylabel('Count')\n",
+    "plt.legend()\n",
+    "plt.show()"
    ]
   },
   {
@@ -89,11 +167,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'The second plot with the data2 has much higher number of counts for each value. The data1 plot has a lot of values with lower number of count.'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your answer here:\n"
+    "# your answer here:\n",
+    "\"The second plot with the data2 has much higher number of counts for each value. The data1 plot has a lot of values with lower number of count.\""
    ]
   },
   {
@@ -114,11 +204,527 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here - from class\n",
+    "from scipy.stats import norm\n",
+    "norm.cdf(0, 0, 1)\n",
+    "0.5\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[11.14443724 12.02902365  9.18315829  9.28861757 12.86618164  9.53146878\n",
+      " 11.56015101  9.78587689  8.72804692 10.01859656  9.68707736 10.53238759\n",
+      " 11.46213074  9.39358353  9.80951797  9.41907936  9.79236027 10.33343334\n",
+      "  8.71227337  9.11753652 10.67171565  9.74420964 10.07541195 10.96031917\n",
+      "  9.41146198 10.40485496 10.98259327 12.15667921  8.01284871  8.33074915\n",
+      "  9.33278218  9.50788809 11.27255074  9.68530753  9.35217831  9.82432947\n",
+      "  9.31977932 10.1423968  10.71307179 11.08838377 10.03720157 10.14035549\n",
+      " 11.09711029 10.05106014 10.98153031  9.65731018  9.94552148 10.67877696\n",
+      " 11.05557757  7.84849755 10.63458598  9.27700204 10.13000154  9.88679749\n",
+      " 10.54794826  8.44914163 10.93582392 10.22050426  9.2279012  10.47855807\n",
+      "  9.86291701 11.32602181  7.32968846 11.12009272 11.1741474   7.76461934\n",
+      " 10.13390771 10.4597214   9.5077581  10.07289448  9.76050973  7.98238261\n",
+      " 11.74434135 11.87365824  8.71441015  8.44869847 10.62888463  9.62512554\n",
+      " 10.62453202  9.15033628 10.33262075 11.80220467 10.21788568  8.47572885\n",
+      "  8.92099035  8.07977629 10.01376113  8.78467259 12.25164464  9.63101538\n",
+      "  9.49378624  8.07801387  9.80537374  9.64446101  8.27246872  8.86465921\n",
+      " 11.58569547  9.79420475  9.99092149 10.01621404  8.62408974  9.80906107\n",
+      "  8.32718306  9.17736083  9.17129588  9.76327087  9.20047706 11.84805645\n",
+      "  9.89196437  9.97741607  9.97697831  7.73454325 10.51338356 10.11528196\n",
+      "  9.90383747  9.01219412  9.52059693 10.8389413  11.07801397 10.8960325\n",
+      "  9.66644777  9.68683959 11.50118735  9.34037476  8.89477838 10.92028932\n",
+      "  9.64875367  9.60569853 11.28919513 10.02180615  8.65093008 11.4517677\n",
+      "  8.92349173 11.16291364 11.06781689 10.44796939 11.68981809  9.71396337\n",
+      "  9.42347209 11.1062378   8.41068233  9.51972273 10.39062468  9.0843881\n",
+      " 11.10804694 11.00115194 12.35107884  9.02109273  9.94685516  9.89562428\n",
+      " 10.31073555  9.71702118 10.87855243 10.53700214 10.15318145 11.03154567\n",
+      "  9.98543349 11.32580493 11.34572217  8.90982874  9.64115687 10.18983484\n",
+      "  9.32642433  9.87348275 10.50176187  8.70759452  9.97858405  9.08210836\n",
+      " 10.12604197 11.22603042  9.91436623  9.17086313  8.45737676 10.12415192\n",
+      " 10.15919806  9.83734331  8.69931134 11.18764899 10.00426512  9.24752757\n",
+      "  9.46968537 10.63215978  9.19357408 10.5138916   7.47901672 10.5259628\n",
+      " 10.54683747  9.93659924 10.1155251  10.98030594  8.68264448 11.83025683\n",
+      "  9.03059226 10.49401617  9.43952116 10.33701534  9.81197566 12.16552569\n",
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+      " -5.30504192e+01  7.46184553e+01  3.80834205e+01  5.33856337e+01\n",
+      "  6.40202685e+01  1.22988092e+02  3.39510940e+01  1.15436127e+02\n",
+      "  7.44232773e+01 -1.03248916e+01  1.30421316e+02 -3.79048717e+00\n",
+      "  6.22173361e+01  2.33453946e+01 -4.98350337e+01  5.85106567e+01\n",
+      "  5.58750289e+01 -4.88072726e+01  6.08899340e+00  1.47660569e+01\n",
+      "  4.43913825e+00 -6.08127036e+01  1.02347424e+02 -1.71211246e+01\n",
+      "  2.68357406e+01  3.44017446e+01  1.08685162e+02  6.18634544e+01\n",
+      "  1.73183840e+01  2.01532495e+00 -2.95292625e+01  6.79321899e+01\n",
+      " -3.69628252e+01 -1.32866559e+02  1.61877362e+01 -8.04997802e-01\n",
+      " -1.29559498e+01 -3.32072558e+01 -4.44802983e+01  3.13472285e+01\n",
+      " -1.24554231e+02 -4.75135321e-01  7.73879450e+01 -1.05767006e+01\n",
+      "  2.96201693e+01  6.65785054e+01  3.37089807e+01 -7.63854613e+00\n",
+      "  9.65177469e+00  6.19299533e+01  4.06236695e+01 -2.68685043e+01\n",
+      " -9.08554240e-01  7.19923689e+01 -6.08035129e+01  5.69626746e+01\n",
+      "  1.32666507e+01  4.73541940e+01 -3.80558532e+00  2.51978387e+01\n",
+      " -9.15842148e+01  8.13079779e-01  5.94960974e+01  6.22116415e-01\n",
+      "  3.67051321e+01 -3.62099824e+01  3.56012371e+01  7.91185815e+01\n",
+      " -3.05175002e+01 -5.51809947e+00  5.51420220e+01 -1.65541463e+01\n",
+      " -2.42893928e+00  2.35213427e+01 -3.66678324e+01  3.02428311e+01\n",
+      " -6.46266931e+01  4.96846260e+01 -5.04497819e+00  4.06113800e+01\n",
+      " -6.13946608e+01  4.14204391e+01  3.89679139e+00  7.48271657e+00\n",
+      " -3.30235590e+01  1.35396329e+01  2.70836726e+01  3.75704224e+01\n",
+      " -2.45103970e+01  9.39990839e+01  1.26416207e+02 -1.78755044e+01\n",
+      " -2.72641081e+01  3.50460896e+01  6.05057026e+01 -7.78286151e+00\n",
+      "  5.24454346e+01  6.83897510e+01 -3.41193328e+01 -4.69263050e+01]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def generate_normal_random(size, mean, standard_dev):\n",
+    "    randoms = norm.rvs(loc=mean, scale=standard_dev, size=size)\n",
+    "    return randoms\n",
+    "\n",
+    "data1 = generate_normal_random(1000, 10, 1)\n",
+    "data2 = generate_normal_random(1000, 10, 50)\n",
+    "print(data1)\n",
+    "print(data2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(data1, bins=60)\n",
+    "plt.xlabel('Value')\n",
+    "plt.ylabel('Count')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "plt.hist(data2, bins=60)\n",
+    "plt.xlabel('Value')\n",
+    "plt.ylabel('Count')\n",
+    "plt.legend()\n",
+    "plt.show()"
    ]
   },
   {
@@ -130,7 +736,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -146,6 +752,185 @@
     "In this challenge we are going to take a look the real data. We will use vehicles.csv file for this exercise"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "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",
+       "  </tbody>\n",
+       "</table>\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",
+       "   Cylinders     Transmission        Drivetrain                Vehicle Class  \\\n",
+       "0        4.0  Automatic 3-spd     2-Wheel Drive  Special Purpose Vehicle 2WD   \n",
+       "1        6.0  Automatic 3-spd     2-Wheel Drive  Special Purpose Vehicle 2WD   \n",
+       "2        4.0  Automatic 3-spd  Rear-Wheel Drive  Special Purpose Vehicle 2WD   \n",
+       "3        6.0  Automatic 3-spd  Rear-Wheel Drive  Special Purpose Vehicle 2WD   \n",
+       "4        6.0  Automatic 4-spd  Rear-Wheel Drive                 Midsize Cars   \n",
+       "\n",
+       "  Fuel Type  Fuel Barrels/Year  City MPG  Highway MPG  Combined MPG  \\\n",
+       "0   Regular          19.388824        18           17            17   \n",
+       "1   Regular          25.354615        13           13            13   \n",
+       "2   Regular          20.600625        16           17            16   \n",
+       "3   Regular          25.354615        13           13            13   \n",
+       "4   Premium          20.600625        14           21            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  \n",
+       "3               683.615385            2550  \n",
+       "4               555.437500            2550  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "df = pd.read_csv(\"vehicles.csv\")\n",
+    "df.head()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -158,11 +943,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "# your code here\n",
+    "data = df[\"Fuel Barrels/Year\"]\n",
+    "plt.hist(data, bins=60)\n",
+    "plt.xlabel('Fuel Barrels/Year')\n",
+    "plt.ylabel('Count')\n",
+    "plt.legend()\n",
+    "plt.show()"
    ]
   },
   {
@@ -174,11 +983,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "# your code here\n",
+    "data = df[\"CO2 Emission Grams/Mile\"]\n",
+    "plt.hist(data, bins=60)\n",
+    "plt.xlabel('CO2 Emission Grams/Mile')\n",
+    "plt.ylabel('Count')\n",
+    "plt.legend()\n",
+    "plt.show()"
    ]
   },
   {
@@ -190,11 +1023,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "# your code here\n",
+    "data = df[\"Combined MPG\"]\n",
+    "plt.hist(data, bins=60)\n",
+    "plt.xlabel('Combined MPG')\n",
+    "plt.ylabel('Count')\n",
+    "plt.legend()\n",
+    "plt.show()"
    ]
   },
   {
@@ -206,11 +1063,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'I would say the last plot, where we use Combined MPG, because it appears to have more distributed values, looking more like a normal distribution function.'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# you answer here:\n"
+    "# you answer here:\n",
+    "\"I would say the last plot, where we use Combined MPG, because it appears to have more distributed values, looking more like a normal distribution function.\""
    ]
   },
   {
@@ -237,11 +1106,598 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here\n"
+    "from scipy.stats import expon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.37842761e+00 1.10754427e-01 4.25483509e+00 1.18580318e+00\n",
+      " 9.62782160e-01 1.73382432e+00 3.90477962e-01 1.32162092e+00\n",
+      " 1.27034498e-01 1.89786516e+00 2.29075615e-01 1.49183241e-01\n",
+      " 1.93422158e+00 1.33796425e+00 2.06673781e+00 3.79782990e-01\n",
+      " 4.87776009e-01 1.86628919e+00 1.49693605e-01 6.52533964e-01\n",
+      " 1.60616446e-01 1.28618842e+00 2.64236923e-01 5.33040220e-01\n",
+      " 1.42467767e-01 4.35320309e-01 1.26258273e-01 3.51891922e+00\n",
+      " 6.49243494e-02 7.13981364e-01 3.86896973e-01 2.59915566e+00\n",
+      " 1.03365195e-01 9.03760695e-03 2.53058360e+00 5.55152959e-01\n",
+      " 2.00861105e+00 7.69163204e-01 9.22433794e-01 6.26719396e-01\n",
+      " 5.91484140e-02 5.77034073e-01 2.93064389e-01 3.39484093e-01\n",
+      " 3.57900540e-01 1.16954604e+00 6.47616353e-01 1.47333677e-01\n",
+      " 1.66398316e+00 4.33447318e-01 3.56137816e-01 4.26784843e-02\n",
+      " 1.59865065e+00 3.33990275e+00 1.64684952e+00 4.28265406e-01\n",
+      " 2.57156567e+00 3.46000549e+00 1.83549520e+00 2.37217806e+00\n",
+      " 1.60688598e+00 1.85222212e-01 2.83257602e-01 1.50308525e+00\n",
+      " 5.60341240e+00 1.08148336e+00 3.67930838e-01 9.86958594e-01\n",
+      " 1.17726594e-01 8.42907117e-01 2.78271828e-01 2.42073186e-02\n",
+      " 9.43046275e-01 4.79262643e+00 1.60215844e+00 3.97987409e-01\n",
+      " 1.56445674e+00 5.99017972e-01 2.42816340e+00 3.22918479e+00\n",
+      " 1.34334011e+00 3.54180376e+00 1.62302116e+00 2.04593129e+00\n",
+      " 2.66866694e-01 7.07611588e-01 2.16822220e-01 4.62957744e-02\n",
+      " 5.33375631e-01 5.26589300e-01 2.00938615e+00 4.21237536e+00\n",
+      " 1.07585453e+00 9.51392282e-01 1.63553803e+00 1.25352617e-01\n",
+      " 1.63629079e+00 4.74368059e-01 1.39838763e-01 6.65910773e-01\n",
+      " 1.53917253e+00 1.92643372e-01 1.75149709e+00 6.93830294e-01\n",
+      " 3.10062720e+00 1.90702072e-01 9.18570098e-01 2.05148715e+00\n",
+      " 5.69696449e-01 1.09701406e+00 6.77462167e-01 4.10642272e+00\n",
+      " 1.54471867e+00 2.18360707e-02 3.22310533e+00 1.71268077e-01\n",
+      " 5.61451539e-01 3.85077827e-03 8.64286781e-01 3.67389880e-02\n",
+      " 8.53370394e-01 4.33231412e-02 7.15143163e-02 3.48858701e-01\n",
+      " 1.73139046e-01 4.25325755e-01 4.67611581e-01 2.49688604e+00\n",
+      " 4.24177330e+00 2.46301793e+00 1.15714267e+00 2.25068263e-01\n",
+      " 1.42567199e-01 4.51882879e-01 1.76347174e-01 5.49713390e-01\n",
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+      " 5.06666279e+01 2.72816992e+02 1.21736429e+02 2.10224959e+02\n",
+      " 1.31985629e+02 3.47852680e+02 3.62392896e+02 3.93934062e+02\n",
+      " 8.59302242e+01 1.39146040e+02 1.27487854e+02 4.55287139e+01\n",
+      " 2.00054528e+02 8.93265349e+01 7.81230192e+01 6.05907004e+00\n",
+      " 1.05597954e+02 1.27576753e+02 1.33549784e+02 2.13510215e+02\n",
+      " 3.57513030e+02 1.17988614e+02 3.02575952e+01 3.28605612e+01\n",
+      " 9.37701218e+00 3.98302614e+01 7.68121041e+01 5.74972952e+01\n",
+      " 5.10452417e+01 5.96653151e+01 4.39818362e+01 2.24548743e+01\n",
+      " 6.50038813e+01 4.11299758e+01 1.51280912e+02 3.56556077e+00\n",
+      " 4.97846881e+01 2.74857230e+01 9.99331491e+01 1.00222399e+02\n",
+      " 3.04615037e+01 1.83367027e+02 3.51795373e+00 2.32325811e+01\n",
+      " 3.00440103e+02 5.97712315e+01 3.88217165e+01 3.29854736e+01\n",
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+      " 5.37459038e+01 3.12892374e+02 2.01337496e+02 7.82030738e+01\n",
+      " 1.44306123e+01 4.84735453e+01 2.55737709e+01 2.34689782e+00\n",
+      " 8.71098564e+01 1.22710520e+02 6.88820747e+01 3.35851676e+01\n",
+      " 8.58278322e+01 2.02375152e+00 5.17336318e+01 7.58189617e+00\n",
+      " 1.00286823e+00 2.12363928e+00 5.18504467e+01 1.65118935e+02\n",
+      " 2.59936525e+02 1.82056111e+02 7.95632300e+01 8.23491216e+01\n",
+      " 1.50594258e+02 1.29941170e+02 1.29905650e+02 1.48727349e+01\n",
+      " 7.97030425e+01 2.19183957e+02 2.28856973e+01 1.94537055e+01\n",
+      " 6.12277828e+01 2.52038182e+02 1.16492368e+02 2.43920101e+02\n",
+      " 4.21051690e+02 8.15994242e+01 5.49761512e+01 1.92012702e+02\n",
+      " 9.13555350e+01 1.74922254e+02 1.70012694e+02 1.17978772e+02\n",
+      " 1.00540606e+01 3.58717817e+01 1.03912551e+00 4.20348005e+01\n",
+      " 1.27461145e+02 2.03583816e+01 1.27426788e+01 1.63557129e+02\n",
+      " 6.50144499e+01 6.37784287e+01 1.15960566e+02 6.98271726e+01\n",
+      " 8.37727934e+01 2.19006392e+02 1.48449165e+01 5.95123858e+01\n",
+      " 9.70556461e+01 5.54583582e+01 1.79371653e+01 1.68595096e+02\n",
+      " 1.81252849e+02 2.20916717e+01 1.48665772e+02 1.77916898e+02\n",
+      " 2.07511110e+01 5.53530272e+00 1.83178234e+02 6.84571930e+01\n",
+      " 7.86550163e+01 1.10069770e+01 1.86731557e+02 5.70666412e+01\n",
+      " 1.19131171e+01 8.52936479e+01 5.11016030e+01 4.72461177e+01\n",
+      " 2.33527362e+01 1.02042703e+02 1.23934560e+02 1.74268512e+02\n",
+      " 2.07043073e+02 9.41245768e+01 3.05533362e+02 4.41240173e+01\n",
+      " 1.05695089e+01 9.84456123e+01 9.16356357e+00 1.75717088e+02\n",
+      " 2.56602779e+02 1.58157864e+00 6.39162990e+01 3.23556044e+01\n",
+      " 4.21513969e+02 2.22868055e+02 8.30890167e+00 2.10825347e+01\n",
+      " 1.31522113e+02 7.08755389e+01 2.38029238e+01 7.51378868e+01\n",
+      " 1.06685538e+02 1.63328490e+02 2.22717863e+02 1.40253553e+01\n",
+      " 3.77279891e+02 2.68887875e+02 5.60724144e+01 1.37135126e+02\n",
+      " 7.48504645e+01 8.19590481e+01 4.69256224e+01 9.22107629e+01\n",
+      " 1.25007997e+02 3.40635222e+01 3.65299290e+01 1.96950903e+02\n",
+      " 3.11638266e+02 1.07804602e+02 5.13700222e+01 2.19234710e+02]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def generate_expon_random(size,mean):\n",
+    "    lambda_inv = mean \n",
+    "    expon_dist = expon(scale=lambda_inv)\n",
+    "    randoms = expon_dist.rvs(size)\n",
+    "    return randoms\n",
+    "\n",
+    "data1 = generate_expon_random(1000,1)\n",
+    "data2 = generate_expon_random(1000, 100)\n",
+    "print(data1)\n",
+    "print(data2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(data1, bins=60)\n",
+    "plt.xlabel('Value')\n",
+    "plt.ylabel('Count')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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iIqJLagQAAMHNYYwxgS4i0Dwej6KiolRTU6PIyEi/HjtYr6s5tnpGoEsAAKBDvuvv73at7EyaNKnN01Vvv/12ew4LAADgd+0KO+eu1znn7Nmz2r9/vw4dOtTiBaEAAACB1K6w8+yzz16wPTMzU2fOnOlQQQAAAP7UrtdFtOa+++7z23uxAAAA/MGvYee9997TZZdd5s9DAgAAdEi7TmOde8P4OcYYnTp1Sh988EGnP1UZAADgUrQr7ERFRfl87tWrl6655ho9+eSTPm8gBwAACLR2hZ38/Hx/1wEAANAp2hV2zikrK9Phw4flcDg0cuRI7/upAAAAgkW7wk5VVZXuvvtu7dy5U/369ZMxRjU1NZo0aZI2b96sK664wt91AgAAtEu77sZasmSJPB6PysvL9cUXX6i6ulqHDh2Sx+PRww8/7O8aAQAA2q1dKztvvvmmtm/frhEjRnjbRo4cqXXr1nGBMgAACCrtWtlpbm5WaGhoi/bQ0FA1Nzd3uCgAAAB/aVfYufXWW7V06VJ99tln3raTJ0/qpz/9qSZPnuy34gAAADqqXWEnLy9PtbW1SkhI0FVXXaWrr75aiYmJqq2t1XPPPefvGgEAANqtXdfsxMfHa+/evSouLtbf/vY3GWM0cuRITZkyxd/1oZMkrHi9zf3HVs/ookoAAOhcl7Sy8/bbb2vkyJHyeDySpKlTp2rJkiV6+OGHdcMNN2jUqFHavXt3pxQKAADQHpcUdnJzc/XjH/9YkZGRLfZFRUVp/vz5ysnJ8VtxAAAAHXVJp7H++te/as2aNa3uT0tL0y9/+csOF4XAa+s0F6e4AADdySWt7Pzv//7vBW85PyckJET/93//1+GiAAAA/OWSws6VV16pgwcPtrr/wIEDio2N7XBRAAAA/nJJYecHP/iBfvGLX+irr75qsa++vl6rVq3SzJkz/VYcAABAR13SNTs///nP9eqrr+p73/ueFi9erGuuuUYOh0OHDx/WunXr1NTUpJUrV3ZWrQAAAJfsksJOTEyMSktL9ZOf/EQZGRkyxkiSHA6Hpk2bpueff14xMTGdUigAAEB7XPJDBYcOHao33nhD1dXV+vjjj2WMUVJSkvr3798Z9QEAAHRIu56gLEn9+/fXDTfc4M9a0E3w9GUAQHfSrndjAQAAdBeEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYrd0vAgU6Q1svGeUFowCA9mBlBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGC1bhV2srOz5XA4lJ6e7m0zxigzM1NxcXEKCwvTxIkTVV5eHrgiAQBAUOk2YWfPnj3asGGDrr32Wp/2tWvXKicnR3l5edqzZ49cLpemTp2q2traAFUKAACCSbcIO2fOnNG9996rjRs3qn///t52Y4xyc3O1cuVKzZo1S8nJySooKFBdXZ0KCwtbPV5DQ4M8Ho/PBgAA7NQtws6iRYs0Y8YMTZkyxae9oqJCbrdbaWlp3jan06kJEyaotLS01eNlZ2crKirKu8XHx3da7QAAILCCPuxs3rxZe/fuVXZ2dot9brdbkhQTE+PTHhMT4913IRkZGaqpqfFulZWV/i0aAAAEjZBAF9CWyspKLV26VEVFRbrsssta7edwOHw+G2NatJ3P6XTK6XT6rU4AABC8gnplp6ysTFVVVUpJSVFISIhCQkJUUlKiX/3qVwoJCfGu6Hx7FaeqqqrFag8AAOiZgnplZ/LkyTp48KBP27/9279p+PDhevTRRzVs2DC5XC4VFxdr7NixkqTGxkaVlJRozZo1gSgZF5Gw4vVAlwAA6GGCOuxEREQoOTnZp61v374aOHCgtz09PV1ZWVlKSkpSUlKSsrKyFB4ertmzZweiZAAAEGSCOux8F8uXL1d9fb0WLlyo6upqjR8/XkVFRYqIiAh0aQAAIAh0u7Czc+dOn88Oh0OZmZnKzMwMSD0AACC4BfUFygAAAB1F2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWCwl0AbBPworXA3LcY6tndMrPBQB0b6zsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjVvP0SNw2zoA9Fys7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYLWQQBfQluzsbL366qv629/+prCwMKWmpmrNmjW65pprvH2MMXriiSe0YcMGVVdXa/z48Vq3bp1GjRoVwMrR3SSseL3d3z22eoYfKwEA+FtQr+yUlJRo0aJF+vOf/6zi4mJ9/fXXSktL05dffunts3btWuXk5CgvL0979uyRy+XS1KlTVVtbG8DKAQBAsAjqlZ0333zT53N+fr6io6NVVlamW265RcYY5ebmauXKlZo1a5YkqaCgQDExMSosLNT8+fMDUTYAAAgiQb2y8201NTWSpAEDBkiSKioq5Ha7lZaW5u3jdDo1YcIElZaWtnqchoYGeTwenw0AANgpqFd2zmeM0bJly3TzzTcrOTlZkuR2uyVJMTExPn1jYmJ0/PjxVo+VnZ2tJ554ovOKBb6jtq4V4logAPCPbrOys3jxYh04cEC/+93vWuxzOBw+n40xLdrOl5GRoZqaGu9WWVnp93oBAEBw6BYrO0uWLNG2bdu0a9cuDR482NvucrkkfbPCExsb622vqqpqsdpzPqfTKafT2XkFAwCAoBHUYccYoyVLlmjr1q3auXOnEhMTffYnJibK5XKpuLhYY8eOlSQ1NjaqpKREa9asCUTJgI+O3NIOAPCPoA47ixYtUmFhof7whz8oIiLCe41OVFSUwsLC5HA4lJ6erqysLCUlJSkpKUlZWVkKDw/X7NmzA1w9AAAIBkEddtavXy9Jmjhxok97fn6+HnzwQUnS8uXLVV9fr4ULF3ofKlhUVKSIiIgurhYAAAQjhzHGBLqIQPN4PIqKilJNTY0iIyP9emxOY9ivrbumOvPPn7u1APR03/X3d7e5GwsAAKA9CDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsFpQv/Uc6A542SsABDdWdgAAgNUIOwAAwGqcxgIs1JFTa8dWz/BjJQAQeKzsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjVvPgW4qGJ/cfLGauK0dQCCwsgMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArMYTlAF0mbaesMzTlQF0FlZ2AACA1Qg7AADAapzGAoA2cOoN6P5Y2QEAAFYj7AAAAKtxGgtAUGjrdJHEKSMA7cfKDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwmsMYYwJdRKB5PB5FRUWppqZGkZGRfj32xZ4KC6DjLvZ05UD9f9iRpz7zRGng4r7r729WdgAAgNUIOwAAwGq8CBRAt8fpYl8dOQUWqO8Gq7bG1B3H01OxsgMAAKxG2AEAAFaz5jTW888/r6efflqnTp3SqFGjlJubq+9///uBLgtAD9aZp9dsO3XXkfEE6nRSZ9bcWafPOvNUYzCf8rNiZWfLli1KT0/XypUrtW/fPn3/+9/X9OnTdeLEiUCXBgAAAsyKsJOTk6N58+bpoYce0ogRI5Sbm6v4+HitX78+0KUBAIAA6/ansRobG1VWVqYVK1b4tKelpam0tPSC32loaFBDQ4P3c01NjaRvHk7kb80NdX4/JgB0RFv/1l3s36xAfbe9x+2ozvo3/GI1t/VzOzLejvwZdeTYnfVndO64F30+sunmTp48aSSZ//mf//Fpf+qpp8z3vve9C35n1apVRhIbGxsbGxubBVtlZWWbWaHbr+yc43A4fD4bY1q0nZORkaFly5Z5Pzc3N+uLL77QwIEDW/1Oe3g8HsXHx6uystLvr6HoLpgD5qCnj19iDiTmoKePX+qcOTDGqLa2VnFxcW326/ZhZ9CgQerdu7fcbrdPe1VVlWJiYi74HafTKafT6dPWr1+/zipRkZGRPfYv9znMAXPQ08cvMQcSc9DTxy/5fw6ioqIu2qfbX6Dcp08fpaSkqLi42Ke9uLhYqampAaoKAAAEi26/siNJy5Yt0/33369x48bpxhtv1IYNG3TixAktWLAg0KUBAIAAsyLs3HXXXfr888/15JNP6tSpU0pOTtYbb7yhoUOHBrQup9OpVatWtThl1pMwB8xBTx+/xBxIzEFPH78U2DlwGHOx+7UAAAC6r25/zQ4AAEBbCDsAAMBqhB0AAGA1wg4AALAaYacTPf/880pMTNRll12mlJQU7d69O9Al+cWuXbt02223KS4uTg6HQ6+99prPfmOMMjMzFRcXp7CwME2cOFHl5eU+fRoaGrRkyRINGjRIffv21Q9/+EN9+umnXTiK9svOztYNN9ygiIgIRUdH64477tCRI0d8+tg+B+vXr9e1117rfTjYjTfeqD/96U/e/baP/9uys7PlcDiUnp7ubesJc5CZmSmHw+GzuVwu7/6eMAcnT57Ufffdp4EDByo8PFxjxoxRWVmZd7/tc5CQkNDi74DD4dCiRYskBdH4O/ZmKrRm8+bNJjQ01GzcuNF8+OGHZunSpaZv377m+PHjgS6tw9544w2zcuVK88orrxhJZuvWrT77V69ebSIiIswrr7xiDh48aO666y4TGxtrPB6Pt8+CBQvMlVdeaYqLi83evXvNpEmTzHXXXWe+/vrrLh7NpZs2bZrJz883hw4dMvv37zczZswwQ4YMMWfOnPH2sX0Otm3bZl5//XVz5MgRc+TIEfPYY4+Z0NBQc+jQIWOM/eM/3/vvv28SEhLMtddea5YuXept7wlzsGrVKjNq1Chz6tQp71ZVVeXdb/scfPHFF2bo0KHmwQcfNH/5y19MRUWF2b59u/n444+9fWyfg6qqKp8//+LiYiPJvPPOO8aY4Bk/YaeT/NM//ZNZsGCBT9vw4cPNihUrAlRR5/h22GlubjYul8usXr3a2/bVV1+ZqKgo88ILLxhjjDl9+rQJDQ01mzdv9vY5efKk6dWrl3nzzTe7rHZ/qaqqMpJMSUmJMaZnzoExxvTv39/8+te/7lHjr62tNUlJSaa4uNhMmDDBG3Z6yhysWrXKXHfddRfc1xPm4NFHHzU333xzq/t7whx829KlS81VV11lmpubg2r8nMbqBI2NjSorK1NaWppPe1pamkpLSwNUVdeoqKiQ2+32GbvT6dSECRO8Yy8rK9PZs2d9+sTFxSk5Oblbzk9NTY0kacCAAZJ63hw0NTVp8+bN+vLLL3XjjTf2qPEvWrRIM2bM0JQpU3zae9IcHD16VHFxcUpMTNTdd9+tTz75RFLPmINt27Zp3Lhx+tGPfqTo6GiNHTtWGzdu9O7vCXNwvsbGRr388suaO3euHA5HUI2fsNMJ/vGPf6ipqanFi0hjYmJavLDUNufG19bY3W63+vTpo/79+7fap7swxmjZsmW6+eablZycLKnnzMHBgwd1+eWXy+l0asGCBdq6datGjhzZY8a/efNm7d27V9nZ2S329ZQ5GD9+vH7zm9/orbfe0saNG+V2u5WamqrPP/+8R8zBJ598ovXr1yspKUlvvfWWFixYoIcffli/+c1vJPWcvwfnvPbaazp9+rQefPBBScE1fiteFxGsHA6Hz2djTIs2W7Vn7N1xfhYvXqwDBw7o3XffbbHP9jm45pprtH//fp0+fVqvvPKK5syZo5KSEu9+m8dfWVmppUuXqqioSJdddlmr/WyeA0maPn26979Hjx6tG2+8UVdddZUKCgr0z//8z5LsnoPm5maNGzdOWVlZkqSxY8eqvLxc69ev1wMPPODtZ/McnO/FF1/U9OnTFRcX59MeDONnZacTDBo0SL17926RSquqqlokXNucuxOjrbG7XC41Njaqurq61T7dwZIlS7Rt2za98847Gjx4sLe9p8xBnz59dPXVV2vcuHHKzs7Wddddp//8z//sEeMvKytTVVWVUlJSFBISopCQEJWUlOhXv/qVQkJCvGOweQ4upG/fvho9erSOHj3aI/4exMbGauTIkT5tI0aM0IkTJyT1nH8LJOn48ePavn27HnroIW9bMI2fsNMJ+vTpo5SUFBUXF/u0FxcXKzU1NUBVdY3ExES5XC6fsTc2NqqkpMQ79pSUFIWGhvr0OXXqlA4dOtQt5scYo8WLF+vVV1/V22+/rcTERJ/9PWEOLsQYo4aGhh4x/smTJ+vgwYPav3+/dxs3bpzuvfde7d+/X8OGDbN+Di6koaFBhw8fVmxsbI/4e3DTTTe1eOzERx995H0JdU+Yg3Py8/MVHR2tGTNmeNuCavx+u9QZPs7dev7iiy+aDz/80KSnp5u+ffuaY8eOBbq0DqutrTX79u0z+/btM5JMTk6O2bdvn/e2+tWrV5uoqCjz6quvmoMHD5p77rnngrcaDh482Gzfvt3s3bvX3Hrrrd3mVsuf/OQnJioqyuzcudPnlsu6ujpvH9vnICMjw+zatctUVFSYAwcOmMcee8z06tXLFBUVGWPsH/+FnH83ljE9Yw5+9rOfmZ07d5pPPvnE/PnPfzYzZ840ERER3n/nbJ+D999/34SEhJinnnrKHD161Pz2t7814eHh5uWXX/b2sX0OjDGmqanJDBkyxDz66KMt9gXL+Ak7nWjdunVm6NChpk+fPub666/33prc3b3zzjtGUottzpw5xphvbrdctWqVcblcxul0mltuucUcPHjQ5xj19fVm8eLFZsCAASYsLMzMnDnTnDhxIgCjuXQXGrskk5+f7+1j+xzMnTvX+3f7iiuuMJMnT/YGHWPsH/+FfDvs9IQ5OPfMlNDQUBMXF2dmzZplysvLvft7whz88Y9/NMnJycbpdJrhw4ebDRs2+OzvCXPw1ltvGUnmyJEjLfYFy/gdxhjjv3UiAACA4MI1OwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AKw1ceJEpaenB7oMAAFG2AEQlG677TZNmTLlgvvee+89ORwO7d27t4urAtAdEXYABKV58+bp7bff1vHjx1vse+mllzRmzBhdf/31AagMQHdD2AEQlGbOnKno6Ght2rTJp72urk5btmzRHXfcoXvuuUeDBw9WeHi4Ro8erd/97ndtHtPhcOi1117zaevXr5/Pzzh58qTuuusu9e/fXwMHDtTtt9+uY8eO+WdQAAKCsAMgKIWEhOiBBx7Qpk2bdP77in//+9+rsbFRDz30kFJSUvTf//3fOnTokP793/9d999/v/7yl7+0+2fW1dVp0qRJuvzyy7Vr1y69++67uvzyy/Uv//Ivamxs9MewAAQAYQdA0Jo7d66OHTumnTt3etteeuklzZo1S1deeaUeeeQRjRkzRsOGDdOSJUs0bdo0/f73v2/3z9u8ebN69eqlX//61xo9erRGjBih/Px8nThxwqcGAN1LSKALAIDWDB8+XKmpqXrppZc0adIk/f3vf9fu3btVVFSkpqYmrV69Wlu2bNHJkyfV0NCghoYG9e3bt90/r6ysTB9//LEiIiJ82r/66iv9/e9/7+hwAAQIYQdAUJs3b54WL16sdevWKT8/X0OHDtXkyZP19NNP69lnn1Vubq5Gjx6tvn37Kj09vc3TTQ6Hw+eUmCSdPXvW+9/Nzc1KSUnRb3/72xbfveKKK/w3KABdirADIKjdeeedWrp0qQoLC1VQUKAf//jHcjgc2r17t26//Xbdd999kr4JKkePHtWIESNaPdYVV1yhU6dOeT8fPXpUdXV13s/XX3+9tmzZoujoaEVGRnbeoAB0Ka7ZARDULr/8ct1111167LHH9Nlnn+nBBx+UJF199dUqLi5WaWmpDh8+rPnz58vtdrd5rFtvvVV5eXnau3evPvjgAy1YsEChoaHe/ffee68GDRqk22+/Xbt371ZFRYVKSkq0dOlSffrpp505TACdiLADIOjNmzdP1dXVmjJlioYMGSJJevzxx3X99ddr2rRpmjhxolwul+644442j/PMM88oPj5et9xyi2bPnq1HHnlE4eHh3v3h4eHatWuXhgwZolmzZmnEiBGaO3eu6uvrWekBujGH+fYJbAAAAIuwsgMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAq/0/EfURNn373KsAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(data2, bins=60)\n",
+    "plt.xlabel('Value')\n",
+    "plt.ylabel('Count')\n",
+    "plt.legend()\n",
+    "plt.show()"
    ]
   },
   {
@@ -253,7 +1709,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -273,12 +1729,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7534030360583935"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# your answer here\n",
-    "# Hint: This is same as saying P(x<15)\n"
+    "# Hint: This is same as saying P(x<15)\n",
+    "lambda_inv = 10 \n",
+    "expon_dist = expon(scale=lambda_inv)\n",
+    "expon_dist.cdf(14)"
    ]
   },
   {
@@ -290,17 +1760,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.24659696394160646"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your answer here\n"
+    "# your answer here\n",
+    "1- expon_dist.cdf(14)"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -314,7 +1796,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,
diff --git a/your-code/discrete.ipynb b/your-code/discrete.ipynb
index 310a3c6..d1de6d5 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": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "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 random"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -33,17 +46,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.6\n",
+      "0.4000000000000001\n"
+     ]
+    }
+   ],
    "source": [
     "\"\"\"\n",
     "Calculate:\n",
-    "p = probability that the fruit is an apple \n",
-    "q = probability that the fruit is an orange\n",
+    "p = probability that the fruit is an apple = 60/100 =0.6\n",
+    "q = probability that the fruit is an orange= 40/100 = 0.4\n",
     "\"\"\"\n",
     "\n",
-    "# your code here\n"
+    "# your code here\n",
+    "p= 60/100\n",
+    "bernoulli_dist =bernoulli(p)\n",
+    "print(bernoulli_dist.pmf(1)) #fruit is an apple\n",
+    "print(bernoulli_dist.pmf(0)) #fruit is an orange"
    ]
   },
   {
@@ -61,11 +87,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.07775999999999998\n",
+      "1.073741824000001e-06\n",
+      "8.349416423424006e-08\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "p = 0.6\n",
+    "q = 0.4\n",
+    "n = 20\n",
+    "\n",
+    "bernoulli_dist_apple = bernoulli(p)\n",
+    "bernoulli_dist_orange = bernoulli(q)\n",
+    "\n",
+    "#1.\n",
+    "prob_apples = (bernoulli_dist_apple.pmf(1) ** 5) \n",
+    "print(prob_apples)\n",
+    "#2.\n",
+    "prob_oranges = (bernoulli_dist_orange.pmf(1) ** 15) \n",
+    "print(prob_oranges) #0.000001073741824000001\n",
+    "prob_apples_oranges = prob_apples * prob_oranges\n",
+    "print(prob_apples_oranges) #0.00000008349416423424006"
    ]
   },
   {
@@ -83,11 +133,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0012944935222876583\n",
+      "1.675713479244708e-06\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "# your code here\n",
+    "p = 0.6\n",
+    "q = 0.4\n",
+    "n = 20\n",
+    "\n",
+    "binomial_dist_apple = binom(n, p)\n",
+    "binomial_dist_orange = binom(n, q)\n",
+    "\n",
+    "# 1.\n",
+    "prob_apples = binomial_dist_apple.pmf(5)\n",
+    "print(prob_apples)\n",
+    "\n",
+    "# 2.\n",
+    "prob_oranges = binomial_dist_orange.pmf(15)\n",
+    "prob_combined = prob_apples * prob_oranges\n",
+    "print(prob_combined)"
    ]
   },
   {
@@ -101,11 +175,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 64,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.00031703112116863004"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "# your code here\n",
+    "p = 0.6\n",
+    "n = 20\n",
+    "\n",
+    "binomial_dist_apple = binom(n, p)\n",
+    "prob_less_apples = binomial_dist_apple.cdf(4) #less or 4 \n",
+    "prob_less_apples"
    ]
   },
   {
@@ -119,12 +210,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 65,
    "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\n",
-    "# Please label the axes and give a title to the plot\n"
+    "# Please label the axes and give a title to the plot\n",
+    "x = np.arange(0,n)\n",
+    "plt.plot(x, binomial_dist_apple.pmf(x), \"o\")\n",
+    "plt.xlabel('number of fruits')\n",
+    "plt.ylabel('probability of apples')\n",
+    "plt.title(\"Probability of choosing apples from the total number of fruits in the basket\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.arange(0,n)\n",
+    "plt.plot(x, binomial_dist_orange.pmf(x), \"o\")\n",
+    "plt.xlabel('number of fruits')\n",
+    "plt.ylabel('probability of oranges')\n",
+    "plt.title(\"Probability of choosing oranges from the total number of fruits in the basket\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -151,11 +284,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 67,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.053775025581946814"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "mu = 2.3\n",
+    "\n",
+    "poisson_dist = poisson(mu)\n",
+    "poisson_dist.pmf(5) #scoring 5 goals"
    ]
   },
   {
@@ -167,18 +314,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 68,
    "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\n",
-    "# Please label the axes and give a title to the plot \n"
+    "# Please label the axes and give a title to the plot \n",
+    "x = np.arange(0,10)\n",
+    "plt.plot(x, poisson_dist.pmf(x), \"o\")\n",
+    "plt.xlabel('number of goals')\n",
+    "plt.ylabel('probability of scoring a goal')\n",
+    "plt.title(\"Probability of having a scored goal\")\n",
+    "plt.show() #it decreases"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "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 +370,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,