diff --git a/your-code/continuous.ipynb b/your-code/continuous.ipynb
index 32218e9..c58bcc8 100644
--- a/your-code/continuous.ipynb
+++ b/your-code/continuous.ipynb
@@ -35,9 +35,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2.7931213  2.19944234 2.08279401 2.25233678 2.64449285 2.69008049\n",
+      " 2.6213758  2.39394713 2.35676919 2.52048969]\n"
+     ]
+    }
+   ],
    "source": [
     "from scipy.stats import uniform\n",
     "x = uniform.rvs(size=10)\n",
@@ -71,13 +80,149 @@
     "![uniform distribution](ud.png)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Imports"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here\n"
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import math\n",
+    "import pandas as pd\n",
+    "from scipy.stats import bernoulli\n",
+    "from scipy.stats import binom\n",
+    "from scipy.stats import geom\n",
+    "from scipy.stats import poisson\n",
+    "\n",
+    "from scipy.stats import uniform\n",
+    "from scipy.stats import expon\n",
+    "from scipy.stats import norm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def uniform_dist (a,b,x):\n",
+    "    x = uniform.rvs(size=x)\n",
+    "    randoms  = a + (b-a)*x\n",
+    "    return randoms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([10.52601396, 13.57849053, 14.49075563, 11.88662204, 10.62690454,\n",
+       "       14.50686457, 12.69646789, 12.76515643, 10.30365447, 11.16208091,\n",
+       "       11.28374055, 14.1467216 , 12.03183369, 14.5413906 , 14.4725695 ,\n",
+       "       14.78497367, 14.97889873, 13.42747328, 11.65097793, 10.18872402,\n",
+       "       11.17262349, 14.85402362, 10.66339558, 13.96751169, 14.03351177,\n",
+       "       14.65735342, 10.2270166 , 10.97032388, 11.88517683, 12.02614921,\n",
+       "       10.28651334, 13.29382448, 12.61502335, 11.57299193, 11.5116339 ,\n",
+       "       11.14374309, 10.01120299, 12.24825967, 14.89059503, 13.49330533,\n",
+       "       11.79076147, 11.57315615, 13.93119021, 13.1363223 , 13.9705958 ,\n",
+       "       11.86650201, 10.25272149, 11.59741237, 14.87072005, 13.9649321 ,\n",
+       "       11.52827197, 13.56227282, 12.41748266, 14.77805467, 10.21086291,\n",
+       "       11.68259445, 14.17786268, 13.53366914, 13.7419255 , 10.27636288,\n",
+       "       14.33016798, 14.07749142, 14.32819049, 12.00161078, 10.66941435,\n",
+       "       11.95688213, 11.43902214, 12.92729822, 11.31856932, 14.5305121 ,\n",
+       "       10.04061175, 11.83678584, 12.49422513, 12.76326371, 12.29858969,\n",
+       "       14.1585574 , 10.41141561, 11.07716629, 12.51235136, 10.77219367,\n",
+       "       10.76891744, 13.90530441, 10.01689701, 14.34306154, 14.00088237,\n",
+       "       13.35375394, 11.33236597, 13.27078232, 14.5196001 , 11.22445557,\n",
+       "       13.23493359, 13.01231315, 14.42666003, 11.99107189, 14.8041996 ,\n",
+       "       11.14498373, 13.85526396, 10.51803167, 13.220944  , 11.92530937])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dist_1=uniform_dist (10, 15,100)\n",
+    "dist_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dist_2=uniform_dist (10, 60,1000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Plot Histogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(dist_1, bins = 10)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(dist_2, bins = 10)\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
@@ -89,11 +234,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your answer here:\n"
+    "# the first distribution has more data points\n"
    ]
   },
   {
@@ -114,11 +259,68 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "def normal_dist (mu, sigma, x):\n",
+    "        random = mu + sigma * np.random.randn(x)\n",
+    "        return random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here\n"
+    "ans_1=normal_dist (10, 1, 1000)\n",
+    "ans_2=normal_dist (10, 50, 1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(ans_1, 50)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(ans_2, 50)\n",
+    "plt.show()"
    ]
   },
   {
@@ -130,11 +332,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your answer here:\n"
+    "# your answer here:\n",
+    "the models are different"
    ]
   },
   {
@@ -158,11 +361,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "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"
+    "vehicles=pd.read_csv(\"vehicles.csv\")\n",
+    "vehicles.head(3)\n",
+    "plt.hist(vehicles[\"Fuel Barrels/Year\"], 50)\n",
+    "plt.show()"
    ]
   },
   {
@@ -174,11 +391,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "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"
+    "plt.hist(vehicles[\"CO2 Emission Grams/Mile\"], 50)\n",
+    "plt.show()\n"
    ]
   },
   {
@@ -190,11 +419,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "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"
+    "plt.hist(vehicles[\"Combined MPG\"], 50)\n",
+    "plt.show()\n"
    ]
   },
   {
@@ -206,11 +447,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# you answer here:\n"
+    "# combined mpg had the smoothest curve"
    ]
   },
   {
@@ -237,11 +478,64 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here\n"
+    "def expo_dist (mu, count):\n",
+    "        return np.random.exponential(mu, size=count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "answer_1=expo_dist(1,1000)\n",
+    "answer_2=expo_dist(100, 1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(answer_1, bins=100)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(answer_2, bins=100)\n",
+    "plt.show()"
    ]
   },
   {
@@ -253,11 +547,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your answer here:\n"
+    "#the tail on the positive side is longer\n"
    ]
   },
   {
@@ -273,12 +567,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7768698398515702"
+      ]
+     },
+     "execution_count": 38,
+     "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=10/1\n",
+    "expon_dist=expon(scale=lambda_inv)\n",
+    "expon_dist.cdf(15)"
    ]
   },
   {
@@ -290,17 +598,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the probability the customer will spend more than 15 mins is  0.7768698398515702\n"
+     ]
+    }
+   ],
    "source": [
-    "# your answer here\n"
+    "lambda_inv=10/1\n",
+    "expon_dist=expon(scale=lambda_inv)\n",
+    "print(\"the probability the customer will spend more than 15 mins is \",expon_dist.cdf(15))\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -314,7 +639,7 @@
    "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..bec8504 100644
--- a/your-code/discrete.ipynb
+++ b/your-code/discrete.ipynb
@@ -31,6 +31,20 @@
     "1. **What is the probability that it is an orange?**"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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\n",
+    "import random"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -39,8 +53,8 @@
    "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 is 60%\n",
+    "q = probability that the fruit is an orange is 40%\n",
     "\"\"\"\n",
     "\n",
     "# your code here\n"
@@ -61,11 +75,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.07775999999999998\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "#What is the probability that the first 5 fruits are all apples?\n",
+    "p=0.6**5\n",
+    "from scipy.stats import bernoulli\n",
+    "bernoulli_dist=bernoulli(p)\n",
+    "print(bernoulli_dist.pmf(1)) #probability of success"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8.349416423424006e-08\n"
+     ]
+    }
+   ],
+   "source": [
+    "#What is the probability that the first 5 fruits are all apples and the next 15 fruits are all oranges?\n",
+    "p=0.6**5\n",
+    "o=0.4**15\n",
+    "from scipy.stats import bernoulli\n",
+    "bernoulli_dist=bernoulli(p*o)\n",
+    "print(bernoulli_dist.pmf(1))"
    ]
   },
   {
@@ -83,11 +131,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0016115246434562898\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "#probability the sample contains 5 apples\n",
+    "\n",
+    "p=0.60\n",
+    "n=20\n",
+    "\n",
+    "bionomial_dist=binom(n,p)\n",
+    "\n",
+    "print(bionomial_dist.cdf(5)) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9996829688788313\n"
+     ]
+    }
+   ],
+   "source": [
+    "#probability the sample contains 15 oranges\n",
+    "\n",
+    "p=0.40\n",
+    "n=20\n",
+    "\n",
+    "bionomial_dist=binom(n,p)\n",
+    "\n",
+    "print(bionomial_dist.cdf(15))"
    ]
   },
   {
@@ -101,11 +188,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9996829688788313\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "#probability the sample contains less than 5 apples\n",
+    "\n",
+    "p=0.60\n",
+    "n=20\n",
+    "\n",
+    "bionomial_dist=binom(n,p)\n",
+    "\n",
+    "print(1-bionomial_dist.cdf(4)) \n"
    ]
   },
   {
@@ -127,6 +229,39 @@
     "# Please label the axes and give a title to the plot\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p = 0.60\n",
+    "n = 20\n",
+    "\n",
+    "binomial_dist = binom(n, p)\n",
+    "\n",
+    "x = np.arange(0, 20)  \n",
+    "\n",
+    "probabilities = binomial_dist.cdf(x)  \n",
+    "\n",
+    "plt.bar(x, probabilities)\n",
+    "plt.xlabel('Number of Apples')\n",
+    "plt.ylabel('Probability')\n",
+    "plt.title('Binomial Distribution - CDF')\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -151,11 +286,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.053775025581946814\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "import math\n",
+    "#math.factorial(n)\n",
+    "mu=2.3\n",
+    "poisson_disc=poisson(mu)\n",
+    "print(poisson_disc.pmf(5))"
    ]
   },
   {
@@ -167,18 +314,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "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"
+    "mu = 2.3\n",
+    "x = np.arange(0, 11)\n",
+    "\n",
+    "poisson_dist = poisson(mu)\n",
+    "probability = poisson_dist.pmf(x)\n",
+    "\n",
+    "plt.bar(x, probability)\n",
+    "plt.xlabel('Number of Goals')\n",
+    "plt.ylabel('Probability')\n",
+    "plt.title('Poisson Distribution - Probability of Goals')\n",
+    "plt.show()\n"
    ]
+  },
+  {
+   "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 +366,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,