diff --git a/your_code/main.ipynb b/your_code/main.ipynb
index 7810ccf..a08e51e 100644
--- a/your_code/main.ipynb
+++ b/your_code/main.ipynb
@@ -14,11 +14,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Power_divergenceResult(statistic=191.93184027673232, pvalue=5.85583627060059e-38)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your answer here"
+    "# your answer here\n",
+    "from scipy.stats import poisson\n",
+    "from scipy.stats import chisquare\n",
+    "import numpy as np\n",
+    "f_obs = np.array([35,99,104,110,62,25,10,3])\n",
+    "mean = 2.435\n",
+    "poisson_dist = poisson(mean)\n",
+    "poisson_pmfs = np.array([poisson_dist.pmf(i) for i in range(1,8)]) \n",
+    "poisson_pmfs\n",
+    "with_tail = np.append(poisson_pmfs,1- poisson_pmfs.sum())\n",
+    "with_tail\n",
+    "f_exp = with_tail*448\n",
+    "f_exp\n",
+    "chisquare(f_exp = f_exp, f_obs = f_obs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "5.85583627060059e-38 < 0.05 #REJECT "
    ]
   },
   {
@@ -60,11 +104,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Power_divergenceResult(statistic=336.43955678670346, pvalue=8.771593494342625e-74)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your answer here"
+    "# your answer here\n",
+    "from scipy.stats import chisquare\n",
+    "\n",
+    "observed_frequencies = [138, 53, 9]\n",
+    "expected_proportion = 0.05\n",
+    "total_samples = 200\n",
+    "categories = 3\n",
+    "\n",
+    "expected_probabilities = [expected_proportion**i * (1 - expected_proportion)**(categories - i) for i in range(categories)]\n",
+    "\n",
+    "expected_frequencies = [total_samples * prob for prob in expected_probabilities]\n",
+    "\n",
+    "expected_frequencies_adjusted = [freq * (total_samples / sum(expected_frequencies)) for freq in expected_frequencies]\n",
+    "\n",
+    "chisquare(f_obs=observed_frequencies, f_exp=expected_frequencies_adjusted)\n"
    ]
   },
   {
@@ -79,17 +148,71 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0047192801370408155"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#your answer here"
+    "#your answer here\n",
+    "import numpy as np\n",
+    "from scipy.stats import chi2\n",
+    "f_obs = np.array([[32, 12], [14, 22], [6, 9]])\n",
+    "\n",
+    "row_totals = [44,36,15]\n",
+    "col_totals = [52,43]\n",
+    "grand_total = [95]\n",
+    "\n",
+    "f_exp = np.outer(row_totals, col_totals) / grand_total\n",
+    "\n",
+    "chi2_statistic = np.sum((f_obs - f_exp)**2 / f_exp)\n",
+    "\n",
+    "degrees_of_freedom = (f_obs.shape[0] - 1) * (f_obs.shape[1] - 1)\n",
+    "\n",
+    "p_value = 1 - chi2.cdf(chi2_statistic, degrees_of_freedom)\n",
+    "p_value"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0.0047192801370408155 < 0.05 #Don't Reject"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -103,7 +226,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,