Merge branch 'main' into nlee097_minorfix

# Conflicts:
#	CS105MiniProject.ipynb

1 files changed, 69 insertions, 28 deletions

diff --git a/CS105MiniProject.ipynb b/CS105MiniProject.ipynb
index fca6629..6fb14c0 100644
--- a/CS105MiniProject.ipynb
+++ b/CS105MiniProject.ipynb
@@ -579,38 +579,16 @@
   },
   {
    "cell_type": "code",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean internships:  31.161538461538463\n",
-      "median internships:  2.0\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"mean internships: \", df[\"How many internship/job applications have you sent out so far?\"].mean())\n",
     "print(\"median internships: \", df[\"How many internship/job applications have you sent out so far?\"].median())\n",
     "_ = sns.violinplot(x=df[\"How many internship/job applications have you sent out so far?\"])"
    ],
    "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-02-24T07:45:31.407456Z",
-     "start_time": "2024-02-24T07:45:30.996717Z"
-    }
+    "collapsed": false
    },
-   "id": "350d4fef50f55e38",
-   "execution_count": 110
+   "id": "8b82459b823370cd"
   },
   {
    "cell_type": "markdown",
@@ -621,7 +599,7 @@
    "metadata": {
     "collapsed": false
    },
-   "id": "a3f2976fd30ca299"
+   "id": "c16acfb6f409d15e"
   },
   {
    "cell_type": "markdown",
@@ -636,6 +614,69 @@
   {
    "cell_type": "markdown",
    "source": [
+    "# Hypothesis 1: There will be a correlation between whether people live with family, friends, or neither and whether or not they work\n",
+    "\n",
+    "Null Hypothesis: There is no relationship between people who live with family, friends, or neither and whether or not they work.\n",
+    "\n",
+    "Significance value: 0.1\n",
+    "Degrees of freedom: 3"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "b3b8550f7d931f4f"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "hyp3_major_table = pd.crosstab(df.iloc[:, 3], df.iloc[:, 8], margins=True, margins_name='Total')\n",
+    "hyp3_major_table"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "e1de39044138b742"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "num_rows, num_cols = hyp3_major_table.shape\n",
+    "# Initialize expected frequencies\n",
+    "expected_frequencies = []\n",
+    "chi_squared = 0\n",
+    "for i in range(num_rows - 1):\n",
+    "    row_totals = hyp3_major_table.iloc[i, -1]\n",
+    "    for j in range(num_cols - 1):\n",
+    "        col_totals = hyp3_major_table.iloc[-1, j]\n",
+    "        expected_frequency = (row_totals * col_totals) / hyp3_major_table.iloc[-1, -1]\n",
+    "        expected_frequencies.append(expected_frequency)\n",
+    "        chi_squared += ((hyp3_major_table.iloc[i, j] - expected_frequency) ** 2) / expected_frequency\n",
+    "\n",
+    "print(\"Chi-squared value:\", chi_squared)"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "a113621af30160ab"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "With a significance value of 0.1 and 3 degrees of freedom, chi-squared must be greater than 6.25.\n",
+    "Since chi-squared of `4.61 < 6.25`, we accept the null hypothesis:\n",
+    "\n",
+    "There is no relationship between people who live with family, friends, or neither and whether or not they work."
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "d8fe5f03e8d634a7"
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
     "### Hypothesis 2: Students who live on-campus are more likely to have roommates of the same major.\n",
     "\n",
     "Null Hypothesis: There is no relationship between students who live on-campus and students who have roommates of the same major.\n",
@@ -646,7 +687,7 @@
    "metadata": {
     "collapsed": false
    },
-   "id": "dcc6d91b3e660c2e"
+   "id": "5c95a7d0932aef71"
   },
   {
    "cell_type": "code",
@@ -662,7 +703,7 @@
     }
    ],
    "source": [
-    "roommates_major_table = pd.crosstab(df.iloc[:, 3], df.iloc[:, 9], margins=True, margins_name='Total')\n",
+    "roommates_major_table = pd.crosstab(df.iloc[:, 4], df.iloc[:, 11], margins=True, margins_name='Total')\n",
     "roommates_major_table"
    ],
    "metadata": {