diff --git a/your_code/main.ipynb b/your_code/main.ipynb
index 7810ccf..e7eab6e 100644
--- a/your_code/main.ipynb
+++ b/your_code/main.ipynb
@@ -14,11 +14,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Power_divergenceResult(statistic=191.93184027673232, pvalue=5.85583627060059e-38)"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "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": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your answer here"
+    "#In this case, the p-value is extremely small (close to zero), which is significantly less than 0.05. Therefore, at a 95% confidence level, we reject the null hypothesis. There is strong evidence to suggest that the variables are not independent.\n",
+    "\n",
+    "#In conclusion, based on the chi-squared test, there is a significant relationship between the variables in the contingency table."
    ]
   },
   {
@@ -39,6 +73,13 @@
     "Does the distribution of defective items follow this distribution?"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -60,11 +101,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Power_divergenceResult(statistic=336.43955678670346, pvalue=8.771593494342625e-74)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your answer here"
+    "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)"
    ]
   },
   {
@@ -79,12 +144,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0047192801370408155"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "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": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your answer here"
+    "#don't reject hypothesis test"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -103,7 +210,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,