Lab done

your_code/main.ipynb

@@ -12,13 +12,45 @@
     "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": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Power_divergenceResult(statistic=6.491310681109821, pvalue=0.4836889068537269)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import scipy.stats as st \n",
+    "from scipy.stats import poisson\n",
+    "\n",
+    "f_obs = [35,99,104,110,62,25,10,3]\n",
+    "mu = 2.435\n",
+    "\n",
+    "poisson_dist = poisson(mu)  \n",
+    "poisson_pmfs = np.array([poisson_dist.pmf(i) for i in range(0,7)])\n",
+    "with_tail = np.append(poisson_pmfs, 1 - poisson_pmfs.sum())\n",
+    "f_exp = with_tail * 448\n",
+    "\n",
+    "st.chisquare(f_exp = f_exp, f_obs = f_obs)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your answer here"
+    "# I can not reject the claim (there is reason to believe) that the number of scores follows a poisson distribution."
    ]
   },
   {
@@ -58,13 +90,46 @@
     "![](table3.png)\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Power_divergenceResult(statistic=8.306179519542805, pvalue=0.015715783395950887)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scipy.stats import binom \n",
+    "\n",
+    "f_obs = [138,53,9]\n",
+    "\n",
+    "p = 0.05\n",
+    "n = 10\n",
+    "\n",
+    "binomial_dist = binom(n,p)\n",
+    "binomial_pmfs = np.array([binomial_dist.pmf(i) for i in range(0,2)])\n",
+    "with_tail = np.append(binomial_pmfs, 1 - binomial_pmfs.sum())\n",
+    "f_exp = with_tail * 200\n",
+    "\n",
+    "st.chisquare(f_exp = f_exp, f_obs = f_obs)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your answer here"
+    "# We can reject the null hypothesis. \n",
+    "# The data suggests that the observed frequencies significantly differ from the expected frequencies based on the assumption that 5% of all tires have defects."
    ]
   },
   {
@@ -77,19 +142,46 @@
     "![](table4.png)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Chi2ContingencyResult(statistic=10.712198008709638, pvalue=0.004719280137040844, dof=2, expected_freq=array([[24.08421053, 19.91578947],\n",
+       "       [19.70526316, 16.29473684],\n",
+       "       [ 8.21052632,  6.78947368]]))"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "table = [[32,12],\n",
+    "         [14,22],\n",
+    "         [6,9]]\n",
+    "\n",
+    "st.chi2_contingency(table)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#your answer here"
+    "# We can reject the null hypothesis.\n",
+    "# There is a significant association between patterns of physical activity and the consumption of sugary drinks among the fifth-grade children in this school."
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -103,7 +195,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.11.3"
   }
  },
  "nbformat": 4,