diff --git a/your_code/main.ipynb b/your_code/main.ipynb
index 7810ccf..c15ea64 100644
--- a/your_code/main.ipynb
+++ b/your_code/main.ipynb
@@ -14,11 +14,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fail to reject the null hypothesis. The data follows a Poisson distribution.\n"
+     ]
+    }
+   ],
    "source": [
-    "# your answer here"
+    "import numpy as np\n",
+    "from scipy.stats import poisson, chi2\n",
+    "\n",
+    "observed_frequencies = np.array([35, 99, 104, 110, 62, 25, 10, 3])\n",
+    "total_times = 448\n",
+    "mean_parameter = np.sum(np.arange(len(observed_frequencies)) * observed_frequencies) / total_times\n",
+    "expected_frequencies = poisson.pmf(np.arange(len(observed_frequencies)), mean_parameter) * total_times\n",
+    "chi_squared_statistic = np.sum((observed_frequencies - expected_frequencies)**2 / expected_frequencies)\n",
+    "degrees_of_freedom = len(observed_frequencies) - 1\n",
+    "critical_value = chi2.ppf(0.95, degrees_of_freedom)\n",
+    "\n",
+    "\n",
+    "if chi_squared_statistic > critical_value:\n",
+    "    print(\"Reject the null hypothesis. The data does not follow a Poisson distribution.\")\n",
+    "else:\n",
+    "    print(\"Fail to reject the null hypothesis. The data follows a Poisson distribution.\")"
    ]
   },
   {
@@ -60,11 +83,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reject the null hypothesis. The data does not fit a binomial distribution.\n"
+     ]
+    }
+   ],
    "source": [
-    "# your answer here"
+    "observed_frequencies = np.array([138, 53, 9])\n",
+    "defective_item_counts = np.array([0, 1, 2])\n",
+    "\n",
+    "\n",
+    "total_samples = np.sum(observed_frequencies)\n",
+    "p = 0.05\n",
+    "expected_frequencies = np.array([total_samples * (1 - p)**2, total_samples * 2 * p * (1 - p), total_samples * p**2])\n",
+    "chi_squared_statistic = np.sum((observed_frequencies - expected_frequencies)**2 / expected_frequencies)\n",
+    "degrees_of_freedom = len(observed_frequencies) - 1\n",
+    "critical_value = chi2.ppf(0.95, degrees_of_freedom)\n",
+    "\n",
+    "\n",
+    "if chi_squared_statistic > critical_value:\n",
+    "    print(\"Reject the null hypothesis. The data does not fit a binomial distribution.\")\n",
+    "else:\n",
+    "    print(\"Fail to reject the null hypothesis. The data fits a binomial distribution.\")"
    ]
   },
   {
@@ -79,17 +125,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reject the null hypothesis. There is a significant association between patterns of physical activity and sugary drink consumption.\n"
+     ]
+    }
+   ],
    "source": [
-    "#your answer here"
+    "from scipy.stats import chi2_contingency\n",
+    "\n",
+    "\n",
+    "observed_frequencies = np.array([[32, 14, 6],\n",
+    "                                 [12, 22, 9]])\n",
+    "chi2, p, dof, expected = chi2_contingency(observed_frequencies)\n",
+    "alpha = 0.05\n",
+    "if p < alpha:\n",
+    "    print(\"Reject the null hypothesis. There is a significant association between patterns of physical activity and sugary drink consumption.\")\n",
+    "else:\n",
+    "    print(\"Fail to reject the null hypothesis. There is no significant association between patterns of physical activity and sugary drink consumption.\")"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -103,7 +167,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,