Merge pull request #18 from ansg191/pie-chart

Chi-square equation

1 files changed, 147 insertions, 53 deletions

diff --git a/CS105MiniProject.ipynb b/CS105MiniProject.ipynb
index 9203683..0663bc0 100644
--- a/CS105MiniProject.ipynb
+++ b/CS105MiniProject.ipynb
@@ -12,7 +12,7 @@
    "metadata": {
     "collapsed": false
    },
-   "id": "845bdbd833f03cba"
+   "id": "21abd26c73fd0070"
   },
   {
    "cell_type": "markdown",
@@ -22,10 +22,24 @@
    "metadata": {
     "collapsed": false
    },
-   "id": "d720609d765d221b"
+   "id": "69d8e8ad7c61ba61"
   },
   {
    "cell_type": "code",
+   "execution_count": 1,
+   "id": "daa13044",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 614
+    },
+    "id": "daa13044",
+    "outputId": "4d440aaa-1ee7-4771-c526-f55e9458ca8a",
+    "ExecuteTime": {
+     "end_time": "2024-02-23T06:18:35.652667Z",
+     "start_time": "2024-02-23T06:18:35.617295Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -51,16 +65,7 @@
     "# Select relevant columns\n",
     "df = df.iloc[:, [2, 3, 7, 8, 9, 58, 59, 60, 61, 26]]\n",
     "df"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-02-23T02:35:17.657074Z",
-     "start_time": "2024-02-23T02:35:17.625812Z"
-    }
-   },
-   "id": "3bea6ea662d6c063",
-   "execution_count": 1
+   ]
   },
   {
    "cell_type": "markdown",
@@ -70,10 +75,19 @@
    "metadata": {
     "collapsed": false
    },
-   "id": "7e69a5a21a9de4ee"
+   "id": "3f7614a5665d55b6"
   },
   {
    "cell_type": "code",
+   "execution_count": 2,
+   "id": "29889175",
+   "metadata": {
+    "id": "29889175",
+    "ExecuteTime": {
+     "end_time": "2024-02-23T06:18:35.665240Z",
+     "start_time": "2024-02-23T06:18:35.654717Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -110,19 +124,19 @@
     "df.loc[df['Do you currently work?'] == 'No', 'Do you work in a department related to your major?'] = np.nan\n",
     "\n",
     "df"
-   ],
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "de4448fd64205d85",
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-02-23T02:35:17.671536Z",
-     "start_time": "2024-02-23T02:35:17.657952Z"
+     "end_time": "2024-02-23T06:18:35.672377Z",
+     "start_time": "2024-02-23T06:18:35.665944Z"
     }
    },
-   "id": "f71f8085d5f66b0",
-   "execution_count": 2
-  },
-  {
-   "cell_type": "code",
    "outputs": [
     {
      "data": {
@@ -140,19 +154,19 @@
     "# Not working DataFrame\n",
     "nw_df = df[df['Do you currently work?'] == 'No']\n",
     "w_df"
-   ],
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5fe8ec7f22878e60",
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-02-23T02:35:17.682158Z",
-     "start_time": "2024-02-23T02:35:17.673510Z"
+     "end_time": "2024-02-23T06:18:35.679365Z",
+     "start_time": "2024-02-23T06:18:35.673583Z"
     }
    },
-   "id": "6c1d9ee7948e6b9a",
-   "execution_count": 3
-  },
-  {
-   "cell_type": "code",
    "outputs": [
     {
      "data": {
@@ -166,26 +180,17 @@
    ],
    "source": [
     "nw_df"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-02-23T02:35:17.690742Z",
-     "start_time": "2024-02-23T02:35:17.683032Z"
-    }
-   },
-   "id": "34f69a756f513fb7",
-   "execution_count": 4
+   ]
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "# Analysis"
-   ],
+   "id": "899d85626b77db20",
    "metadata": {
     "collapsed": false
    },
-   "id": "d5c1424ddd30ca97"
+   "source": [
+    "# Analysis"
+   ]
   },
   {
    "cell_type": "code",
@@ -212,11 +217,11 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-02-23T02:35:17.897912Z",
-     "start_time": "2024-02-23T02:35:17.691750Z"
+     "end_time": "2024-02-23T06:18:35.781584Z",
+     "start_time": "2024-02-23T06:18:35.680024Z"
     }
    },
-   "id": "da1811cc63b41845",
+   "id": "6bc50ddc195d88a",
    "execution_count": 5
   },
   {
@@ -246,11 +251,11 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-02-23T02:35:18.116782Z",
-     "start_time": "2024-02-23T02:35:17.901109Z"
+     "end_time": "2024-02-23T06:18:36.021224Z",
+     "start_time": "2024-02-23T06:18:35.783111Z"
     }
    },
-   "id": "201db70188d3e778",
+   "id": "15f1e14311b1b17f",
    "execution_count": 6
   },
   {
@@ -259,7 +264,7 @@
    "metadata": {
     "collapsed": false
    },
-   "id": "8d65fec230193b72"
+   "id": "2b499b750ea3aec9"
   },
   {
    "cell_type": "code",
@@ -282,12 +287,101 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2024-02-23T02:35:18.248057Z",
-     "start_time": "2024-02-23T02:35:18.118242Z"
+     "end_time": "2024-02-23T06:18:36.170073Z",
+     "start_time": "2024-02-23T06:18:36.024936Z"
     }
    },
-   "id": "5e460707e32c4a2a",
+   "id": "a3d9a4a3b5eba149",
    "execution_count": 7
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Hypotheses"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "4df3824f641fb18b"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### Hypothesis 2: Students who live on-campus are more likely to have roommates of the same major."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "796d474b4650e712"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Do you have roommates that are part of your major?   No  Yes  Total\nDo you currently live in a house, apartnment, o...                 \nApartment                                            83   44    127\nDorm                                                 17   11     28\nHouse                                                78   21     99\nRoom                                                  1    0      1\nTotal                                               179   76    255",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>Do you have roommates that are part of your major?</th>\n      <th>No</th>\n      <th>Yes</th>\n      <th>Total</th>\n    </tr>\n    <tr>\n      <th>Do you currently live in a house, apartnment, or dorm?</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Apartment</th>\n      <td>83</td>\n      <td>44</td>\n      <td>127</td>\n    </tr>\n    <tr>\n      <th>Dorm</th>\n      <td>17</td>\n      <td>11</td>\n      <td>28</td>\n    </tr>\n    <tr>\n      <th>House</th>\n      <td>78</td>\n      <td>21</td>\n      <td>99</td>\n    </tr>\n    <tr>\n      <th>Room</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Total</th>\n      <td>179</td>\n      <td>76</td>\n      <td>255</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roommates_major_table = pd.crosstab(df.iloc[:, 3], df.iloc[:, 9], margins=True, margins_name='Total')\n",
+    "roommates_major_table"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-02-23T06:18:36.201284Z",
+     "start_time": "2024-02-23T06:18:36.179437Z"
+    }
+   },
+   "id": "2ee7f39b5d8df8de",
+   "execution_count": 8
+  },
+  {
+   "cell_type": "code",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Chi-squared Value: 6.54402786926266\n",
+      "Degrees of Freedom: 8\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extract the observed values from the contingency table\n",
+    "observed_values = roommates_major_table.iloc[:-1, :-1].values\n",
+    "\n",
+    "# Calculate expected values\n",
+    "row_totals = roommates_major_table.iloc[:-1, -1].values\n",
+    "col_totals = roommates_major_table.iloc[-1, :-1].values\n",
+    "total = np.sum(row_totals)\n",
+    "\n",
+    "expected_values = np.outer(row_totals, col_totals) / total\n",
+    "\n",
+    "# Calculate chi-squared statistic\n",
+    "chi2_statistic = np.sum((observed_values - expected_values)**2 / expected_values)\n",
+    "\n",
+    "# Degrees of freedom\n",
+    "degrees_of_freedom = (roommates_major_table.shape[0] - 1) * (roommates_major_table.shape[1] - 1)\n",
+    "\n",
+    "# Print results\n",
+    "print(f\"Chi-squared Value: {chi2_statistic}\\nDegrees of Freedom: {degrees_of_freedom}\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-02-23T06:18:36.219063Z",
+     "start_time": "2024-02-23T06:18:36.205767Z"
+    }
+   },
+   "id": "957406c164cf2ef1",
+   "execution_count": 9
   }
  ],
  "metadata": {