Student_Mental_Health_Analysis.ipynb

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    "# Analysis and visualization of medical data in the field of mental health with Python\n",
    "\n",
    "## Efim Shliamin\n",
    "\n",
    "\n",
    "In recent years, there has been a growing awareness of mental health and its impact on different aspects of life. Mental health is especially important for students in the field of education. For example, the article \"Grading Bias and Young Adult Mental Health\" explains that grading bias influences health and skill development after graduation. However, the effects differ based on gender, socioeconomic status, and migration background, leading to health inequalities.\n",
    "\n",
    "The article \"Effects of Mental Health on Student Learning\" also mentions that mental illness is linked to lower academic success and poorer achievement. Good mental health can boost academic performance, while mental stress and issues can negatively affect it.\n",
    "\n",
    "The data set titled \"A Statistical Research on the Effects of Mental Health on Students' CGPA\" was gathered through a survey using Google Forms among university students. The main goal of this study was to examine the relationship between students' mental health and their academic success, as measured by their cumulative grade point average (CGPA). We compared our findings with previous studies and found no negative relationship between grades and mental health disorders in our study. However, our research focused on the mental health of students before they graduate.\n",
    "\n",
    "\n",
    "The survey included questions on various aspects of mental health, such as stress, anxiety, depression, and sleep disorders. It also collected demographic information and data on the students' academic backgrounds. By analyzing this data set, we can gain important insights into how mental health affects students' academic success. This comprehensive study allows us to compare our results with those published in the journal Health Economics. The findings could be valuable for educational institutions, psychologists, and counselors in developing targeted support measures and interventions to promote the mental health and well-being of students.\n",
    "\n",
    "As part of this project, we will analyze the data set using statistical methods to investigate how various mental health factors affect students' CGPA. We will also visualize the results to better represent and interpret the relationships. This project has the potential to enhance our understanding of students' mental health and provide recommendations for educational institutions to promote students' well-being and academic success."
   ]
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   "source": [
    "### Data collection\n",
    "\n",
    "This questionnaire was developed based on the dataset \"A Statistical Research on the Effects of Mental Health on Students' CGPA\" and is designed to gather information on how mental health affects students' academic success.\n",
    "\n",
    "This data set was collected from 100 university students through a survey conducted via Google Forms to examine their current academic situation and mental health."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42f4dc19-81bc-4b14-b333-36303aec1a0f",
   "metadata": {},
   "source": [
    "### Data analysis and data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0ba0702f-4fd2-4975-af59-911c5990b9a2",
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   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d4bce8fa-6517-4176-ae98-d5c155e37a86",
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   "outputs": [],
   "source": [
    "# Load data from CSV file into a DataFrame\n",
    "data = pd.read_csv('students.csv')\n",
    "data = data.dropna()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c1717d62-cb80-4cf7-82fa-d005cdb08c41",
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   "source": [
    "# Convert relevant columns to numeric\n",
    "data['Do you have Anxiety?'] = data['Do you have Anxiety?'].replace({'Yes': 1, 'No': 0}).astype(int)\n",
    "data['Do you have Depression?'] = data['Do you have Depression?'].replace({'Yes': 1, 'No': 0}).astype(int)\n",
    "data['Do you have Panic attack?'] = data['Do you have Panic attack?'].replace({'Yes': 1, 'No': 0}).astype(int)\n",
    "\n",
    "# Clean 'Your current year of Study' column\n",
    "data['Your current year of Study'] = data['Your current year of Study'].str.extract('(\\d+)').astype(int)\n"
   ]
  },
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   "execution_count": 12,
   "id": "021bfb7a-5b6b-4207-8052-d6c3f268ee00",
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   "outputs": [
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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Anxiety Analysis\n",
    "anxiety_by_age = data.groupby('Age')['Do you have Anxiety?'].sum()\n",
    "total_anxiety = data['Do you have Anxiety?'].sum()\n",
    "relative_anxiety_by_age = anxiety_by_age / total_anxiety if total_anxiety > 0 else pd.Series()\n",
    "\n",
    "fig1, ax1 = plt.subplots(1, 2, figsize=(12, 6))\n",
    "ax1[0].bar(anxiety_by_age.index, anxiety_by_age.values)\n",
    "ax1[0].set_xlabel('Age')\n",
    "ax1[0].set_ylabel('Absolute Anxiety Values')\n",
    "ax1[0].set_title('Absolute Anxiety Values by Age')\n",
    "\n",
    "ax1[1].bar(relative_anxiety_by_age.index, relative_anxiety_by_age.values)\n",
    "ax1[1].set_xlabel('Age')\n",
    "ax1[1].set_ylabel('Relative Anxiety Values')\n",
    "ax1[1].set_title('Relative Anxiety Values by Age')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b5e432ac-ccff-42dc-ac94-d36f94ad6178",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Depression Analysis\n",
    "depression_by_age = data.groupby('Age')['Do you have Depression?'].sum()\n",
    "total_depression = data['Do you have Depression?'].sum()\n",
    "relative_depression_by_age = depression_by_age / total_depression if total_depression > 0 else pd.Series()\n",
    "\n",
    "fig2, ax2 = plt.subplots(1, 2, figsize=(12, 6))\n",
    "ax2[0].bar(depression_by_age.index, depression_by_age.values)\n",
    "ax2[0].set_xlabel('Age')\n",
    "ax2[0].set_ylabel('Absolute Depression Values')\n",
    "ax2[0].set_title('Absolute Depression Values by Age')\n",
    "\n",
    "ax2[1].bar(relative_depression_by_age.index, relative_depression_by_age.values)\n",
    "ax2[1].set_xlabel('Age')\n",
    "ax2[1].set_ylabel('Relative Depression Values')\n",
    "ax2[1].set_title('Relative Depression Values by Age')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "007467a8-eabe-4758-baa2-0f866dc01741",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAABnt3r1aq1bty5V+7p167RmzRoHVAQAAJCz2B1KPfbYY1q7dm2q9jVr1uiRRx7JkqIAAACc3dChQ5WUlJSq3TAMDR061AEVAQAA5Cyu9m4QGhqqAQMG6Ny5c2rQoIEk6fvvv9fHH3+siIiIrK4PAADAKR09elTlypVL1V6mTBn9/vvvDqgIAAAgZ7E7lOrVq5cSEhI0ZswYvf/++5KkkiVLavr06erWrVuWFwgAAOCMfHx8dOzYMZUsWdKm/ffff1eePHkcUxQAAEAOYvfpe5L06quv6s8//9Tff/+t+Ph4HTt2jEAKAAA8VJ5//nkNHjxYf/zxh7Xt999/1+uvv67nn3/egZUBAADkDJkKpVIULlxYefPmzapaAAAAcowPP/xQefLkUZkyZVSqVCmVKlVKZcuWVcGCBfXRRx85ujwAAACnl6FQqmrVqrp06ZIkqUqVKqpatWq6DwAAgIeBj4+Ptm/frm+//Vb9+vXT66+/ru+//14bN25U/vz5M7XPadOmqVSpUvL09FS1atW0devWdPueOXNGnTt3VunSpZUrVy4NHjw4VZ/IyEhZLJZUjxs3bmSqPgAAgKyUoWtKtWrVSh4eHpKk1q1bZ2c9AAAAOYbFYlHjxo3VuHHj+95XVFSUBg8erGnTpik4OFiffvqpmjVrpkOHDqlEiRKp+ickJKhw4cIaNmyYJk6cmO5+vb29dfjwYZs2T0/P+64XAADgfmUolBo+fHia/wYAIDuVHPqto0vIdifGtXB0CbDD5MmT9corr8jT01OTJ0++a9+BAwfate8JEyYoJCREvXv3liRFRERo3bp1mj59usLDw1P1L1mypCZNmiRJmjNnTrr7tVgs8vf3t6sWAMDD40GfbzHXcm52330vxc2bN3X27FklJyfbtKf1TR4AAMCDYOLEiXrppZfk6el519VJFovFrlDq5s2b2rt3r4YOHWrT3rhxY23fvj3T9UrS1atXFRQUpKSkJFWuXFnvv/++qlSpkm7/hIQEJSQkWJ/Hx8ff1/sDAACkx+5Q6siRIwoJCUk1QTIMQxaLRUlJSVlWHAAAgDM5fvx4mv++X+fPn1dSUpKKFCli016kSBHFxsZmer9lypRRZGSkKlSooPj4eE2aNEnBwcE6cOCAHn/88TS3CQ8P18iRIzP9ngAAABlldyjVs2dPubq66ptvvlFAQIAsFkt21AUAAPDQuXNelfKlX2bVqlVLtWrVsj4PDg5W1apVNWXKlHRPPwwLC1NoaKj1eXx8vAIDAzNdAwAAQHrsDqWio6O1d+9elSlTJjvqAQAAyBGSkpIUGRmp77//Ps1LGmzcuDHD+ypUqJBcXFxSrYo6e/ZsqtVT9yNXrlyqUaOGjh49mm4fDw8P6w1uAAAAspPdoVS5cuV0/vz57KgFAAAgxxg0aJAiIyPVokULlS9f/r5WNLm7u6tatWpav3692rRpY21fv369WrVqlRXlSrq98io6OloVKlTIsn0CAABklt2h1Pjx4/XWW29p7NixqlChgtzc3Gxe9/b2zrLiAAAAnNWiRYu0ePFiNW/ePEv2Fxoaqq5du6p69eqqXbu2Zs6cqZiYGPXt21fS7dPqTp8+rfnz51u3iY6OlnT7Yubnzp1TdHS03N3dVa5cOUnSyJEjVatWLT3++OOKj4/X5MmTFR0drU8++SRLagYAALgfdodSDRs2lCQ9++yzNu1c6BwAADxM3N3d9dhjj2XZ/jp27KgLFy5o1KhROnPmjMqXL6/Vq1crKChIknTmzBnFxMTYbPPvu+jt3btXCxYsUFBQkE6cOCFJiouL0yuvvKLY2Fj5+PioSpUq2rJli2rWrJlldQMAAGSW3aHUpk2bsqMOAACAHOX111/XpEmTNHXq1Cy78Uu/fv3Ur1+/NF+LjIxM1WYYxl33N3HiRE2cODErSgMAAMhydodS9erVy446AAAAcpRt27Zp06ZNWrNmjZ588slUlzRYunSpgyoDAADIGewOpVJcv35dMTExunnzpk17xYoV77soAAAAZ5c/f36bi5IDAADAPnaHUufOnVPPnj21Zs2aNF/nmlIAAOBhMHfuXEeXAAAAkKPlsneDwYMH69KlS9q5c6e8vLy0du1azZs3T48//rhWrlyZHTUCAAAAAADgAWP3SqmNGzdqxYoVqlGjhnLlyqWgoCA1atRI3t7eCg8PV4sWLbKjTgAAAKezZMkSLV68OM1LGuzbt89BVQEAAOQMdq+Uunbtmvz8/CRJvr6+OnfunCSpQoUKTL4AAMBDY/LkyerZs6f8/Py0f/9+1axZUwULFtSxY8fUrFkzR5cHAADg9OwOpUqXLq3Dhw9LkipXrqxPP/1Up0+f1owZMxQQEJDlBQIAADijadOmaebMmZo6darc3d311ltvaf369Ro4cKAuX77s6PIAAACcXqauKfXXX39JkoYPH661a9eqRIkSmjx5ssaOHZvlBQIAADijmJgY1alTR5Lk5eWlK1euSJK6du2qhQsXOrI0AACAHMHua0q99NJL1n9XqVJFJ06c0P/93/+pRIkSKlSoUJYWBwAA4Kz8/f114cIFBQUFKSgoSDt37lSlSpV0/PhxGYbh6PIAAACcXoZXSl2/fl39+/dXsWLF5Ofnp86dO+v8+fPKnTu3qlatSiAFAAAeKg0aNNCqVaskSSEhIRoyZIgaNWqkjh07qk2bNg6uDgAAwPlleKXU8OHDFRkZqZdeekmenp5auHChXn31VX311VfZWR8AAIBTmjlzppKTkyVJffv2la+vr7Zt26aWLVuqb9++Dq4OAADA+WU4lFq6dKlmz56tTp06SZK6dOmi4OBgJSUlycXFJdsKBAAAcDY//fSTVq5cqVu3bqlhw4Zq3LixOnTooA4dOji6NAAAgBwjw6fvnTp1SnXr1rU+r1mzplxdXa0XPQcAAHgYLFu2TMHBwZo0aZJmzpypZs2aKSIiwtFlAQAA5DgZDqWSkpLk7u5u0+bq6qrExMQsLwoAAMBZjR07Vj169FBcXJzi4uI0cuRIjR492tFlAQAA5DgZPn3PMAz16NFDHh4e1rYbN26ob9++ypMnj7Vt6dKlWVshAACAEzl8+LC+/PJLubrenka9+eabGjFihM6fP8+NXwAAAOyQ4VCqe/fuqdq6dOmSpcUAAAA4u6tXryp//vzW5x4eHvLy8lJ8fDyhFAAAgB0yHErNnTs3O+sAAADIMdatWycfHx/r8+TkZH3//ff65ZdfrG3PP/+8I0oDAADIMTIcSgEAAOC2tFaQ9+nTx/pvi8WipKQkM0sCAADIcQilAAAA7JCcnOzoEgAAAB4IGb77HgAAAAAAAJBVCKUAAAAAAABgOkIpAAAAAAAAmC5TodTnn3+u4OBgFS1aVCdPnpQkRUREaMWKFVlaHAAAAAAAAB5MdodS06dPV2hoqJo3b664uDjrnWXy58+viIiIrK4PAAAAAAAADyC7Q6kpU6bos88+07Bhw+Ti4mJtr169un7++ecsLQ4AAMCZxcXFadasWQoLC9PFixclSfv27dPp06cdXBkAAIDzc7V3g+PHj6tKlSqp2j08PHTt2rUsKQoAAMDZHTx4UA0bNpSPj49OnDihl19+Wb6+vlq2bJlOnjyp+fPnO7pEAAAAp2b3SqlSpUopOjo6VfuaNWtUrly5rKgJAADA6YWGhqpHjx46evSoPD09re3NmjXTli1bHFgZAABAzmD3Sqk333xT/fv3140bN2QYhnbt2qWFCxcqPDxcs2bNyo4aAQAAnM7u3bv16aefpmovVqyYYmNjHVARAABAzmJ3KNWzZ08lJibqrbfe0vXr19W5c2cVK1ZMkyZNUqdOnbKjRgAAAKfj6emp+Pj4VO2HDx9W4cKFHVARAABAzmL36XuS9PLLL+vkyZM6e/asYmNjderUKYWEhGR1bQAAAE6rVatWGjVqlG7duiVJslgsiomJ0dChQ9WuXTsHVwcAAOD87A6lGjRooLi4OElSoUKF5OfnJ0mKj49XgwYNsrQ4AAAAZ/XRRx/p3Llz8vPz0z///KN69erpscceU758+TRmzBhHlwcAAOD07D5974cfftDNmzdTtd+4cUNbt27NkqIAAACcnbe3t7Zt26aNGzdq3759Sk5OVtWqVdWwYUNHlwYAAJAjZDiUOnjwoPXfhw4dsrmAZ1JSktauXatixYplbXUAAABO6sSJEypZsqQaNGjAanEAAIBMyHAoVblyZVksFlksljQnXl5eXpoyZUqWFgcAAOCsHnnkEdWpU0ddu3bVCy+8IF9fX0eXBAAAkKNk+JpSx48f1x9//CHDMLRr1y4dP37c+jh9+rTi4+PVq1ev7KwVAADAaezZs0e1a9fW6NGjVbRoUbVq1UpfffWVEhISHF0aAABAjpDhUCooKEglS5ZUcnKyqlevrqCgIOsjICBALi4u2VknAACAU6latao+/PBDxcTEaM2aNfLz81OfPn3k5+fHF3UAAAAZYPeFzufPn3/X17t165bpYgAAAHIai8Wi+vXrq379+nr11VcVEhKiefPmac6cOY4uDQAAwKnZHUoNGjTI5vmtW7d0/fp1ubu7K3fu3IRSAADgoXLq1CktXLhQCxYs0M8//6zatWtr6tSpji4LAADA6dkdSl26dClV29GjR/Xqq6/qzTffzJKiAAAAnN3MmTP15Zdf6scff1Tp0qX10ksvafny5SpZsqSjSwMAAMgR7A6l0vL4449r3Lhx6tKli/7v//4vK3YJAADg1N5//3116tRJkyZNUuXKlR1dDgAAQI6T4Qud34uLi4v++uuvTG8fHh4ui8WiwYMHZ1VJAAAA2SYmJkYffvghgRQAAEAm2b1SauXKlTbPDcPQmTNnNHXqVAUHB2eqiN27d2vmzJmqWLFiprYHAAAww8GDB1W+fHnlypVLP//88137Mq8BAAC4O7tDqdatW9s8t1gsKly4sBo0aKCPP/7Y7gKuXr2ql156SZ999plGjx5t9/YAAABmqVy5smJjY+X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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Panic Attack Analysis\n",
    "panic_by_age = data.groupby('Age')['Do you have Panic attack?'].sum()\n",
    "total_panic = data['Do you have Panic attack?'].sum()\n",
    "relative_panic_by_age = panic_by_age / total_panic if total_panic > 0 else pd.Series()\n",
    "\n",
    "fig3, ax3 = plt.subplots(1, 2, figsize=(12, 6))\n",
    "ax3[0].bar(panic_by_age.index, panic_by_age.values)\n",
    "ax3[0].set_xlabel('Age')\n",
    "ax3[0].set_ylabel('Absolute Panic Values')\n",
    "ax3[0].set_title('Absolute Panic Values by Age')\n",
    "\n",
    "ax3[1].bar(relative_panic_by_age.index, relative_panic_by_age.values)\n",
    "ax3[1].set_xlabel('Age')\n",
    "ax3[1].set_ylabel('Relative Panic Values')\n",
    "ax3[1].set_title('Relative Panic Values by Age')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "42d449a1-44e8-491e-9fe8-67449209890f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age vs. Year of Study Analysis\n",
    "age_vs_year = data.groupby('Age')['Your current year of Study'].mean()\n",
    "\n",
    "fig4 = plt.figure(figsize=(10, 6))\n",
    "plt.plot(age_vs_year.index, age_vs_year.values, marker='o')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Year of Study')\n",
    "plt.title('Age vs. Year of Study')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "954b59b7-616e-4568-aa04-8ed5a3dbd4ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T-Test Results: Statistic=-0.2788, P-value=0.7810\n"
     ]
    }
   ],
   "source": [
    "# T-Test between students with and without Anxiety for Years of Study\n",
    "t_test_results = stats.ttest_ind(data[data['Do you have Anxiety?'] == 1]['Your current year of Study'],\n",
    "                                 data[data['Do you have Anxiety?'] == 0]['Your current year of Study'])\n",
    "print(f\"T-Test Results: Statistic={t_test_results.statistic:.4f}, P-value={t_test_results.pvalue:.4f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac729cb0-d9db-47ec-8132-a45702bbf8ea",
   "metadata": {},
   "source": [
    "The diagram indicates a general trend: most students are nearing the end of their studies by age 22. To illustrate this, we used a bar plot.\n",
    "\n",
    "Following this analysis, we investigated student performance in relation to mental disorders. Statistical methods such as the t-test were employed. The data was divided into two groups: students with mental disorders and students without mental disorders. The null hypothesis stated that there would be no difference in performance between the two groups, while the alternative hypothesis proposed that there would be a difference.\n",
    "\n",
    "However, it is important to consider other influencing factors such as educational level, socioeconomic status, and the presence of other health problems."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "351893c0-e79d-47e6-9793-96ec6f0ff0ed",
   "metadata": {},
   "source": [
    "### Find connections\n",
    "\n",
    "The aim of our analysis is to identify relationships between students' grades and their mental states. We use statistical methods to uncover patterns and relationships between students' grades and their mental health. By examining these relationships, we can understand the possible effects of mental health on academic performance.\n",
    "\n",
    "Statistical methods, such as the t-test, were applied. The results provide the following information:\n",
    "\n",
    "- Average grade for students with depression: 3.14\n",
    "- Average grade for students without depression: 3.05\n",
    "- The t-test results are as follows:\n",
    "\n",
    "- t-value: 0.654\n",
    "- p-value: 0.514\n",
    "The t-value measures the difference in average grades between the two groups. The p-value indicates the probability of observing the data if there is no real difference between the groups. In this case, the p-value suggests that there is no significant difference in grades between students with and without depression.\n",
    "\n",
    "### Interpretation of the results\n",
    "\n",
    "Based on the average grades, students with depression have a slightly higher average (3.14) compared to students without depression (3.05). However, the results of the t-test, with a t-value of 0.654 and a p-value of 0.514, indicate that there is no significant difference in the average grades between the two groups. The p-value of 0.514 is greater than the commonly used significance level of 0.05, suggesting that the observed difference is likely due to chance rather than an actual difference.\n",
    "\n",
    "### Conclusion\n",
    "\n",
    "The p-value is a statistical measure that helps evaluate the evidence against the null hypothesis. In a statistical test, the p-value indicates the likelihood of obtaining the observed data, or even more extreme results, if the null hypothesis is true. A small p-value suggests that the observed data is unlikely if the null hypothesis is true, thereby supporting the alternative hypothesis.\n",
    "\n",
    "The t-value is a measure of statistical significance used in hypothesis tests. It shows how strongly the average values of two groups (e.g., average grades of students with and without depression) differ in standard errors. A higher t-value indicates a greater deviation between the groups.\n",
    "\n",
    "For this paper, we compared the average grades of students with and without depression. The results show that the average grade of students with depression (3.14) is slightly higher than the average grade of students without depression (3.05). The t-value is 0.654, and the p-value is 0.514. Since the p-value is greater than the commonly used significance level of 0.05, we do not have sufficient evidence to reject the null hypothesis. This means we could not find a significant difference in mean grades between the two groups. Thus, it cannot be said unequivocally that depression has a significant effect on grades.\n",
    "\n",
    "Therefore, our results contradict the statement published in the article \"Effects of Mental Health on Student Learning.\""
   ]
  }
 ],
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   "language": "python",
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