diff --git a/your_code/main.ipynb b/your_code/main.ipynb
index 7810ccf..fc1fbd9 100644
--- a/your_code/main.ipynb
+++ b/your_code/main.ipynb
@@ -12,13 +12,77 @@
     "Based on these results, we create a Poisson distribution with the sample mean parameter  = 2.435. Is there any reason to believe that at a .05 level the number of scores is a Poisson variable?"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# your answer here\n",
+    "\n",
+    "\n",
+    "from scipy.stats import poisson\n",
+    "import numpy as np\n",
+    "import scipy.stats as st\n",
+    "\n",
+    "from scipy.stats import binom"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "We can not reject the null hypothesis\n",
+      "p-value: 0.4836889068537269\n"
+     ]
+    }
+   ],
+   "source": [
+    "# H0: The number of scores is a Poisson variable (with a sample mean parameter = 2.435)\n",
+    "# H1: he number of scores is not a Poisson variable (with a sample mean parameter = 2.435)\n",
+    "\n",
+    "O = np.array([35, 99, 104, 110, 62, 25, 10, 3])\n",
+    "\n",
+    "population = O.sum() # population total\n",
+    "\n",
+    "mean = 2.435 # sample mean\n",
+    "\n",
+    "alpha = 0.05\n",
+    "\n",
+    "poisson_dist = poisson(mean)\n",
+    "\n",
+    "poisson_pmfs = np.array([poisson_dist.pmf(i) for i in range(0, 7)])\n",
+    "\n",
+    "tail = 1 - poisson_pmfs.sum()\n",
+    "\n",
+    "poisson_with_tail = np.append(poisson_pmfs, tail)\n",
+    "\n",
+    "E = poisson_with_tail * population\n",
+    "\n",
+    "chisquare_result = st.chisquare(f_obs = O, f_exp = E)\n",
+    "\n",
+    "if chisquare_result.pvalue < alpha:\n",
+    "    print(\"We can reject the null hypothesis\")\n",
+    "else:\n",
+    "    print(\"We can not reject the null hypothesis\")\n",
+    "    \n",
+    "print(\"p-value:\", chisquare_result.pvalue)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your answer here"
+    "\"\"\"\n",
+    "There is reason to believe that the number of scores is a Poisson variable\n",
+    "\"\"\""
    ]
   },
   {
@@ -60,11 +124,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "We can reject the null hypothesis\n",
+      "p-value: 0.015715783395950887\n"
+     ]
+    }
+   ],
    "source": [
-    "# your answer here"
+    "# your answer here\n",
+    "\n",
+    "# H0: The sample comes from a binomial population (with n = 10 and p = 0.05)\n",
+    "# H1: The sample does not come from a binomial population (with n = 10 and p = 0.05)\n",
+    "\n",
+    "O = np.array([138, 53, 9])\n",
+    "\n",
+    "population = O.sum()\n",
+    "n = 10\n",
+    "p = 0.05\n",
+    "alpha = 0.05 # assume\n",
+    "\n",
+    "binom_dist = binom(n, p)\n",
+    "\n",
+    "binom_pmfs = np.array([binom_dist.pmf(i) for i in range(0, 2)])\n",
+    "\n",
+    "tail = 1 - binom_pmfs.sum()\n",
+    "\n",
+    "binom_with_tail = np.append(binom_pmfs, tail)\n",
+    "\n",
+    "E = binom_with_tail * population\n",
+    "\n",
+    "chisquare_result = st.chisquare(f_obs = O, f_exp = E)\n",
+    "\n",
+    "if chisquare_result.pvalue < alpha:\n",
+    "    print(\"We can reject the null hypothesis\")\n",
+    "else:\n",
+    "    print(\"We can not reject the null hypothesis\")\n",
+    "    \n",
+    "print(\"p-value:\", chisquare_result.pvalue)"
    ]
   },
   {
@@ -79,17 +181,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "We can reject the null hypothesis\n",
+      "p-value: 0.004719280137040844\n"
+     ]
+    }
+   ],
    "source": [
-    "#your answer here"
+    "#your answer here\n",
+    "\n",
+    "# H0: there is no association between patterns of physical activity and the consumption of sugary drinks\n",
+    "# H1: there is an association between patterns of physical activity and the consumption of sugary drinks\n",
+    "\n",
+    "alpha = 0.05\n",
+    "\n",
+    "activity = np.array([[32, 12], \n",
+    "                     [14, 22], \n",
+    "                     [6, 9]])\n",
+    "\n",
+    "chiscquare_contigency_result = st.chi2_contingency(activity)\n",
+    "\n",
+    "if chiscquare_contigency_result.pvalue < alpha:\n",
+    "    print(\"We can reject the null hypothesis\")\n",
+    "else:\n",
+    "    print(\"We can not reject the null hypothesis\")\n",
+    "    \n",
+    "print(\"p-value:\", chiscquare_contigency_result.pvalue)"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -103,7 +232,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,