gina_UPDATEDPLOTS_a4.ipynb

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   "source": [
    "# Assignment 2: Exploratory Data Analysis and Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd64372d-31d9-4263-aa22-61c95e876baf",
   "metadata": {},
   "source": [
    "#### Gina Choi 84259324"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d1ce2ae-2fa2-49a1-81fd-765f311d207d",
   "metadata": {},
   "source": [
    "**Summary of Dataset**\n",
    "\n",
    "From kaggle, the World University Rankings 2023 dataset spans 104 countries and regions with 1799 universities. The data was collected by Times Higher Education's survey responses from 40,000 scholars across the world and analyzing 121 million citations in over 15.5 million research publications. Over 2,500 institutions\n",
    "submitted data and over 680,000 data points were collected as a result. The key variables in this dataset are the teaching, research, international outlook and citations score. \n",
    "\n",
    "**Characterization of Data:**\n",
    "\n",
    "- 2341 observations\n",
    "- 13 variables\n",
    "\n",
    "**Link to Data:** https://www.kaggle.com/datasets/alitaqi000/world-university-rankings-2023"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9a90b94-2381-48b0-9964-cb6855d7b796",
   "metadata": {},
   "source": [
    "| Variable Name             | Description                               | Data Type |\n",
    "|-------------------------|-------------------------------------------|-----------|\n",
    "| University Rank         | World ranking of the university                 | character   |\n",
    "| Name of University      | Name                     | character |\n",
    "| Location                | University Location               | character |\n",
    "| No of student           | Number of students enrolled presently                     | numeric  |\n",
    "| No of student per staff | Number of students per staff (students under 1 professor)          | numeric   |\n",
    "| International Student   | Percentage of international students      | character  |\n",
    "| Female : Male Ratio     | Ratio of female to male students          | character   |\n",
    "| OverAll Score           | Overall score out of 100          | character    |\n",
    "| Teaching Score          | Teaching score out of 100         | character    |\n",
    "| Research Score          | Research score out of 100         | character    |\n",
    "| Citations Score         | Citations score out of 100. (Number of citations recieved by a journal in a year to documents published in the past 3 years divided by number of indexed documents in Scopus published in the past 3 years)     | character    |\n",
    "| Industry Income Score   | Industry income score out of 100. Based on how much the university earns from the working industry due to its academics.  | character    |\n",
    "| International Outlook Score | International outlook score out of 100. Based on how well the university attracts students and staff on a global scale. | character  |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e03e43c4-c6fc-475e-82ee-4766c242005e",
   "metadata": {},
   "source": [
    "**Question:** \n",
    "\n",
    "### **How does the research score change based on university rank, teaching score, and female to male ratios?**\n",
    "\n",
    "This question is inferential - I am looking to explore the relationship between these covariates and the research score. My goal is to quantify these trends using the following variables in the dataset: Female : Male Ratio, University Rank, Research Score, and Teaching Score. This would provide insight into how the quality of teaching, gender ratios, and ranking affects the quality of research at an institution. We will explore and quantify how applicable these insights can be to the wider population.  \n",
    "\n",
    "Gender wage gaps vary across the globe. For example, according to the annual Gender Gap Report in 2023 by the World Economic Forum, India placed 127th out of 146 countries. In comparison, Canada ranked 52nd. Therefore, it will be interesting to see if the institutions' research score reflects this gap. \n",
    "\n",
    "Furthermore, we can see how rank affects these gender ratios. In general and in most countries, higher ranking universities more likely lead to better opportunities and salaries, which could be a factor in the gender gap if higher ranking institutions had fewer females enrolled. As a higher research score increases rank, this could suggest that high research scores may associate with lower female enrollment. \n",
    "\n",
    "Finally, teaching score reflects the expertise of faculty, which may be associated with the research score. However, we will keep in mind that some institutions are better known for research than teaching and vice versa.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "037bc314-e52b-4bd8-a0a0-f2ff26841dec",
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Installing package into ‘/home/jupyter/R/x86_64-pc-linux-gnu-library/4.3’\n",
      "(as ‘lib’ is unspecified)\n",
      "\n",
      "Warning message in install.packages(\"readr\"):\n",
      "“installation of package ‘readr’ had non-zero exit status”\n",
      "Installing package into ‘/home/jupyter/R/x86_64-pc-linux-gnu-library/4.3’\n",
      "(as ‘lib’ is unspecified)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "install.packages(\"readr\")\n",
    "install.packages(\"ggplot2\")\n",
    "library(readr)\n",
    "library(dplyr)\n",
    "library(ggplot2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c7a6cd2-6b41-4b98-b9ec-b5c4bd683dff",
   "metadata": {},
   "source": [
    "### First, we read the dataset from the web into R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "a114c9b8-2e25-4d29-9d97-89b3010c34fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1mRows: \u001b[22m\u001b[34m2341\u001b[39m \u001b[1mColumns: \u001b[22m\u001b[34m13\u001b[39m\n",
      "\u001b[36m──\u001b[39m \u001b[1mColumn specification\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m (11): University Rank, Name of University, Location, International Stude...\n",
      "\u001b[32mdbl\u001b[39m  (1): No of student per staff\n",
      "\u001b[32mnum\u001b[39m  (1): No of student\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use `spec()` to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set `show_col_types = FALSE` to quiet this message.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 13</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>University Rank</th><th scope=col>Name of University</th><th scope=col>Location</th><th scope=col>No of student</th><th scope=col>No of student per staff</th><th scope=col>International Student</th><th scope=col>Female:Male Ratio</th><th scope=col>OverAll Score</th><th scope=col>Teaching Score</th><th scope=col>Research Score</th><th scope=col>Citations Score</th><th scope=col>Industry Income Score</th><th scope=col>International Outlook Score</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>University of Oxford                 </td><td>United Kingdom</td><td>20965</td><td>10.6</td><td>42%</td><td>48 : 52</td><td>96.4</td><td>92.3</td><td>99.7</td><td>99.0</td><td>74.9</td><td>96.2</td></tr>\n",
       "\t<tr><td>2</td><td>Harvard University                   </td><td>United States </td><td>21887</td><td> 9.6</td><td>25%</td><td>50 : 50</td><td>95.2</td><td>94.8</td><td>99.0</td><td>99.3</td><td>49.5</td><td>80.5</td></tr>\n",
       "\t<tr><td>3</td><td>University of Cambridge              </td><td>United Kingdom</td><td>20185</td><td>11.3</td><td>39%</td><td>47 : 53</td><td>94.8</td><td>90.9</td><td>99.5</td><td>97.0</td><td>54.2</td><td>95.8</td></tr>\n",
       "\t<tr><td>3</td><td>Stanford University                  </td><td>United States </td><td>16164</td><td> 7.1</td><td>24%</td><td>46 : 54</td><td>94.8</td><td>94.2</td><td>96.7</td><td>99.8</td><td>65.0</td><td>79.8</td></tr>\n",
       "\t<tr><td>5</td><td>Massachusetts Institute of Technology</td><td>United States </td><td>11415</td><td> 8.2</td><td>33%</td><td>40 : 60</td><td>94.2</td><td>90.7</td><td>93.6</td><td>99.8</td><td>90.9</td><td>89.3</td></tr>\n",
       "\t<tr><td>6</td><td>California Institute of Technology   </td><td>United States </td><td> 2237</td><td> 6.2</td><td>34%</td><td>37 : 63</td><td>94.1</td><td>90.9</td><td>97.0</td><td>97.3</td><td>89.8</td><td>83.6</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 13\n",
       "\\begin{tabular}{lllllllllllll}\n",
       " University Rank & Name of University & Location & No of student & No of student per staff & International Student & Female:Male Ratio & OverAll Score & Teaching Score & Research Score & Citations Score & Industry Income Score & International Outlook Score\\\\\n",
       " <chr> & <chr> & <chr> & <dbl> & <dbl> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t 1 & University of Oxford                  & United Kingdom & 20965 & 10.6 & 42\\% & 48 : 52 & 96.4 & 92.3 & 99.7 & 99.0 & 74.9 & 96.2\\\\\n",
       "\t 2 & Harvard University                    & United States  & 21887 &  9.6 & 25\\% & 50 : 50 & 95.2 & 94.8 & 99.0 & 99.3 & 49.5 & 80.5\\\\\n",
       "\t 3 & University of Cambridge               & United Kingdom & 20185 & 11.3 & 39\\% & 47 : 53 & 94.8 & 90.9 & 99.5 & 97.0 & 54.2 & 95.8\\\\\n",
       "\t 3 & Stanford University                   & United States  & 16164 &  7.1 & 24\\% & 46 : 54 & 94.8 & 94.2 & 96.7 & 99.8 & 65.0 & 79.8\\\\\n",
       "\t 5 & Massachusetts Institute of Technology & United States  & 11415 &  8.2 & 33\\% & 40 : 60 & 94.2 & 90.7 & 93.6 & 99.8 & 90.9 & 89.3\\\\\n",
       "\t 6 & California Institute of Technology    & United States  &  2237 &  6.2 & 34\\% & 37 : 63 & 94.1 & 90.9 & 97.0 & 97.3 & 89.8 & 83.6\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 13\n",
       "\n",
       "| University Rank &lt;chr&gt; | Name of University &lt;chr&gt; | Location &lt;chr&gt; | No of student &lt;dbl&gt; | No of student per staff &lt;dbl&gt; | International Student &lt;chr&gt; | Female:Male Ratio &lt;chr&gt; | OverAll Score &lt;chr&gt; | Teaching Score &lt;chr&gt; | Research Score &lt;chr&gt; | Citations Score &lt;chr&gt; | Industry Income Score &lt;chr&gt; | International Outlook Score &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | University of Oxford                  | United Kingdom | 20965 | 10.6 | 42% | 48 : 52 | 96.4 | 92.3 | 99.7 | 99.0 | 74.9 | 96.2 |\n",
       "| 2 | Harvard University                    | United States  | 21887 |  9.6 | 25% | 50 : 50 | 95.2 | 94.8 | 99.0 | 99.3 | 49.5 | 80.5 |\n",
       "| 3 | University of Cambridge               | United Kingdom | 20185 | 11.3 | 39% | 47 : 53 | 94.8 | 90.9 | 99.5 | 97.0 | 54.2 | 95.8 |\n",
       "| 3 | Stanford University                   | United States  | 16164 |  7.1 | 24% | 46 : 54 | 94.8 | 94.2 | 96.7 | 99.8 | 65.0 | 79.8 |\n",
       "| 5 | Massachusetts Institute of Technology | United States  | 11415 |  8.2 | 33% | 40 : 60 | 94.2 | 90.7 | 93.6 | 99.8 | 90.9 | 89.3 |\n",
       "| 6 | California Institute of Technology    | United States  |  2237 |  6.2 | 34% | 37 : 63 | 94.1 | 90.9 | 97.0 | 97.3 | 89.8 | 83.6 |\n",
       "\n"
      ],
      "text/plain": [
       "  University Rank Name of University                    Location      \n",
       "1 1               University of Oxford                  United Kingdom\n",
       "2 2               Harvard University                    United States \n",
       "3 3               University of Cambridge               United Kingdom\n",
       "4 3               Stanford University                   United States \n",
       "5 5               Massachusetts Institute of Technology United States \n",
       "6 6               California Institute of Technology    United States \n",
       "  No of student No of student per staff International Student Female:Male Ratio\n",
       "1 20965         10.6                    42%                   48 : 52          \n",
       "2 21887          9.6                    25%                   50 : 50          \n",
       "3 20185         11.3                    39%                   47 : 53          \n",
       "4 16164          7.1                    24%                   46 : 54          \n",
       "5 11415          8.2                    33%                   40 : 60          \n",
       "6  2237          6.2                    34%                   37 : 63          \n",
       "  OverAll Score Teaching Score Research Score Citations Score\n",
       "1 96.4          92.3           99.7           99.0           \n",
       "2 95.2          94.8           99.0           99.3           \n",
       "3 94.8          90.9           99.5           97.0           \n",
       "4 94.8          94.2           96.7           99.8           \n",
       "5 94.2          90.7           93.6           99.8           \n",
       "6 94.1          90.9           97.0           97.3           \n",
       "  Industry Income Score International Outlook Score\n",
       "1 74.9                  96.2                       \n",
       "2 49.5                  80.5                       \n",
       "3 54.2                  95.8                       \n",
       "4 65.0                  79.8                       \n",
       "5 90.9                  89.3                       \n",
       "6 89.8                  83.6                       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data <- read_csv(\"/home/jupyter/301 Project/data.csv\")\n",
    "head(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "412de3df-1221-475d-addd-02b8d9fab421",
   "metadata": {},
   "source": [
    "## Cleaning and Wrangling data into a Tidy format:\n",
    "\n",
    "By looking at the last rows of the data, we see many NA and n/a values. To clean the data up, we use na.omit() to remove these values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "1b2da575-3c8f-4cc0-b693-0796f90fd2e7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 13</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>University Rank</th><th scope=col>Name of University</th><th scope=col>Location</th><th scope=col>No of student</th><th scope=col>No of student per staff</th><th scope=col>International Student</th><th scope=col>Female:Male Ratio</th><th scope=col>OverAll Score</th><th scope=col>Teaching Score</th><th scope=col>Research Score</th><th scope=col>Citations Score</th><th scope=col>Industry Income Score</th><th scope=col>International Outlook Score</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>-</td><td>University of the Western Cape     </td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>34.0–39.2</td><td>20.4</td><td>23.7</td><td>61.6</td><td>36.9</td><td>55.8</td></tr>\n",
       "\t<tr><td>-</td><td>University of the West of Scotland </td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>34.0–39.2</td><td>24.1</td><td>15.5</td><td>61.5</td><td>37.9</td><td>76.8</td></tr>\n",
       "\t<tr><td>-</td><td>University of Windsor              </td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>34.0–39.2</td><td>35.1</td><td>29.4</td><td>34.5</td><td>44.2</td><td>88.7</td></tr>\n",
       "\t<tr><td>-</td><td>University of Wolverhampton        </td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>34.0–39.2</td><td>18.2</td><td>14.3</td><td>68.8</td><td>37.3</td><td>72.0</td></tr>\n",
       "\t<tr><td>-</td><td>University of Wuppertal            </td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>34.0–39.2</td><td>26.4</td><td>26.7</td><td>52.8</td><td>52.1</td><td>47.6</td></tr>\n",
       "\t<tr><td>-</td><td>Xi’an Jiaotong-Liverpool University</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>34.0–39.2</td><td>17.8</td><td>14.8</td><td>68.2</td><td>38.2</td><td>72.4</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 13\n",
       "\\begin{tabular}{lllllllllllll}\n",
       " University Rank & Name of University & Location & No of student & No of student per staff & International Student & Female:Male Ratio & OverAll Score & Teaching Score & Research Score & Citations Score & Industry Income Score & International Outlook Score\\\\\n",
       " <chr> & <chr> & <chr> & <dbl> & <dbl> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t - & University of the Western Cape      & NA & NA & NA & NA & NA & 34.0–39.2 & 20.4 & 23.7 & 61.6 & 36.9 & 55.8\\\\\n",
       "\t - & University of the West of Scotland  & NA & NA & NA & NA & NA & 34.0–39.2 & 24.1 & 15.5 & 61.5 & 37.9 & 76.8\\\\\n",
       "\t - & University of Windsor               & NA & NA & NA & NA & NA & 34.0–39.2 & 35.1 & 29.4 & 34.5 & 44.2 & 88.7\\\\\n",
       "\t - & University of Wolverhampton         & NA & NA & NA & NA & NA & 34.0–39.2 & 18.2 & 14.3 & 68.8 & 37.3 & 72.0\\\\\n",
       "\t - & University of Wuppertal             & NA & NA & NA & NA & NA & 34.0–39.2 & 26.4 & 26.7 & 52.8 & 52.1 & 47.6\\\\\n",
       "\t - & Xi’an Jiaotong-Liverpool University & NA & NA & NA & NA & NA & 34.0–39.2 & 17.8 & 14.8 & 68.2 & 38.2 & 72.4\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 13\n",
       "\n",
       "| University Rank &lt;chr&gt; | Name of University &lt;chr&gt; | Location &lt;chr&gt; | No of student &lt;dbl&gt; | No of student per staff &lt;dbl&gt; | International Student &lt;chr&gt; | Female:Male Ratio &lt;chr&gt; | OverAll Score &lt;chr&gt; | Teaching Score &lt;chr&gt; | Research Score &lt;chr&gt; | Citations Score &lt;chr&gt; | Industry Income Score &lt;chr&gt; | International Outlook Score &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| - | University of the Western Cape      | NA | NA | NA | NA | NA | 34.0–39.2 | 20.4 | 23.7 | 61.6 | 36.9 | 55.8 |\n",
       "| - | University of the West of Scotland  | NA | NA | NA | NA | NA | 34.0–39.2 | 24.1 | 15.5 | 61.5 | 37.9 | 76.8 |\n",
       "| - | University of Windsor               | NA | NA | NA | NA | NA | 34.0–39.2 | 35.1 | 29.4 | 34.5 | 44.2 | 88.7 |\n",
       "| - | University of Wolverhampton         | NA | NA | NA | NA | NA | 34.0–39.2 | 18.2 | 14.3 | 68.8 | 37.3 | 72.0 |\n",
       "| - | University of Wuppertal             | NA | NA | NA | NA | NA | 34.0–39.2 | 26.4 | 26.7 | 52.8 | 52.1 | 47.6 |\n",
       "| - | Xi’an Jiaotong-Liverpool University | NA | NA | NA | NA | NA | 34.0–39.2 | 17.8 | 14.8 | 68.2 | 38.2 | 72.4 |\n",
       "\n"
      ],
      "text/plain": [
       "  University Rank Name of University                  Location No of student\n",
       "1 -               University of the Western Cape      NA       NA           \n",
       "2 -               University of the West of Scotland  NA       NA           \n",
       "3 -               University of Windsor               NA       NA           \n",
       "4 -               University of Wolverhampton         NA       NA           \n",
       "5 -               University of Wuppertal             NA       NA           \n",
       "6 -               Xi’an Jiaotong-Liverpool University NA       NA           \n",
       "  No of student per staff International Student Female:Male Ratio OverAll Score\n",
       "1 NA                      NA                    NA                34.0–39.2    \n",
       "2 NA                      NA                    NA                34.0–39.2    \n",
       "3 NA                      NA                    NA                34.0–39.2    \n",
       "4 NA                      NA                    NA                34.0–39.2    \n",
       "5 NA                      NA                    NA                34.0–39.2    \n",
       "6 NA                      NA                    NA                34.0–39.2    \n",
       "  Teaching Score Research Score Citations Score Industry Income Score\n",
       "1 20.4           23.7           61.6            36.9                 \n",
       "2 24.1           15.5           61.5            37.9                 \n",
       "3 35.1           29.4           34.5            44.2                 \n",
       "4 18.2           14.3           68.8            37.3                 \n",
       "5 26.4           26.7           52.8            52.1                 \n",
       "6 17.8           14.8           68.2            38.2                 \n",
       "  International Outlook Score\n",
       "1 55.8                       \n",
       "2 76.8                       \n",
       "3 88.7                       \n",
       "4 72.0                       \n",
       "5 47.6                       \n",
       "6 72.4                       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tail(data)\n",
    "data[data == \"n/a\"]  <- NA\n",
    "tidy_data <- na.omit(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9369a773-8cc7-40b1-b020-a8b668f52198",
   "metadata": {},
   "source": [
    "Now when we view the last rows of our data, all NA observations have been removed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "33912430-7e4d-4b43-bfa1-33c543e6f5f7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 13</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>University Rank</th><th scope=col>Name of University</th><th scope=col>Location</th><th scope=col>No of student</th><th scope=col>No of student per staff</th><th scope=col>International Student</th><th scope=col>Female:Male Ratio</th><th scope=col>OverAll Score</th><th scope=col>Teaching Score</th><th scope=col>Research Score</th><th scope=col>Citations Score</th><th scope=col>Industry Income Score</th><th scope=col>International Outlook Score</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1501+</td><td>West University of Timişoara                </td><td>Romania</td><td>14527</td><td>24.6</td><td>6%</td><td>66 : 34</td><td>10.4–18.3</td><td>18.7</td><td>13.4</td><td>16.8</td><td>37.7</td><td>28.7</td></tr>\n",
       "\t<tr><td>1501+</td><td>Wrocław University of Science and Technology</td><td>Poland </td><td>23657</td><td>20.3</td><td>6%</td><td>35 : 65</td><td>10.4–18.3</td><td>17.9</td><td>16.0</td><td>14.9</td><td>45.5</td><td>27.3</td></tr>\n",
       "\t<tr><td>1501+</td><td>Yamaguchi University                        </td><td>Japan  </td><td>10724</td><td>11.1</td><td>7%</td><td>38 : 62</td><td>10.4–18.3</td><td>21.3</td><td>12.3</td><td>13.9</td><td>43.8</td><td>30.4</td></tr>\n",
       "\t<tr><td>1501+</td><td>Yanshan University                          </td><td>China  </td><td>38649</td><td>13.2</td><td>1%</td><td>39 : 61</td><td>10.4–18.3</td><td>17.2</td><td>10.9</td><td>18.3</td><td>46.9</td><td>17.0</td></tr>\n",
       "\t<tr><td>1501+</td><td>Yeditepe University                         </td><td>Turkey </td><td>17038</td><td>28.2</td><td>5%</td><td>55 : 45</td><td>10.4–18.3</td><td>18.8</td><td>12.2</td><td>10.5</td><td>65.7</td><td>29.6</td></tr>\n",
       "\t<tr><td>1501+</td><td>Zonguldak Bülent Ecevit University          </td><td>Turkey </td><td>28319</td><td>25.4</td><td>8%</td><td>47 : 53</td><td>10.4–18.3</td><td>13.2</td><td>8.2 </td><td>23.5</td><td>38.9</td><td>23.1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 13\n",
       "\\begin{tabular}{lllllllllllll}\n",
       " University Rank & Name of University & Location & No of student & No of student per staff & International Student & Female:Male Ratio & OverAll Score & Teaching Score & Research Score & Citations Score & Industry Income Score & International Outlook Score\\\\\n",
       " <chr> & <chr> & <chr> & <dbl> & <dbl> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t 1501+ & West University of Timişoara                 & Romania & 14527 & 24.6 & 6\\% & 66 : 34 & 10.4–18.3 & 18.7 & 13.4 & 16.8 & 37.7 & 28.7\\\\\n",
       "\t 1501+ & Wrocław University of Science and Technology & Poland  & 23657 & 20.3 & 6\\% & 35 : 65 & 10.4–18.3 & 17.9 & 16.0 & 14.9 & 45.5 & 27.3\\\\\n",
       "\t 1501+ & Yamaguchi University                         & Japan   & 10724 & 11.1 & 7\\% & 38 : 62 & 10.4–18.3 & 21.3 & 12.3 & 13.9 & 43.8 & 30.4\\\\\n",
       "\t 1501+ & Yanshan University                           & China   & 38649 & 13.2 & 1\\% & 39 : 61 & 10.4–18.3 & 17.2 & 10.9 & 18.3 & 46.9 & 17.0\\\\\n",
       "\t 1501+ & Yeditepe University                          & Turkey  & 17038 & 28.2 & 5\\% & 55 : 45 & 10.4–18.3 & 18.8 & 12.2 & 10.5 & 65.7 & 29.6\\\\\n",
       "\t 1501+ & Zonguldak Bülent Ecevit University           & Turkey  & 28319 & 25.4 & 8\\% & 47 : 53 & 10.4–18.3 & 13.2 & 8.2  & 23.5 & 38.9 & 23.1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 13\n",
       "\n",
       "| University Rank &lt;chr&gt; | Name of University &lt;chr&gt; | Location &lt;chr&gt; | No of student &lt;dbl&gt; | No of student per staff &lt;dbl&gt; | International Student &lt;chr&gt; | Female:Male Ratio &lt;chr&gt; | OverAll Score &lt;chr&gt; | Teaching Score &lt;chr&gt; | Research Score &lt;chr&gt; | Citations Score &lt;chr&gt; | Industry Income Score &lt;chr&gt; | International Outlook Score &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1501+ | West University of Timişoara                 | Romania | 14527 | 24.6 | 6% | 66 : 34 | 10.4–18.3 | 18.7 | 13.4 | 16.8 | 37.7 | 28.7 |\n",
       "| 1501+ | Wrocław University of Science and Technology | Poland  | 23657 | 20.3 | 6% | 35 : 65 | 10.4–18.3 | 17.9 | 16.0 | 14.9 | 45.5 | 27.3 |\n",
       "| 1501+ | Yamaguchi University                         | Japan   | 10724 | 11.1 | 7% | 38 : 62 | 10.4–18.3 | 21.3 | 12.3 | 13.9 | 43.8 | 30.4 |\n",
       "| 1501+ | Yanshan University                           | China   | 38649 | 13.2 | 1% | 39 : 61 | 10.4–18.3 | 17.2 | 10.9 | 18.3 | 46.9 | 17.0 |\n",
       "| 1501+ | Yeditepe University                          | Turkey  | 17038 | 28.2 | 5% | 55 : 45 | 10.4–18.3 | 18.8 | 12.2 | 10.5 | 65.7 | 29.6 |\n",
       "| 1501+ | Zonguldak Bülent Ecevit University           | Turkey  | 28319 | 25.4 | 8% | 47 : 53 | 10.4–18.3 | 13.2 | 8.2  | 23.5 | 38.9 | 23.1 |\n",
       "\n"
      ],
      "text/plain": [
       "  University Rank Name of University                           Location\n",
       "1 1501+           West University of Timişoara                 Romania \n",
       "2 1501+           Wrocław University of Science and Technology Poland  \n",
       "3 1501+           Yamaguchi University                         Japan   \n",
       "4 1501+           Yanshan University                           China   \n",
       "5 1501+           Yeditepe University                          Turkey  \n",
       "6 1501+           Zonguldak Bülent Ecevit University           Turkey  \n",
       "  No of student No of student per staff International Student Female:Male Ratio\n",
       "1 14527         24.6                    6%                    66 : 34          \n",
       "2 23657         20.3                    6%                    35 : 65          \n",
       "3 10724         11.1                    7%                    38 : 62          \n",
       "4 38649         13.2                    1%                    39 : 61          \n",
       "5 17038         28.2                    5%                    55 : 45          \n",
       "6 28319         25.4                    8%                    47 : 53          \n",
       "  OverAll Score Teaching Score Research Score Citations Score\n",
       "1 10.4–18.3     18.7           13.4           16.8           \n",
       "2 10.4–18.3     17.9           16.0           14.9           \n",
       "3 10.4–18.3     21.3           12.3           13.9           \n",
       "4 10.4–18.3     17.2           10.9           18.3           \n",
       "5 10.4–18.3     18.8           12.2           10.5           \n",
       "6 10.4–18.3     13.2           8.2            23.5           \n",
       "  Industry Income Score International Outlook Score\n",
       "1 37.7                  28.7                       \n",
       "2 45.5                  27.3                       \n",
       "3 43.8                  30.4                       \n",
       "4 46.9                  17.0                       \n",
       "5 65.7                  29.6                       \n",
       "6 38.9                  23.1                       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tail(tidy_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae056e69-df01-4af4-ac68-8b60a8f21cb4",
   "metadata": {},
   "source": [
    "To simplify data handling for future visualizations etc., we will replace the spaces in the variable names with periods. We use make.names() to do this. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "1792bb70-7708-4be4-ba79-fed53b8d123c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 13</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>University.Rank</th><th scope=col>Name.of.University</th><th scope=col>Location</th><th scope=col>No.of.student</th><th scope=col>No.of.student.per.staff</th><th scope=col>International.Student</th><th scope=col>Female.Male.Ratio</th><th scope=col>OverAll.Score</th><th scope=col>Teaching.Score</th><th scope=col>Research.Score</th><th scope=col>Citations.Score</th><th scope=col>Industry.Income.Score</th><th scope=col>International.Outlook.Score</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>University of Oxford                 </td><td>United Kingdom</td><td>20965</td><td>10.6</td><td>42%</td><td>48 : 52</td><td>96.4</td><td>92.3</td><td>99.7</td><td>99.0</td><td>74.9</td><td>96.2</td></tr>\n",
       "\t<tr><td>2</td><td>Harvard University                   </td><td>United States </td><td>21887</td><td> 9.6</td><td>25%</td><td>50 : 50</td><td>95.2</td><td>94.8</td><td>99.0</td><td>99.3</td><td>49.5</td><td>80.5</td></tr>\n",
       "\t<tr><td>3</td><td>University of Cambridge              </td><td>United Kingdom</td><td>20185</td><td>11.3</td><td>39%</td><td>47 : 53</td><td>94.8</td><td>90.9</td><td>99.5</td><td>97.0</td><td>54.2</td><td>95.8</td></tr>\n",
       "\t<tr><td>3</td><td>Stanford University                  </td><td>United States </td><td>16164</td><td> 7.1</td><td>24%</td><td>46 : 54</td><td>94.8</td><td>94.2</td><td>96.7</td><td>99.8</td><td>65.0</td><td>79.8</td></tr>\n",
       "\t<tr><td>5</td><td>Massachusetts Institute of Technology</td><td>United States </td><td>11415</td><td> 8.2</td><td>33%</td><td>40 : 60</td><td>94.2</td><td>90.7</td><td>93.6</td><td>99.8</td><td>90.9</td><td>89.3</td></tr>\n",
       "\t<tr><td>6</td><td>California Institute of Technology   </td><td>United States </td><td> 2237</td><td> 6.2</td><td>34%</td><td>37 : 63</td><td>94.1</td><td>90.9</td><td>97.0</td><td>97.3</td><td>89.8</td><td>83.6</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 13\n",
       "\\begin{tabular}{lllllllllllll}\n",
       " University.Rank & Name.of.University & Location & No.of.student & No.of.student.per.staff & International.Student & Female.Male.Ratio & OverAll.Score & Teaching.Score & Research.Score & Citations.Score & Industry.Income.Score & International.Outlook.Score\\\\\n",
       " <chr> & <chr> & <chr> & <dbl> & <dbl> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t 1 & University of Oxford                  & United Kingdom & 20965 & 10.6 & 42\\% & 48 : 52 & 96.4 & 92.3 & 99.7 & 99.0 & 74.9 & 96.2\\\\\n",
       "\t 2 & Harvard University                    & United States  & 21887 &  9.6 & 25\\% & 50 : 50 & 95.2 & 94.8 & 99.0 & 99.3 & 49.5 & 80.5\\\\\n",
       "\t 3 & University of Cambridge               & United Kingdom & 20185 & 11.3 & 39\\% & 47 : 53 & 94.8 & 90.9 & 99.5 & 97.0 & 54.2 & 95.8\\\\\n",
       "\t 3 & Stanford University                   & United States  & 16164 &  7.1 & 24\\% & 46 : 54 & 94.8 & 94.2 & 96.7 & 99.8 & 65.0 & 79.8\\\\\n",
       "\t 5 & Massachusetts Institute of Technology & United States  & 11415 &  8.2 & 33\\% & 40 : 60 & 94.2 & 90.7 & 93.6 & 99.8 & 90.9 & 89.3\\\\\n",
       "\t 6 & California Institute of Technology    & United States  &  2237 &  6.2 & 34\\% & 37 : 63 & 94.1 & 90.9 & 97.0 & 97.3 & 89.8 & 83.6\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 13\n",
       "\n",
       "| University.Rank &lt;chr&gt; | Name.of.University &lt;chr&gt; | Location &lt;chr&gt; | No.of.student &lt;dbl&gt; | No.of.student.per.staff &lt;dbl&gt; | International.Student &lt;chr&gt; | Female.Male.Ratio &lt;chr&gt; | OverAll.Score &lt;chr&gt; | Teaching.Score &lt;chr&gt; | Research.Score &lt;chr&gt; | Citations.Score &lt;chr&gt; | Industry.Income.Score &lt;chr&gt; | International.Outlook.Score &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | University of Oxford                  | United Kingdom | 20965 | 10.6 | 42% | 48 : 52 | 96.4 | 92.3 | 99.7 | 99.0 | 74.9 | 96.2 |\n",
       "| 2 | Harvard University                    | United States  | 21887 |  9.6 | 25% | 50 : 50 | 95.2 | 94.8 | 99.0 | 99.3 | 49.5 | 80.5 |\n",
       "| 3 | University of Cambridge               | United Kingdom | 20185 | 11.3 | 39% | 47 : 53 | 94.8 | 90.9 | 99.5 | 97.0 | 54.2 | 95.8 |\n",
       "| 3 | Stanford University                   | United States  | 16164 |  7.1 | 24% | 46 : 54 | 94.8 | 94.2 | 96.7 | 99.8 | 65.0 | 79.8 |\n",
       "| 5 | Massachusetts Institute of Technology | United States  | 11415 |  8.2 | 33% | 40 : 60 | 94.2 | 90.7 | 93.6 | 99.8 | 90.9 | 89.3 |\n",
       "| 6 | California Institute of Technology    | United States  |  2237 |  6.2 | 34% | 37 : 63 | 94.1 | 90.9 | 97.0 | 97.3 | 89.8 | 83.6 |\n",
       "\n"
      ],
      "text/plain": [
       "  University.Rank Name.of.University                    Location      \n",
       "1 1               University of Oxford                  United Kingdom\n",
       "2 2               Harvard University                    United States \n",
       "3 3               University of Cambridge               United Kingdom\n",
       "4 3               Stanford University                   United States \n",
       "5 5               Massachusetts Institute of Technology United States \n",
       "6 6               California Institute of Technology    United States \n",
       "  No.of.student No.of.student.per.staff International.Student Female.Male.Ratio\n",
       "1 20965         10.6                    42%                   48 : 52          \n",
       "2 21887          9.6                    25%                   50 : 50          \n",
       "3 20185         11.3                    39%                   47 : 53          \n",
       "4 16164          7.1                    24%                   46 : 54          \n",
       "5 11415          8.2                    33%                   40 : 60          \n",
       "6  2237          6.2                    34%                   37 : 63          \n",
       "  OverAll.Score Teaching.Score Research.Score Citations.Score\n",
       "1 96.4          92.3           99.7           99.0           \n",
       "2 95.2          94.8           99.0           99.3           \n",
       "3 94.8          90.9           99.5           97.0           \n",
       "4 94.8          94.2           96.7           99.8           \n",
       "5 94.2          90.7           93.6           99.8           \n",
       "6 94.1          90.9           97.0           97.3           \n",
       "  Industry.Income.Score International.Outlook.Score\n",
       "1 74.9                  96.2                       \n",
       "2 49.5                  80.5                       \n",
       "3 54.2                  95.8                       \n",
       "4 65.0                  79.8                       \n",
       "5 90.9                  89.3                       \n",
       "6 89.8                  83.6                       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names(tidy_data) <- make.names(names(tidy_data))\n",
    "\n",
    "head(tidy_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56cb1b4b-9026-481f-86f1-9cae769ce641",
   "metadata": {},
   "source": [
    "We use duplicated() to check if there's any duplicate observations. With a FALSE output, we know each observation is unique. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "c5a99b35-b408-48bc-8790-76190d74f6a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Mode   FALSE \n",
       "logical    1488 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "duplicated_rows <- duplicated(tidy_data)\n",
    "summary(duplicated_rows)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "255bc182-0f40-448b-b4b6-6a344162833d",
   "metadata": {},
   "source": [
    "In order to simplify the process of handling and visualizing data, we will convert the Female to Male Ratio to a single numerical value. Here, we will divide the female count by the total count. Ex. For a 48 : 52 Female to Male Ratio, the result would be 48/(48 + 52) = 0.48 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "121b29a0-663e-4166-90b4-4769314d1ac2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 13</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>University.Rank</th><th scope=col>Name.of.University</th><th scope=col>Location</th><th scope=col>No.of.student</th><th scope=col>No.of.student.per.staff</th><th scope=col>International.Student</th><th scope=col>Female.Male.Ratio</th><th scope=col>OverAll.Score</th><th scope=col>Teaching.Score</th><th scope=col>Research.Score</th><th scope=col>Citations.Score</th><th scope=col>Industry.Income.Score</th><th scope=col>International.Outlook.Score</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>University of Oxford                 </td><td>United Kingdom</td><td>20965</td><td>10.6</td><td>42%</td><td>0.48</td><td>96.4</td><td>92.3</td><td>99.7</td><td>99.0</td><td>74.9</td><td>96.2</td></tr>\n",
       "\t<tr><td>2</td><td>Harvard University                   </td><td>United States </td><td>21887</td><td> 9.6</td><td>25%</td><td>0.50</td><td>95.2</td><td>94.8</td><td>99.0</td><td>99.3</td><td>49.5</td><td>80.5</td></tr>\n",
       "\t<tr><td>3</td><td>University of Cambridge              </td><td>United Kingdom</td><td>20185</td><td>11.3</td><td>39%</td><td>0.47</td><td>94.8</td><td>90.9</td><td>99.5</td><td>97.0</td><td>54.2</td><td>95.8</td></tr>\n",
       "\t<tr><td>3</td><td>Stanford University                  </td><td>United States </td><td>16164</td><td> 7.1</td><td>24%</td><td>0.46</td><td>94.8</td><td>94.2</td><td>96.7</td><td>99.8</td><td>65.0</td><td>79.8</td></tr>\n",
       "\t<tr><td>5</td><td>Massachusetts Institute of Technology</td><td>United States </td><td>11415</td><td> 8.2</td><td>33%</td><td>0.40</td><td>94.2</td><td>90.7</td><td>93.6</td><td>99.8</td><td>90.9</td><td>89.3</td></tr>\n",
       "\t<tr><td>6</td><td>California Institute of Technology   </td><td>United States </td><td> 2237</td><td> 6.2</td><td>34%</td><td>0.37</td><td>94.1</td><td>90.9</td><td>97.0</td><td>97.3</td><td>89.8</td><td>83.6</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 13\n",
       "\\begin{tabular}{lllllllllllll}\n",
       " University.Rank & Name.of.University & Location & No.of.student & No.of.student.per.staff & International.Student & Female.Male.Ratio & OverAll.Score & Teaching.Score & Research.Score & Citations.Score & Industry.Income.Score & International.Outlook.Score\\\\\n",
       " <chr> & <chr> & <chr> & <dbl> & <dbl> & <chr> & <dbl> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t 1 & University of Oxford                  & United Kingdom & 20965 & 10.6 & 42\\% & 0.48 & 96.4 & 92.3 & 99.7 & 99.0 & 74.9 & 96.2\\\\\n",
       "\t 2 & Harvard University                    & United States  & 21887 &  9.6 & 25\\% & 0.50 & 95.2 & 94.8 & 99.0 & 99.3 & 49.5 & 80.5\\\\\n",
       "\t 3 & University of Cambridge               & United Kingdom & 20185 & 11.3 & 39\\% & 0.47 & 94.8 & 90.9 & 99.5 & 97.0 & 54.2 & 95.8\\\\\n",
       "\t 3 & Stanford University                   & United States  & 16164 &  7.1 & 24\\% & 0.46 & 94.8 & 94.2 & 96.7 & 99.8 & 65.0 & 79.8\\\\\n",
       "\t 5 & Massachusetts Institute of Technology & United States  & 11415 &  8.2 & 33\\% & 0.40 & 94.2 & 90.7 & 93.6 & 99.8 & 90.9 & 89.3\\\\\n",
       "\t 6 & California Institute of Technology    & United States  &  2237 &  6.2 & 34\\% & 0.37 & 94.1 & 90.9 & 97.0 & 97.3 & 89.8 & 83.6\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 13\n",
       "\n",
       "| University.Rank &lt;chr&gt; | Name.of.University &lt;chr&gt; | Location &lt;chr&gt; | No.of.student &lt;dbl&gt; | No.of.student.per.staff &lt;dbl&gt; | International.Student &lt;chr&gt; | Female.Male.Ratio &lt;dbl&gt; | OverAll.Score &lt;chr&gt; | Teaching.Score &lt;chr&gt; | Research.Score &lt;chr&gt; | Citations.Score &lt;chr&gt; | Industry.Income.Score &lt;chr&gt; | International.Outlook.Score &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | University of Oxford                  | United Kingdom | 20965 | 10.6 | 42% | 0.48 | 96.4 | 92.3 | 99.7 | 99.0 | 74.9 | 96.2 |\n",
       "| 2 | Harvard University                    | United States  | 21887 |  9.6 | 25% | 0.50 | 95.2 | 94.8 | 99.0 | 99.3 | 49.5 | 80.5 |\n",
       "| 3 | University of Cambridge               | United Kingdom | 20185 | 11.3 | 39% | 0.47 | 94.8 | 90.9 | 99.5 | 97.0 | 54.2 | 95.8 |\n",
       "| 3 | Stanford University                   | United States  | 16164 |  7.1 | 24% | 0.46 | 94.8 | 94.2 | 96.7 | 99.8 | 65.0 | 79.8 |\n",
       "| 5 | Massachusetts Institute of Technology | United States  | 11415 |  8.2 | 33% | 0.40 | 94.2 | 90.7 | 93.6 | 99.8 | 90.9 | 89.3 |\n",
       "| 6 | California Institute of Technology    | United States  |  2237 |  6.2 | 34% | 0.37 | 94.1 | 90.9 | 97.0 | 97.3 | 89.8 | 83.6 |\n",
       "\n"
      ],
      "text/plain": [
       "  University.Rank Name.of.University                    Location      \n",
       "1 1               University of Oxford                  United Kingdom\n",
       "2 2               Harvard University                    United States \n",
       "3 3               University of Cambridge               United Kingdom\n",
       "4 3               Stanford University                   United States \n",
       "5 5               Massachusetts Institute of Technology United States \n",
       "6 6               California Institute of Technology    United States \n",
       "  No.of.student No.of.student.per.staff International.Student Female.Male.Ratio\n",
       "1 20965         10.6                    42%                   0.48             \n",
       "2 21887          9.6                    25%                   0.50             \n",
       "3 20185         11.3                    39%                   0.47             \n",
       "4 16164          7.1                    24%                   0.46             \n",
       "5 11415          8.2                    33%                   0.40             \n",
       "6  2237          6.2                    34%                   0.37             \n",
       "  OverAll.Score Teaching.Score Research.Score Citations.Score\n",
       "1 96.4          92.3           99.7           99.0           \n",
       "2 95.2          94.8           99.0           99.3           \n",
       "3 94.8          90.9           99.5           97.0           \n",
       "4 94.8          94.2           96.7           99.8           \n",
       "5 94.2          90.7           93.6           99.8           \n",
       "6 94.1          90.9           97.0           97.3           \n",
       "  Industry.Income.Score International.Outlook.Score\n",
       "1 74.9                  96.2                       \n",
       "2 49.5                  80.5                       \n",
       "3 54.2                  95.8                       \n",
       "4 65.0                  79.8                       \n",
       "5 90.9                  89.3                       \n",
       "6 89.8                  83.6                       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tidy_data$Female.Male.Ratio <- sapply(strsplit(tidy_data$Female.Male.Ratio, \":\"), function(x) as.numeric(x[1])/(as.numeric(x[2]) + as.numeric(x[1])))\n",
    "\n",
    "head(tidy_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "6d14edc0-2dc9-4b9b-a742-1c2031672ee7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>University.Rank</th><th scope=col>Female.Male.Ratio</th><th scope=col>International.Outlook.Score</th><th scope=col>No.of.student.per.staff</th><th scope=col>Teaching.Score</th><th scope=col>Research.Score</th><th scope=col>Citations.Score</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1501+</td><td>0.66</td><td>28.7</td><td>24.6</td><td>18.7</td><td>13.4</td><td>16.8</td></tr>\n",
       "\t<tr><td>1501+</td><td>0.35</td><td>27.3</td><td>20.3</td><td>17.9</td><td>16.0</td><td>14.9</td></tr>\n",
       "\t<tr><td>1501+</td><td>0.38</td><td>30.4</td><td>11.1</td><td>21.3</td><td>12.3</td><td>13.9</td></tr>\n",
       "\t<tr><td>1501+</td><td>0.39</td><td>17.0</td><td>13.2</td><td>17.2</td><td>10.9</td><td>18.3</td></tr>\n",
       "\t<tr><td>1501+</td><td>0.55</td><td>29.6</td><td>28.2</td><td>18.8</td><td>12.2</td><td>10.5</td></tr>\n",
       "\t<tr><td>1501+</td><td>0.47</td><td>23.1</td><td>25.4</td><td>13.2</td><td>8.2 </td><td>23.5</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " University.Rank & Female.Male.Ratio & International.Outlook.Score & No.of.student.per.staff & Teaching.Score & Research.Score & Citations.Score\\\\\n",
       " <chr> & <dbl> & <chr> & <dbl> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t 1501+ & 0.66 & 28.7 & 24.6 & 18.7 & 13.4 & 16.8\\\\\n",
       "\t 1501+ & 0.35 & 27.3 & 20.3 & 17.9 & 16.0 & 14.9\\\\\n",
       "\t 1501+ & 0.38 & 30.4 & 11.1 & 21.3 & 12.3 & 13.9\\\\\n",
       "\t 1501+ & 0.39 & 17.0 & 13.2 & 17.2 & 10.9 & 18.3\\\\\n",
       "\t 1501+ & 0.55 & 29.6 & 28.2 & 18.8 & 12.2 & 10.5\\\\\n",
       "\t 1501+ & 0.47 & 23.1 & 25.4 & 13.2 & 8.2  & 23.5\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 7\n",
       "\n",
       "| University.Rank &lt;chr&gt; | Female.Male.Ratio &lt;dbl&gt; | International.Outlook.Score &lt;chr&gt; | No.of.student.per.staff &lt;dbl&gt; | Teaching.Score &lt;chr&gt; | Research.Score &lt;chr&gt; | Citations.Score &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| 1501+ | 0.66 | 28.7 | 24.6 | 18.7 | 13.4 | 16.8 |\n",
       "| 1501+ | 0.35 | 27.3 | 20.3 | 17.9 | 16.0 | 14.9 |\n",
       "| 1501+ | 0.38 | 30.4 | 11.1 | 21.3 | 12.3 | 13.9 |\n",
       "| 1501+ | 0.39 | 17.0 | 13.2 | 17.2 | 10.9 | 18.3 |\n",
       "| 1501+ | 0.55 | 29.6 | 28.2 | 18.8 | 12.2 | 10.5 |\n",
       "| 1501+ | 0.47 | 23.1 | 25.4 | 13.2 | 8.2  | 23.5 |\n",
       "\n"
      ],
      "text/plain": [
       "  University.Rank Female.Male.Ratio International.Outlook.Score\n",
       "1 1501+           0.66              28.7                       \n",
       "2 1501+           0.35              27.3                       \n",
       "3 1501+           0.38              30.4                       \n",
       "4 1501+           0.39              17.0                       \n",
       "5 1501+           0.55              29.6                       \n",
       "6 1501+           0.47              23.1                       \n",
       "  No.of.student.per.staff Teaching.Score Research.Score Citations.Score\n",
       "1 24.6                    18.7           13.4           16.8           \n",
       "2 20.3                    17.9           16.0           14.9           \n",
       "3 11.1                    21.3           12.3           13.9           \n",
       "4 13.2                    17.2           10.9           18.3           \n",
       "5 28.2                    18.8           12.2           10.5           \n",
       "6 25.4                    13.2           8.2            23.5           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tidy_data <- tidy_data |> select(University.Rank, Female.Male.Ratio,International.Outlook.Score,No.of.student.per.staff,Teaching.Score,Research.Score,Citations.Score)\n",
    "tail(tidy_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7d6f3aa-068a-4f16-885a-d32ed90978a3",
   "metadata": {},
   "source": [
    "Here we see that the University.Rank values in the tail of tidy_data are not exact ranks. They are all grouped in categories. Using unique(), we can get an overview of all possible values of University.Rank. Unique ranks range from 1 to 198. Ranks past 198 are grouped into one of 11 ranges.\n",
    "\n",
    "The categories are quite large. For example, the rank 1201-1500 is very broad. Therefore, including this in our exploration would not bring more insight."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "8796a4c7-1a99-4a74-aaf5-36ea653c5a27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [1] \"1\"         \"2\"         \"3\"         \"5\"         \"6\"         \"7\"        \n",
      "  [7] \"8\"         \"9\"         \"10\"        \"11\"        \"13\"        \"14\"       \n",
      " [13] \"15\"        \"18\"        \"20\"        \"21\"        \"22\"        \"23\"       \n",
      " [19] \"24\"        \"25\"        \"26\"        \"28\"        \"29\"        \"32\"       \n",
      " [25] \"34\"        \"35\"        \"36\"        \"37\"        \"38\"        \"40\"       \n",
      " [31] \"41\"        \"44\"        \"47\"        \"48\"        \"49\"        \"50\"       \n",
      " [37] \"51\"        \"52\"        \"54\"        \"57\"        \"59\"        \"60\"       \n",
      " [43] \"62\"        \"63\"        \"64\"        \"65\"        \"66\"        \"68\"       \n",
      " [49] \"69\"        \"70\"        \"71\"        \"73\"        \"82\"        \"86\"       \n",
      " [55] \"89\"        \"90\"        \"91\"        \"93\"        \"94\"        \"95\"       \n",
      " [61] \"98\"        \"99\"        \"101\"       \"104\"       \"106\"       \"107\"      \n",
      " [67] \"108\"       \"110\"       \"112\"       \"113\"       \"114\"       \"117\"      \n",
      " [73] \"119\"       \"121\"       \"122\"       \"123\"       \"124\"       \"126\"      \n",
      " [79] \"127\"       \"128\"       \"130\"       \"131\"       \"134\"       \"136\"      \n",
      " [85] \"137\"       \"139\"       \"144\"       \"145\"       \"147\"       \"148\"      \n",
      " [91] \"151\"       \"154\"       \"155\"       \"156\"       \"163\"       \"166\"      \n",
      " [97] \"168\"       \"170\"       \"175\"       \"180\"       \"181\"       \"182\"      \n",
      "[103] \"183\"       \"186\"       \"189\"       \"191\"       \"192\"       \"194\"      \n",
      "[109] \"196\"       \"198\"       \"201–250\"   \"251–300\"   \"301–350\"   \"351–400\"  \n",
      "[115] \"401–500\"   \"501–600\"   \"601–800\"   \"801–1000\"  \"1001–1200\" \"1201–1500\"\n",
      "[121] \"1501+\"    \n"
     ]
    }
   ],
   "source": [
    "unique_values <- unique(tidy_data$University.Rank)\n",
    "print(unique_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1238120-5582-449b-8906-e18bf1203a61",
   "metadata": {},
   "source": [
    "To remove these broad ranges, we use as.numeric() to convert the University Rank entries to numeric. Ranges will become NA values. We then remove these NA values using na.omit(). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "c82f620c-30ab-485b-a703-40292f772db4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in eval(expr, envir, enclos):\n",
      "“NAs introduced by coercion”\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 6 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>University.Rank</th><th scope=col>Female.Male.Ratio</th><th scope=col>International.Outlook.Score</th><th scope=col>No.of.student.per.staff</th><th scope=col>Teaching.Score</th><th scope=col>Research.Score</th><th scope=col>Citations.Score</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>192</td><td>0.47</td><td>67.9</td><td>22.6</td><td>32.6</td><td>33.3</td><td>95.9</td></tr>\n",
       "\t<tr><td>194</td><td>0.50</td><td>81.7</td><td> 8.4</td><td>35.0</td><td>27.6</td><td>94.0</td></tr>\n",
       "\t<tr><td>194</td><td>0.53</td><td>60.2</td><td>10.8</td><td>48.4</td><td>33.5</td><td>81.0</td></tr>\n",
       "\t<tr><td>196</td><td>0.51</td><td>53.5</td><td>43.4</td><td>44.6</td><td>47.5</td><td>68.8</td></tr>\n",
       "\t<tr><td>198</td><td>0.54</td><td>94.3</td><td>14.1</td><td>40.0</td><td>44.6</td><td>70.0</td></tr>\n",
       "\t<tr><td>198</td><td>0.56</td><td>93.3</td><td>16.4</td><td>36.5</td><td>39.6</td><td>78.5</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 6 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " University.Rank & Female.Male.Ratio & International.Outlook.Score & No.of.student.per.staff & Teaching.Score & Research.Score & Citations.Score\\\\\n",
       " <dbl> & <dbl> & <chr> & <dbl> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t 192 & 0.47 & 67.9 & 22.6 & 32.6 & 33.3 & 95.9\\\\\n",
       "\t 194 & 0.50 & 81.7 &  8.4 & 35.0 & 27.6 & 94.0\\\\\n",
       "\t 194 & 0.53 & 60.2 & 10.8 & 48.4 & 33.5 & 81.0\\\\\n",
       "\t 196 & 0.51 & 53.5 & 43.4 & 44.6 & 47.5 & 68.8\\\\\n",
       "\t 198 & 0.54 & 94.3 & 14.1 & 40.0 & 44.6 & 70.0\\\\\n",
       "\t 198 & 0.56 & 93.3 & 16.4 & 36.5 & 39.6 & 78.5\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 6 × 7\n",
       "\n",
       "| University.Rank &lt;dbl&gt; | Female.Male.Ratio &lt;dbl&gt; | International.Outlook.Score &lt;chr&gt; | No.of.student.per.staff &lt;dbl&gt; | Teaching.Score &lt;chr&gt; | Research.Score &lt;chr&gt; | Citations.Score &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| 192 | 0.47 | 67.9 | 22.6 | 32.6 | 33.3 | 95.9 |\n",
       "| 194 | 0.50 | 81.7 |  8.4 | 35.0 | 27.6 | 94.0 |\n",
       "| 194 | 0.53 | 60.2 | 10.8 | 48.4 | 33.5 | 81.0 |\n",
       "| 196 | 0.51 | 53.5 | 43.4 | 44.6 | 47.5 | 68.8 |\n",
       "| 198 | 0.54 | 94.3 | 14.1 | 40.0 | 44.6 | 70.0 |\n",
       "| 198 | 0.56 | 93.3 | 16.4 | 36.5 | 39.6 | 78.5 |\n",
       "\n"
      ],
      "text/plain": [
       "  University.Rank Female.Male.Ratio International.Outlook.Score\n",
       "1 192             0.47              67.9                       \n",
       "2 194             0.50              81.7                       \n",
       "3 194             0.53              60.2                       \n",
       "4 196             0.51              53.5                       \n",
       "5 198             0.54              94.3                       \n",
       "6 198             0.56              93.3                       \n",
       "  No.of.student.per.staff Teaching.Score Research.Score Citations.Score\n",
       "1 22.6                    32.6           33.3           95.9           \n",
       "2  8.4                    35.0           27.6           94.0           \n",
       "3 10.8                    48.4           33.5           81.0           \n",
       "4 43.4                    44.6           47.5           68.8           \n",
       "5 14.1                    40.0           44.6           70.0           \n",
       "6 16.4                    36.5           39.6           78.5           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tidy_data$University.Rank <- as.numeric(tidy_data$University.Rank)\n",
    "tidy_data <- na.omit(tidy_data)\n",
    "tail(tidy_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38687ad9-f12a-4b04-94f9-b601a654f5bd",
   "metadata": {},
   "source": [
    "## Creating Visualizations:\n",
    "Here, we plot University Ranking vs. the Female to Male ratio. In the plot below, there is no evidence of a relationship. Gender ratios do not appear to affect ranking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "a23b7647-5041-4d89-b0bb-0aeda3dcf2e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2V7RwAAAPhMlT2VinBML5gTC5adMyY6+fxzzz03Z3PtbKlSx7dWquOVxhZlvKxc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DQDgsCIeAcCgIh4JwKE29tPldu5t/q9xE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\nRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 1080
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(tidy_data, aes(x=University.Rank, y=Female.Male.Ratio)) +\n",
    "       geom_point()+ scale_x_continuous(breaks = seq(0, 200, by = 5)) + labs(x = \"University Rank\", y = \"Female to Male Ratio\", title = \"University Ranking vs. Female to Male Ratio\") +\n",
    "       theme(axis.text = element_text(size = 12),  \n",
    "             axis.title = element_text(size = 20),  \n",
    "             plot.title = element_text(size = 25))  \n",
    "\n",
    "options(repr.plot.width = 18, repr.plot.height = 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "786c854c-ae63-474c-b55d-ffa0c0fa9701",
   "metadata": {},
   "source": [
    "We then plot Research and Teaching Scores vs. University Ranking. In the visualization below, research scores have been colored blue and teaching scores are red. There is a strong pattern of both scores with ranking. This is to be expected, as ranking is based on the overall score (of which Research and Teaching scores are factors in). \n",
    "\n",
    "The relationships do not appear to be strictly linear, as a slight curve is visible. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "1c27cbab-a81c-436a-9fa8-67450a5f17c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TiBHPhw4pf/0V4df/oYec6enB5Y0pU8If0nQJU523YuQAACAASURBVKcGd3X99d77\n7w+emA4AAAAAkLAuOeXX9u3bi/L4qVOn5uXlrVu3btKkSZUrV27VqlVQg8aNG19VggAAoIzK\nnTvX9Prr+o8+kk+d8jdo4Bk3ztmjR3E6kuWc994zTZum//xzOTPTm55uHzeucLUmUWhCfbvT\nluDK6iF3KITQaCKxtkkBer36ySc5U6aYvvlGl5cnNWvmfeYZ+803R2xisY4d3cuW5bz6qmnb\nNk1ysr97d/czz9hK8pUDAAAAAJQylyyoNAk1EcdlqKo6ZMiQkPGwkwIAAAlANZlskybZJk0S\nQhgMBkmSxKUWVrliV0lJtsmTbSU5sVVp4e7YUbtmTeFgye2xY0fP9OnBwRYtvKmpkfwSqOze\nrdm8+VqTafakm/0zK+TH5cxM7YYNUl6et1kzb3p6MXretk2zfbvGavW3bevp2NHdsaM7clkn\nEI9H/PKL9tgxpXp1X5s2nkuV2QAAAACgVONcBwAAoOxwDB+uW7lS++uv+RFf/fr2p58uuT3e\neKNn+HDHvHnG/IjFos6cmRuxHfj9ljFjDEuWBLZUs9k2ZYqzXz8hhP6jjywTJki55/fluu++\n3NmzLzlkphC3W3r0UcuKFfrAZkqKOmNGXo8erohlnjD271cGDUras+f8ajbp6b53382pXTvE\nmjQAAAAAUKpd8oSzQ4cO0cwDAAAAEaDVnvv8c8P77+vWrRNut6d1a+fQoarReOUHXoXJk23t\n2nk+/1yfmSk3auR99FFH5cr+SHVunDMnv5oihJBsNvOECd5GjYTBYBk7VnJeWONE/9FHvpo1\n7ePHF7HnqVNN+dUUIUR2tjRypKVRI2/I1elxKV6vePhha341RQixa5cybJj1u++yFeUyjwMA\nAACA0ueSBZXVq1dHMQ0AAABEiFbrfPhh58MPR3OfXbu6u3YtkcmyDO+9FxSRXC7DkiWq0Viw\nmpLfuIgFFVUV779vCAra7dKHH+onTrQXO9sE9Pvv2p07g88ptm7V/O9/mlatvMXoUNm1y/Dh\nh/LJk76aNZ0DB/qvuSYSaSIytm/XfPSR/s8/5Tp1fAMHOitViljpFAAAACgVmPILAAAA8Uv+\n668QwVOnVLM5RPzMGeH3C1m+Yrcul5STIxWOnz595ceioL/+Cv2KFe+V1C9bZh0zRrjPF+eM\nb7+d8+GHntati58fImfxYsOYMZb8zblzjZ98cq5Fi+KUzQAAAIBSijNGAAAAxC9fjRohgrVq\nhY5Xr16UaooQwmBQQ95cX6sW832Fp0aN0K9Y7dphj12QT5+2jB+fX00RQkh2u3XECOHxFD8/\nRMixY/LEiRdVMfPypEcftfoZowIAAIBEQkEFAAAgZqTffxfdu6c1bpzatq158mQpLy/WGcUd\nx9ixQRE1Kck5dKhz4EA1Le2KjS9jzJjgqb0qVfL373/RovQ+n1iwwHDrrSnp6Wnduyd/9ZWu\n6P0niMaNvbfdFjzbW9eu7vT0sAcuaNetk2y2oKB87Jhmx47i54cIWbNG53AED+o6dEjZu5el\ncgAAAJBAijPlV3Z29pYtW06fPm2321VVvXzjQYMGFScvAACAsk7z+++a3r2F0ykLIU6dMu7d\nq9mw4dznnwsNk7Je4OrdW/7rL9PUqVJurhDCV6dO3owZvmrVhBA5ixdbxoxR9uwRQqhms/2p\np5wPPlj0nocMcZ47J8+caQxcJr7uOu/MmXnlyl10v/2zz5rnzzcGfj5zRt60Sfvaa3mDBgWv\n3ZLIJEnMmZP31FPmL77QBzZ79XJNm1ac0mDhRXEuH0c0ORyh405niKnzAAAAgLIqvNP1I0eO\njB49esWKFT5fUSdDoKACAAAQkmXCBHHxlWLtpk2GZcucffvGKqX45Bg2zPnQQ8qeParF4qtR\nQyjn74j3tGqVtWaNcvSodO6cr0ED1RC8yPzlSZIYO9b+6KOOffuUlBS1enWfdPGV4V27NPnV\nlHzPP2++916XxXKFm4oSSlqa/9//zs3MtB06JNeq5Q8qShWdt3nzwkFVr/c2anR1CSICmjcP\nMeTIbFYbNGCWPAAAACSQMKb8On36dNu2bT/77LOiV1MAAAAQmsej2bq1cFjz22/RzyX+qXq9\nt2lTX+3a+dWU82TZV7Omt1mzcKsp+UwmtVkzb40awdUUIcT//hfi3iOHQ9q1iyFEIZQr52/Z\n0lvsaooQwtuokbPQzVj2555Tk5KuKjNEwg03eB94wBUUfOklm9FIcREAAAAJJIyzwenTpx8/\nfjzwc5MmTRo3bpycnKwozJkLAAAQPkURGk2I1bb1+lhkgxC02tBXinU6riCXlLxXXvHVrm34\nz3/kY8d8des6Hn3Udc89sU4K5/3zn7n16nk/+MBw4oRcv75v5EhHz57BJRYAAACgbAujoLJy\n5UohhNVqXbFiRYcOHUosJQAAUHZIubm61avlkyd99eq527cPHl6QyGTZ3amT7ttvg8Luzp2L\n3sf69dodOzTly/tvucVTvnzxRwYgpFtu8RiNatBC3Ndc42/U6JLLrUu5ubpVq+Q///TVq+fu\n0EHIYQwHhxBCaLWOESMcI0bEOo8i2blT8+uvGq1WtGnjqVmz7A/i1+nEE084nnjiEqupAAAA\nAAlAuuKq8vksFovNZnvmmWdeeeWVEs0pmrzeS54PlwqKokiS5PP5iv4+lhmKoiTg7HOyLMuy\nzDueOCRJUhTF7/f7/Ql3nTTwxBPwUNdoNKqqlpmjXVq7VunXT5w8GdhUmzf3ff65uOaawi1l\nWRZCJNyhfuyYpk2b/NdHCOEfNsz/1ltFeWhurrjnHmXVqvPX+lNSxLx5vrvvLjV/MpIkBf6j\nxTqRK/j3v+VHH71QFDEaxRdf+P72t9Cvs7RmjdKvn/jzz8Cmev31vs8+CzrgE/lfuUajKe3f\nvfOpqhg5Uv7Xv84fG3q9mDjRP3FiiE8w/pUn5qFelv6VF50sy6qqJuA7HjgrLzOfb0UnSZIk\nSYn54cZ1mISSsF/e/H6/TqeLdRZAPAqjoKLVar1e78cff3xPGRp3n5mZGesUrorFYtHr9dnZ\n2Yn2L02WZavVeu7cuVgnEm0Gg8FsNufl5blcCTfBQkpKSnZ2dqyziDaNRpOcnOx0Om02W6xz\niTaLxeJyuTyFZ0Mq68qVK+f1esvG55uUnZ3Stq186lTBoKdDh5xPPinc2GAwSJLkcCTcjc9J\nqqqdN8+9YYOamuru2tXdtWsRHzh6tGXx4osmBzOb1TVrztWoUTq+Emg0GqPRmJubG+tErmzL\nFs2SJfpjx+R69XxDhjirVw995UjKzk5p00Y+fbpg0PO3v+V8/HHBiMlkMhqNOTk5Cfj5lpqa\nmpWVFessIuPddw1PPmkOCi5dmtu5szsoqNPprFar3W5PxM+3pCSbzZaA5ympqalut7tUfL5F\nlslk8vl8iXmeIklSmfl8KzqdTqfVahPzPIXrMAklYa/DaLXaJNaxA0IJY8qvihUrnjhxQqMp\nU4twlo3ycgLeBxR4von2rPMl4DsekIDPOv8pJ+BzFwl8qIuy8o7rfvghqJoihND+/LN09Ki/\nWrWgeOJ+sCcni0mTcs6cCWxlZ4l33jFu365JSfHfcYe7a9fgi7MBbrdYtiz4fjGbTVq+XDd6\ntL1kE46QUvSON23qadr0QvHjUinrvv8+qJoihNCuXi1lZPivvTYonrCfb2XmWQeVM/ODt93m\nEkLovv9e99VX8tmz3uuu8z32mLBaE/kdT7Qnzpe3xHziIiHf8VL0r7wkJODRnrDveP4TT7Tn\nnmjPFyi6MKojnTp1Wrx48Z49e0ouGwAAUGbIlxgGKp85U7igAiFERobcpUvKmTPnJxFassQw\nZIjz1VfzCrfMzZXdbqlw/MyZEEFEx+UO+EIFFZR2mZkhVscJ/AGan33WOG9eIKL7+mt14UKx\ncaOoXDmq+QEAAAAoGWGskzl69GhZlufPn59oY9wAAEAx+GrVChFVFF/NmtFOpZR48klLfjUl\nYMECw+rV2sItU1P9qakhbhmrUyexpp6IK6EPeI3GV6NG1HNBiatdO8TfWp06Pu369fnVlAAp\nM1MMGxatvAAAAACUrDAKKjfccMOsWbP27dvXp0+fnJyckssJAACUAe6OHb2tWgUFHcOGqamp\nMcknwvx+w6JFKe3bl6tRI/WWWwwLFogw59E+fVoeNcpSq5YmLU306ZP0xx+an38OserjDz+E\nCMqyGDcueGqvmjV9993HXS8x4+7UyduyZVCw7BzwuNjYscF/gCaTOnKkQ/fTTyFar14t7KVj\nLj4AAAAAlxfGlF8+n2/QoEFWq3XUqFF169bt379/69atK1asePlVVdq1a3fVSQIAgFJIo8n5\n978t48frvvlGCCG0WsfQofZnn411WpFhmj7d9NprgZ+V3bstEyYox47Znn++iA+326VevZL3\n71cCmz/9pFu/XhuyIhNyai8hxCOPOOx2aeZMo90uCSFat/bMmJFnsTDTcexoNDkLFpTVAx5B\n2rTxzJ+fO2mS+dQpWQhRu7bvtdfy6tXziZBD+VVV8nqFNsRoMwAAAAClSxgFlaDCyeuvv16U\nR7GEEQAACctfpUrOokVSdrZ88qS/Vi3VYIh1RldLzsyUDx0SBoOp0Bch4xtvOAcOLOL8Tv/6\nlzG/mhLgdEoWi5qXF1w+adnSI0KRJDFmjP2xx+wHDyrly6vly/uL/CRQUuLtgHe5pH37FJ1O\nrV3bd9k7oFAcvXq5evRwHTmiaDSiWrXz5VDvDTeEaJqeriYlCYcjqvkBAAAAKAGcWgEAgJKl\npqT4UlJincXVks6ds4wfr//0U3Hpm0U0W7YUsaCyZUuI72CFO27TxnPPPZebxUunEw0bsm5K\nfImTA37xYsNLL5mzsyUhRLVq/tdey7v1VneskyprZFnUqnXRH6CrZ0/D4sXan3++qN3cuVFN\nCwAAAECJCaOg0qFDB4PBoNFoFEWRpNCzTwAAAJRJltGj9V9+efk2RR+RoNeHqMqYzeqnn557\n7TXTtm2alBT1jjtco0c7FKVwQ+AKfvhBN2aMJX8zI0MePNj63XfZlN9KnCznLFpknD1b/9VX\n0tmz3saNPc88Y+7QQdhssc4MAAAAQASEUVBZvXp1iaUBAAAQv5T9+69cTUlN9bRuXcQOu3Z1\nf/KJvnDw+uu9S5bkFCdFoICZM41BEYdDevtt48yZeTHJJ6GoRqN9wgT7hAmBTZ1OF9t8AAAA\nAESQHOsEAAAA4p1y5MjlG6h6fe6sWWpSUhE77N3bdd99F83l1aCB74UXuIcdkXHkSIiRTYcP\nM9wJAAAAAK5KSa2hcuzYsTfffLN58+YPPPBACe0CAAAgOvwVK4aMu+65R6iqv3p1Z9++vlq1\nwurzrbdye/Z0rV1rcbnkRo3y+vd36XSXXJ0FCEvFiv4//wy+capSJX9MkgEAAACAMqOkCipn\nz5599dVX69WrR0EFAACUdt7Gjb0tW2p++61g0NewYe7s2eIq5vPp2tXdp49Pq5XPnHEWjGt/\n/VW/aJFy/Livdm3Hww/7GjYs9i7KJCk72zhvnuZ//1MtFs9ttzn79BEyo64vMnCgc9w4S1Dw\noYecIRsX0bZtmoULDUeOKNWr+wYOdDZv7r2a3gL++kueO9e4fbuSnKx27+7u1cvFQo0AAAAA\n4lmJFFSysrLmzJkjhMjIyCiJ/gEAAKJKknLnzbMOHKjZvj0Q8NWrlzN//tVUUy7F8O67lqee\nCvysXbPGsGRJzoIF7ttvj/iOSin59OmUW2+V//wzsKn/4gvd11/nvPee4Ep8AQ895Ny7V5k3\n7/xKKgaD+vzz9rZtPcXu8LPP9MOGWf9vS7t4seHNN3P79HFd7jFXcuiQ0qVLSna2lL+Ln37S\nzp7NKi8AAAAA4lfYBZVjx47NmjXrxx9/PHHihNMZ4jY3r9drs52fAbxy5cpXmyAAAEAc8FWv\nnv3DD9oNG5TDh33VqnnatBFabcT3Iv/5p/m55y4Kud2WJ57I2rJFNRgivrvSyDxxYn41JUD3\n9df6jz5y3X9/rFKKT5Mn24YMcf7+u0anEzfd5KlcufjzfeXmSoXHu0yYYOnc2ZOWVvxun3zS\nkl9NCVi61NCzp/u229zF7hMAAAAASlR4BZVVq1b16tUrNze3iO379+8ffkoAAABxSVE87dp5\n2rUruT1oN2yQCt2wIp89q9m61XPjjSW331JE9/PPIYKrVlFQKax2bV/t2r6r7+f33zU5OcED\ngGw2aeNGzR13FLP44XaLdetClCR/+klLQQUAAABA3AqjoPLXX3/dd999RammlC///9m777im\nrvcP4M/NHoTtoi4UB4parThaq2DdSIerzoqiVesCW63WilZb69a6qq1+xYGjYt0/3KsO7LQ4\nUFFUUBHZI5B9f3/cNo0hQAJhKJ/36/v6vsi5J+c8uWCa3Oee87h7e3sPGTJk7NixpYgNAAAA\noIrRFVKXorD2KsjiqcD5KUt6veXt1AprtwbLMgZLi1tKMyYAAAAAAEBZs6GA58aNG9PS0oho\n0KBBly9fzsjIePbvfgv5+fm5ubnXr1+fP3++q6tr7dq1165dO2HCBIGgrIreAwAAALx6dG3b\nFmxkpVJdixblH0zlpPX1tdDYvn35R1J1vP66TiRizRqFQmrTpuRFWcRitlUrC2mw9u1LPmbl\n9OQJ79w5io+34WsXAAAAAABUWjZ8sj927BgRtWvXbvfu3R07dnR2dhaLxdwhiUQil8t9fHzm\nzJlz/fp1g8HQrl27X375pUxCBgAAAHhF6T0980JDzVsNBklEREWEUxkpFy5k5XLTFl2bNqqR\nIysqnqrAzc0wZ06eWeOMGUoPj5IXUCGipUtzxeIX8jR+ftr33y9VoftKJSuLCQ5WNGvm4O9P\nLVtK33/fKTERaRUAAAAAgJebDZ/pb9++TUQTJ05kmKJW4nt4eBw9elQoFL733nvcihYAAAAA\nsFLerFnKOXOI99+HNEatls+ZIz5woAKjqjz0Xl6ZZ86oBw7UN2iga9kyb9q0rJ9/JqGFahxg\nR+PH52/dmt25s7ZOHcPbb2s3b84JCckv5ZitW+tOnsx69111/fr61q11s2crd+zI5r1CGYdP\nP3U4dEhsfHjpkjA42FH7qq3AAQAAAACoWmzYkisrK4uI6tatW/CQTqcz3d2rdu3a48ePX7Zs\n2caNG7/44ovSRwkAAABQVTAMPyGBCtSXkK5Zo37//QqJqLLRN2iQs359RUdR5fTpo+nTx87l\n4r29dZs3F1+g8WWUmMg7eFBs1vjXX4KLF4X+/kiqAAAAAAC8rGy4B4xLmahUKmOLXC7nVquk\np6ebde7duzcR/fTTT3aIEQAAAKAq4SUmFmzkJySUfyQAUDKJiXyb2gEAAAAA4KVgQ0LFzc2N\niOLj440tQqHQxcWFiB4/fmzWuXr16kR07949O8QIAAAAUJUYatYs2KivVav8IwGAkqlZ03KB\nmVq1SlV4BgAAAAAAKpYNCRUfHx8i2rJli0bz32L/mjVrElFUVJRZ58TERCIy7QkAAAAA1lCN\nHMmKzTcLUgUHV0gwAFACDRroC27t1bixvnNn7PcFAAAAAPASsyGhEhgYSES///57jx49jhw5\nwjW2a9eOiJYuXXrz5k1jT51Ot2zZMiKqUaOGPYMFAAAAqAJ0bdooly5lFYp/HotE+ZMmqUaO\nrNCgAMA2a9fmdOz4X/qkaVP95s3ZYjFbgSEBAAAAVCmRkZEMw9SvX78Ezz1w4ICVz7W+J7wa\nbChKHxQUtHDhwqdPn54/fz47O7tv375ENGzYsPDw8KysrPbt23/44Yfe3t7p6ekHDx68desW\nEXXu3LmsAgcAAAB4damGDFH37Cn84w9GpdK2bm2oXbuiIyobGg3x+cQv76oSajUjErEMU87T\nQtVSvbrh4MGsW7ekjx/Lq1dXtWiRK7DhuxcAAADAqyYwMPDIkSN+fn5nz54toltycnLt2rV1\nOt22bdtGjBhRbuEBWMmGD/VyufzAgQN9+vRJTU2t+e/W3t26dQsMDDx8+LBSqfzf//5n2l8k\nEs2YMcOewQIAAABUGayrq6Z794qOoqwILl8Wzpvn/tdfrECg7dxZOX++vmHDcpj32DHRwoXy\nu3f5Egnbo4fmq6+UqGkBZYdhqHVrfZcupFTq8/MrOhoAAACACjVmzJgjR46cP3/+wYMHnp6e\nhXXbvn27TqdzcnIaMGBAKWfs0aPH9evXRSJRKccBMGXDll9E5Ovre+vWrXnz5vn7+xsbIyIi\nCv59u7m57du3r1WrVnaIEQAAAABeIYK//5b368f89hvpdIxKJTpxwun993np6WU97+nTohEj\nHGNj+Xo9KZXM/v3igQOd8vOxUAUAAAAAoMwFBATUqlWLZdmtW7cW0Y07OnToUKlUWsoZHR0d\nfXx8GjduXMpxAEzZvOy8WrVqc+fONW1RKBR79+6NiYk5efJkUlKSWCxu0aJFYGCgXC63X5wA\nAAAA8IqQL1jAqNWmLbxnz6Rr1ihf/JBpd2Fh5p9O79zh71inmdjmF4Ozs755c1YsLtMAKpW0\nNN7163yplHx8dHI5CnsAAAAAQNkSCAQjR45ctGjRtm3b5s6dy1jagfePP/64ceMGEY0ZM6bc\nAwSwim0rVIrQsmXLTz/9dNmyZd98883gwYORTQEAAAAAi/i3bllovHmzTCfVaikuzkKxlntL\njjl++KFzz54uHTsKf/mlTGOoPJYvl7Vq5TJwoFPfvk5t27ocPlyFMkkAAAAAUFGCg4MZhnnw\n4MH58+ctduCWp7z++utt2rThWi5evDho0KA6deqIxWKZTObt7T19+vT0F1e3//zzzwzD+Pn5\naTSaiRMnVq9e3cvLiwovSm/NmKa2b9/evn17R0dHhULRsWPHXbt2WfNik5OTZ82a5ePjI5PJ\nHBwcmjVrNn369JSUFLNuz58/nz59uo+Pj1wul0gknp6eH3zwwYkTJ6yZAiqE3RIqAAAAAADW\nYB0cLDQqFGU6qUBAEomFdRiObBb3Ay8x0XH0aN6TJ2UaRmWwZ4940SKZWv3PLYGpqbxPPnG4\neRMF0wEAAACgbHl5eXXu3JmIwsPDCx7VarVcrsK4PGXjxo2dO3feu3evWq329fX18vK6f//+\nsmXL2rZta5qZ4KqkKJXKpUuXrl+/PiUlJTc3t7AYrByTw+fzv/76648++igpKalp06YikSg6\nOnro0KFLliwp+pVeu3atVatWixYtevz4cfv27Vu2bPnkyZNly5Y1a9bs+vXrxm5Pnjxp3br1\nsmXLHjx40Lx5844dOxoMhgMHDvTs2XPlypVFTwEVpWwTKn/88UeZjg8AAAAALx3Ne+9ZaHz/\n/TKdlGHo3Xc1BdsHUOR/fTIzJdbdblb+4uP5a9ZI582T794t1mhKVfdlwwbz3ahVKmbrxFvi\nn38mna40IwMAAAAAFI1LlkRGRhbMeRw5ciQ1NVUqlQ4bNoyIlErltGnTWJb97LPPkpKSLl68\nGBMTc+vWrTp16jx48ODrr782PlEoFHL9v//++4ULF8bHx1++fNni7NaPyUlLS1uyZMnBgwcT\nEhJ+/fXXZ8+ejR07lojCwsKeFH4nllqt7t+/f3Jy8rhx4xISEs6ePXv58uWEhIQhQ4akpqYO\nGjRIq9VyPZcsWfL06dNOnTo9ffr0119/PXv27KNHj/bu3SsQCGbNmpWRkWHr6YVyYFVCRafT\nnTx5cu7cuRMnTpw5c2ZkZGR+fn7RT1Gr1bNmzWrfvr09ggQAAACAV0fe9Om6N980bVGNGqUO\nDCzreb/+Otfb+4WEwXwKe4sumbbwHj8u6zBKICJC8vbbzvPny9etk06erOjUyTkpqeT3RT19\namHrs6c3cxTjxjl368ZkZpYiUgAAAACAogwYMMDZ2VmpVO7du9fsELdspX///s7OzkSUmJjY\nt2/fLl26hIWF8fn/fIL18vIKDQ0lomPHjhmfyB2NjY0NDAycNWuWp6dngwYNLM5u/ZicrKys\nkJCQd999l3soFApXr17t5uamVqsLxm+0c+fO+Pj4Fi1arF+/3tHRkWt0cnLasmWLh4fH7du3\no6KiuMa///6biAYPHuzk5GR6itasWbNo0aJir8BDhSh+af/FixeDg4Pv3r1r2ujq6rp8+fKg\noCCLT7ly5cro0aNv375tlxABAAAA4FXCikS5R444nDih/eUXVijUvvOOtkOHcpjX2Zk9fTpz\n/35xTIzAyYkduKFv2+yzZn0MtWuXQyQ2uX+fP3Om3HRVyoMH/JAQhz17sks2YK1a+vR0828B\ndSmBiAQ3bzrMnp2zbl2JowUAAAAAKIJEIhk2bNi6devCw8NHjRplbE9JSeHSDMb9vpo2bbpn\nz56CIzRq1IiInj17VvAQt7SlCCUYc+jQoWbx+/v7R0ZGRkdHFzbL0aNHiWjAgAE83gt3QYnF\n4t69e2/evPnUqVNcksbFxYXr//HHH3PrbDjjx48v+oVABSomoXLx4sUePXoUzIalp6ePGjUq\nOTn5888/N23Py8v78ssvv/vuO4PBYOdIAQAAAOCVwTCGDz5QvvNOOU8rFNKgQepBg9REJON3\noG9fSKiwTk6qwYPLOaRiRUWJVCrzPb7OnhVlZjLOzhaqwhRr/HjV5MkvlLGRkGoCfc/9LDp4\nkFavJr6FVSwAAAAAAKU3ZsyYdevW/fLLL/Hx8calJBEREVqt1svLq0uXLqadMzMzT548ef/+\n/WfPnqlUKiLi9trS6/UFR27durU1AVg/plgsbty4sVkjV/H+wYMHhY1/48YNItqxY8eZM2fM\nDj18+JCIjEsXpkyZcvTo0aioqObNm48YMaJXr15vvPGGWRoGKpuiEioajWbkyJFcNkUmk/Xp\n08fT0zM9Pf3EiROJiYlE9MUXX3Tt2tXX15frf/78+eDg4Pv373MP3d3dly5dWsbxAwAAAACU\nRN7UqbxnzyRbtnAPDa+9lrNqVSVcoZKTY6FiCstSJ07wfQAAIABJREFUTk4JEyqDB6sePeKt\nXi3TaIiI3ChtHU1sTX9xRxm1mtFoWKl5nRUAAAAAALt4/fXX27Rp8+eff27duvWrr77iGrdu\n3UpEwcHBpj3XrVs3c+bMIirMm5JIJHK5vNhuNo3p6upaML3B7eKVk5NT2LO42idxcXFxcXEW\nO2RlZXE/+Pv779u3LzQ0NC4uLiwsLCwszMXFJSAgYPLkye3atbMmQih/ReW7Dhw4EB8fT0RN\nmjSJjY3du3fvkiVLNm3adP/+/ZCQECIyGAzffvstEeXk5HzyySf+/v7GbMrIkSNv375d2J5g\nAAAAAADW0mqZwr+ulByfn7tkSfq1a9kREVlHjmRER2v9/Ow/S6l5e1u4+c7Vla1Vq+Qrwj//\nPO+vv9L3Bf10mt55QJ4f0n/7Hug9PZFNIaLMTAZL7gEAAADKCLev17Zt21iWJaKYmJhr164J\nBALTi8n79++fNGlSbm5ucHBwdHR0ZmYmy7Isy548edLimGKxuNh5bR2Tb2ndNsMwxv+3iDu0\ndu1athBXrlwxdg4MDLxz586xY8cmTZrUsGHDjIyMHTt2tG/fPiwsrNiXAxWiqITKwYMHuR92\n7txZt25dY7tQKFyxYgW3/Or48eNHjhxp0aLF999/z/0DaNKkydmzZ8PDw93c3MoycgAAAAB4\nxfETEhw/+si9Xj23Bg1cfH3F/346tSPDa69pevTQtm/PSiR2H9wu+vZV+/rqzBrnzFEKii+G\nWJTq1Q1d5nfs0uSJgl5IVin/vUmwajIYaMMGqbe3a6NGbvXru02b5pCRUehXZQAAAAAomaFD\nh8pksocPH54/f57+XZ4SEBBQs2ZNY581a9YQ0aBBgzZt2tS+fXtj2fa8vLwSz2vrmMalJKay\ns7OJyLSMvBnuqnhSUpKVUfH5/J49e65Zs+bevXs3btwYOXIkES1YsODixYtWjgDlqaiEyu+/\n/05Ebdu2bdOmjdkhhmGmTJlCRHl5eYGBgY8ePSIisVg8d+7cv//+269S3twHAAAAAC8RRql0\nHDhQFBVFWi0R8R8+VIwZI4qKqui4yptAQNu2ZQ8erJLJWCKqU0e/alXu8OGq0o/MSqVZe/ao\n33uPFYuJSO/llbN5s6Z379KP/PJau1Y6Z448NZVHRPn5zPbtkjFjHLFUBQAAAMC+nJycBgwY\nQER79+5lWfann34ik3L0nDt37hBRnz59zJ576dKlEs9r65g5OTmPHz82a7x37x4RNWzYsLBZ\nWrRoQUS//fZbCSJs3rx5eHh43759iejEiRMlGAHKWlEJlWfPnhFR27ZtLR598803TR/6+/vH\nxMTMmzfPmtVVAAAAAABFk2zfzo+PN2uUV8n1E+7uhjVrch88SHv4MO3PPzOGDbNDNoVjeO21\nnE2b0h49Snv0KOPKFfW779pr5JdRXh6zdKnMrPHCBeGZM6IKiQcAAADgFcaVSzlw4MDVq1cf\nP37s4eHR+8U7exwcHIiIKxpv9OzZs02bNlEhRemLVYIx9+3bZ/pQrVafO3eOiN56663CZuHS\nIadOneJK0JsaOHDgkCFDuLzOkydPxo8fP3z48IIjuLq6EhFX2hwqm6ISKlxpHXd3d4tHq1Wr\nxv3g7u6+ZcuWM2fONG7c2O7xAQAAAEDVxL9920Lj/fsMV0u96uHxSC4vSRX64vH5rMw8kVAF\nPXrEU6ksbPAVG2th72wAAAAAKI3OnTs3btz46dOnn3/+OREFBQWZFSxp2bIlEYWHhxvzH7Gx\nsd27dw8ICCAilUqVmJho66TWj8nVtpDJZN98841xrYnBYJg1a1ZKSoqDgwO3wsaigQMHNmrU\nyGAwfPjhh8aNv1Qq1cyZMyMjI48ePcpdV3dxcYmMjIyIiPjyyy9NcyfR0dGHDh0iIuwCVTkV\nlVDh/m4s1t4xbf/0009RfB4AAAAA7It1dLTQKJOxQmH5BwNVgaOj5XyVs3PZ5LEAAAAAqjZu\nkcqFCxcYhuF+NjVnzhw+nx8dHd24ceP333+/Y8eOPj4+devW3bhxI7cAwM/Pb/To0TbNaP2Y\nOp2OiOrXr//xxx936NChbdu2AQEBDRs2XLlyJREtX768sEUIRCQWiyMjI2vUqPHrr796enq+\n/fbbXbp0qV279uLFi0UiUWRkJLcARSaTbd26VSKRfPPNNzVq1Gjbtm2XLl0aN27csWPHzMzM\nIUOGcGkeqGyKSqgAAAAAQPnjPX4sXbXK4fPPpevX89LTi+7Mf/RIxnXeuJHJzCyfCMuH+r33\nCjb+8vb0bxbKZ82S794t1mptHlOjoV27JLNmyRculP35Z+mqusMr57XXDO3amf9VKRRs9+5V\ndFEUAAAAQJkaOXKkUCgkIn9//wYNGpgdbdmyZVRUVKdOnVJTU8+ePavRaFauXHnw4EGpVPrD\nDz/UqVPnyZMnti5SsX5MbsmIk5PT119//eOPPzIMc+HChZSUlLfeeuvAgQMff/xxsRNdv379\n888/9/Ly+vPPP6OjoxUKRXBw8LVr13r06GHsFhAQEB0dPX78+Jo1a8bFxV2+fDkrK6tnz547\nd+6MiIiw6aVBuWG4ZSiWjzEMEc2dO3fevHlFdPj2229nzpxZNuGVudTU1IoOoVQUCoVYLM7I\nyCjZvoEvLx6P5+jomPlqXTayhlQqlcvlOTk5arW6omMpby4uLhkZGRUdRXkTCATOzs4qlSo3\nN7eiYylvCoVCpVJpS3C59CXn7u6u0+mq4PubRCJhGKYKbhHr5OQkFApNP5CIjh9XjB3L/Hsq\nWBeXrN27dW3aWHy6+NAhh08+Yf79j4LBzS173z5d8+ZlHXbpCQQCmUyWnZ1ddDfp2rXyb7+l\nf/f4mlVj06Lk/+5c8/bWHz6c6eRk7eqBjAwmMND5zp3/ll9Pn543Y0aejbGXilwul0qlWVlZ\nVfD9zdXVNb24BGGFe/SI36+fY0LCP38kMhm7bl1u374l/9wlEokcHR2VSmXVfH/Lzc2tgt9T\nXF1dNRpNse9vrx65XK7T6arm9xSGYSr/+5vdiUQikUhUNb+n4DpMlSKRSBwcHKrgdRihUOjk\n5FTRUQBURrgvDwAAAKCyYDIyFJMnMyYXXpmMDMXHH2dER5PA/GMb7/lzh5AQxuSrHS8tTTFu\nXMaFC8R7RVYh50+apOneXXTiBC87+4yo56IlvUyPxsbyv/zSYc2aHCtH++ILB9NsChEtXSrr\n3FnboUOVy21AYerV01+6lHnggCguTlCjhqFvX7WHh6GigwIAAAAAgMoCCRUAAACAykJ46RJT\nYDUe/9EjwfXrutatzTufP8/kmOcS+Hfu8O/e1TdtWoZRli99kyb5TZoQ0YFZDgWPHj4sWrPG\nqnFYlo4cEVkcodiECpOTI1u1Snj+PGm1urZt8z77zFCrllWzwktIImEHD1YT2ecuVI2Gli2j\nyEhJRobEx0cXGprXtGnVuqMZAAAAAOBVgoQKAAAAQGXBFLJvhsV2mzq/AnJzmYKN+fmMXk98\nfsEj5nQ6UqstjGBxWFOMSuUcEMCPjeUeCm7dEh05knnuHHIqUCyWpY8+kh07RkR8Irp7l3/0\nqOjIkazXX9dVdGgAAAAAAFASr8h2EAAAAACvAL2Pj4VWgUDfrJmVnVmRSN+4sd0Dqwx8fCxc\ng/b21lmTTSEioZCaNLGwMqBFi2KWC0i//96YTeHw0tPlX31l1axgHa2Wnj7lFboXvVbLe/qU\nDC/f1ltHj4qPHXvhDja1mvn0UwtrrQAAAAAA4KVQ/AqVtWvX7t69u4gOK1asCA8PL+zo7du3\nSxAWAAAAQBWk8/FRDR4sefGjV15oqMHNrWBnra+v+r33xAcPvtB55kzW0bFso6wgH32k2rZN\ncvfuC/mTBQuU1o8wf37uoEEvlNb09tYPH64q+lmC6OiCjUJLjVACWVnM/Pny3bslGg1Jpey4\ncarp0/NEIpY7ymRkyOfNk+zdS1otK5fnjx+f9+mnJBRWbMzWu3LFwretmBhBXh4jk7HlHw8A\nAAAAAJRS8QmVtLS0tLS0IjqkpKSkpKTYLyQAAACAqku5dKmhTh3J9u28Z8/0deuqxo/PHz26\nsM65a9YYPD3FO3fyUlL09erlT5yo+uij8oy2PEmlbGRk1vz58hMnRHl5TPPmutmz895+24Z6\n8v7+2l27shculMXGCuRytlcvzZw5SomkuOvalpbAsDys87YDlqVJkxTHjv1T2yY/n1m1SqpU\n0sKFSu6wYvx40Zkz3FFGqZQtX86o1cq5cysqYFsJLH3ZYhjCnw8AAAAAwEsKNVQAAAAAKhFW\nIsmbMSNvxgzSaEhkoYj6C52lUuXs2crZs63p/AqoVcvw/fc5RCV/ud26abp109j0dG2XLqKT\nJ80b/fxKMj286PffBcZsitHmzdIpU/Jr1jQIr1wxZlOMpN9/nz9pksU1W5VQly7a9eulZo0d\nOmiLT+MBAAAAAEClVFRC5WSBr44AAAAAUE5syhhUgWyKqVK+XJuenh8cLDp8WHj1qrFFX6eO\ncs6cUkUARER0756FLyMGA8XF8WvWNPDv3rXwHL2ef//+y5JQ6dpVM2yYNiLivz3KFAp2xYrc\nCgwJAAAAAABKo6iESrdu3cotDgAAAACAykggyNq/XxoeLjx/nlGrte3a5Y8fzyoUFR3Wq8DZ\n2XKdeRcXlohYFxeLRw3OzmUYk72tW5ffu7cwMlKXksK2aKGbODG/Zk3LrxoAAAAAACo/bPkF\nAAAAAEBExOTnS7Zv51+/zrq5aXr10nbo8M8BoTB/7Nj8sWMrNLpX0Ntva2vWNDx79kJFkebN\ndc2a6YhI4+dnqFaN92KxRl3r1vpGjUo23Y0bgv37xc+f8xo31o0YoXJ2Lo99txiGhg2j999X\n5+fnl8N0AAAAAABQppBQAQAAAAAgXnKyc+/evMRE7qF03bq8adPyZs2q2KhebQ4O7A8/5AQF\nKdLT/8mp1Kmj/+GHHK5mO+vklLNhg+OYMUxGBndUX79+zoYNxDAlmGvLFsmMGQ7/PhKvXSs9\ndCirSRN9qV8EAAAAAABUIUioAAAAAACQw/TpxmwKR7ZihbZrV2379hUVUlXQsaP26tWMqCjx\n48e8Bg30AQEa04Lt2s6d06OjxVFRvKdP9V5emj59WLG4BLM8eMAPC5ObtqSn8z75RHH6dGZp\nXwAAAAAAAFQlSKgAAAAAQJWn1YpOnizYLIqKQkKlrDk7s0OGqAo7yrq6qoYNK+UUp0+LVCrz\ndS0xMYLHj3m1a6OiCQAAAAAAWItXfBcAAAAAgFcao9ORTmehHXUvXgl5eZbb8/NLsnsYAAAA\nAABUWVihAgAAAABVHSuV6r28+PfumbXrWrWqkHjAvlq2tJAtc3Ji69VDDRUAAACAcpKTk1MW\nwyoUirIYFqAwWKECAAAAAEC5CxeatejeeEM1cGCFBAP21aWLtndvjVnj118rRaIKCQcAAAAA\nAF5WSKgAAAAAAJDW3z9r716dry8rkRhq1lQFB2ft2kVCYUXHBXbAMLRhQ05ISF6dOnqRiFq0\n0G3enDN4cKGFWwAAAAAAACyqRFt+6fX6iIiIffv2NWzYcMWKFWZHlUrlzp07r169mpaW5ujo\n2LZt2+HDh7u4uFjfAQAAAADKSGYmc/GiKC2NadZM7+urrehwiDQa0S+/8B49Mnh6ajp1sjIv\novXzy/TzK+PIqrDHj8XHjzNKpe6NN3Te3nYcOCGBf/WqQKdjfH21Xl6Wd/GSydjZs/Nmzy6k\nmgoAAAAAAIAVKktCJTExccWKFU+fPrV4VKfTffnll/fv33/zzTd79uyZlJR05syZmJiYlStX\nOjg4WNMBAAAAAMrIiROiyZMV6en/1Pf289OGh2fL5WxFxcO/fdsxKIh//z73UN+kSfbWrfqG\nDSsqHiAiyf/+x5s3T5Gfzz1UDRmSu2oV8eywXH7VKtnSpVKN5p8/v/Hj8xcsUJZ+WAAAAACA\nquDUqVPdu3d3cnLKzMys6FheDjZ8h9m4cWNWVlZZBJGXlxcaGsowzKpVqwQCCzmeo0eP3r9/\nPygoaObMmQMHDpwyZcqnn36anJz8008/WdkBAAAAAMrCkye8CRP+y6YQ0blzwi++kFdYQFqt\n49ixxmwKEfHv3FGMHUt6lB+vMILffnP4/HP6N5tCRJJdu6Tr15d+5FOnRN98IzNmU4howwbp\nrl2S0o8MAAAAAK8YBwcHxhKxWFynTp2BAwcePXq0omN8iZ07d2706NHNmzdXKBQCgUChUDRt\n2nT48OHHjh2r6NDszIaEyvjx42vWrDl06NATJ04YDAY7BqHX6/v06bN06dJatWpZ7HD27Fmp\nVBoYGGhs6dSpU61atc6ePcuyrDUdAAAAAKAsHDwozs5mzBr37pWoVOaN5UP422/827fNGgXX\nrwv++qtC4gEikuzcaaFx27bSj7xjh4XcybZtSKgAAAAAgGVisVj+Iq1W+/jx48jIyL59+w4b\nNkyPO7FspNPphg8f7u/vv2XLltjYWBcXl4YNGwqFwjt37kRERPTu3XvIkCGv0lm1bZW9SqXa\ntWtXz54969evP3v27Li4OLsEoVAoRo8ezefzLR7VaDQPHz5s3Lix8MXNr5s1a5aVlZWcnFxs\nB7sECQAAAAAFpaZa+Dyp1VJGRsUkVHipqZbbU1LKORIwsvhLsctv5PlzC39mFhsBAAAAAIho\nw4YNuS9SKpV//PHH4MGDiWjnzp2LFy+u6BhfMgsXLoyIiBCLxatWrcrIyEhISLhz5056enp8\nfPzHH39MRLt37/7uu+8qOky7sSGh0r9/f6lUyv2cmJi4cOHCxo0bd+rUafPmzTk5OWUTHhFR\namqqwWBwd3c3a69evToRJScnF9uh7GIDAAAAqOLq17dwq5FCwbq723NBs/X09etbbm/QoHwD\ngf9Y/KXoPT1LP7Knp4U/swYNKuZvDwAAAABeRlKptE2bNrt27erfvz8RrVq1yr6bM73yNm7c\nSERz5syZOnWqk5OTsd3T03Pjxo3Dhw8notWrV78yi1RsKEofGRmZm5t76NChPXv2HD9+XK1W\nE9GlS5cuXbo0ZcqU/v37BwUF+fv7M4yd7wjLz88nIonEfOU+15KXl1dsB9PGKVOm6HQ67ufO\nnTtz/05eXtyyHoVCUQV3NuPz+ab/RKsIHo9HRDKZrOAf/CuPx+NVwd84944qEomq4Gvn8/l8\nPr8KvrlR1X5/E4lEFR1IeeOqx73Uv/GgIFq3jo2Pf+ET4IwZBnf3ol4UwzBl9cbeqRPbpw/z\nf/9n2mb44AOHdu3sP5ftuA9vcrm8Sr2/MdOm0a5d9GI5RiYsrLR/AFrtFx4Rh3nD8g1i0+bZ\ns5nK82+Ke3OTSCSv3vubRkNr1vB27OA9e8Y0bsx+9pkhMPCFSw8CgcDBwaGiwqtYAoGg8vwR\nlhs+ny8Siarm9xSGqURvO+WmDP9TXrnhOkxFR1Hequx1mKqZUQgJCdm3b19KSkp8fLyXl5ex\nPTk5edWqVYcPH46Pj+fxeHXr1g0ICJgxY0a1atVMn/78+fOlS5dGRUU9ePBAr9fXqlXr9ddf\nnzBhQo8ePcwmsnJAIrp48eLq1auvXLny/PlzPp9fr169vn37zpo1y9XV1djn559/7t+/f5cu\nXU6cOBEaGrp3715HR8d79+5xR58+fbpkyZL/+7//S0xMlEgk7dq1CwsLe+utt8wm4t7cjh8/\nvnjx4mvXruXn5zds2HDMmDFTp04t+mq/Wq1++vQpEXXs2NFihwULFgQEBLRq1Yr712RkZWAJ\nCQlLly49ceJEYmIiwzC1a9fu0aPH9OnT69ata+UZsP5sW4stkczMzC1btvTq1cushnz9+vXD\nwsLu379fsmFZlu3Xr19oaKhpy7179wIDAzds2GDWc9++fYGBgVeuXCm2g2lj+/bt3/jXokWL\nShwnAAAAAHBu3WLffJMlYolYiYT98ktWr6/QgFJT2Q8//CcghmFHjGAzMio0IGDZ8+fZJk3+\n+aW4uLAFPrqXxKhRLNFR6lOPHnID13DM27nTDgODNUaO/Of3afzfli0VHRMAAADYiVartfuY\n2VY4eza3Uydd/fr6Dh10hw/nWfMU6wOQy+VEtKXwjyw3b97kLnH//fffxsa//vqrRo0aROTk\n5OTn59exY0dHR0cicnd3j4mJMXZ7/Pixh4cHEclkMl9fXz8/P+MV/xUrVpjOYuWALMtu2LCB\nS2ZUq1btrbfeatGiBVfwwtPT8/nz58Zuhw8fJqK2bdt+/fXX3Iw1atTgDl29etXNzY2IHBwc\nfHx8jDs8LVu2zPj0kydPEpGHh8f27dt5PF7t2rXbtWvHPYuIwsLCij2xzs7ORLR69epiexpZ\nExjLsufPn1coFETk7Oz8zjvvvPnmm9y5cnR0vHTpkjVnwPqzbT2GLV1GPS0t7eeff96zZ8+5\nc+eMy3YYhuncufOoUaMGDBjA/aVar3///vXq1VuxYoWxJSkpady4cV27dg0JCTHtGRERsWfP\nngULFlSvXr3oDq1atSpsutRCdtl+WSgUCrFYnJGR8cqsmbISj8dzdHTMzMys6EDKm1Qqlcvl\nOTk53BKxKsXFxSUjI6OioyhvAoHA2dlZpVLl5uZWdCzlTaFQqFQqrVZb0YGUN3d3d51OVwXf\n3yQSCcMw3KrTKsXJyUkoFL7sH0g4SUm8tDRew4Z6qbT4j5cCgUAmk2VnZ5ddPExGBj8hQV+v\nHuvsbNqu0TD37/McHdnXXiuz2+70en5iIun1+nr16MXbj+RyuVQqzcrKqoLvb64uLlnXrlFe\nnr5hQyr1cg3BH3849+rF/awnfjw10JKwkcPT7NjrbGW6gVQkEjk6OiqVylfs/e233wR9+jib\nNTo6srdupYvF/7wDODk55ebmVsHvKa6urhqNpkzf3yonuVyu0+mq5vcUhmHS09MrOpDyJhKJ\nRCJR1fyeguswVYpEInFwcKiC12GEQqHdFyQVWzNi7VrRF1+8sPJ46lTNggXFnHnugrs1HBwc\nlErlli1bgoKCLHY4cODABx98QETZ2dncsGq1ulmzZvHx8ePGjVuyZAl3LT4rK2vChAm7du1q\n2rRpTEwMl+SYOnXq6tWrO3XqdOTIEeOpi4yMHDJkCJ/PT0pKcnFxsWlApVJZvXr1vLy8zz77\nbNGiRdwKknv37nXt2jUxMXHKlCnGqiTHjx/v1auXt7d3dnb2xIkTBw8ezLJsgwYNlEply5Yt\n4+PjJ0yYsGLFColEwrLs+vXrJ02axOfzr169+sYbbxDRqVOnunfv7ubmxuPxvvvuuyFDhhCR\nVqv9+OOPw8PDHRwc0tLSil5sPXTo0F27dslkstWrVw8fPlwsFhfRmXtp1gSWnZ3dtGnTpKSk\noUOH/vDDD1yWIS8vLygoaO/evXXr1r19+zZXoKSwM2D92baJbUXpC3Jzcxs7duypU6cSExOX\nL1/epk0bIuLSR0FBQTVr1vz444+vXbtWmimqVavG5/NTClStTEpKIiIPD49iO5RmdgAAAACw\nRq1aBh8fnTXZlPLBurjoWrUyy6asWydt3Ni1c2eX1193fecd55gYG/a/tZLo9GkXX18XX1+X\nDh1cW7USHzhg9yleVgyjr1dP7+1d+mwKEQn++sv4M5/0jSiuGd0S5mby794t/eBQrGvXLHzz\nzM5m4uL45R8MAAAAvAJycmjOHPML8atXix48KO3laytx1/SJqFWrVsYkzc6dO+Pj41u0aLF+\n/XrucjwROTk5bdmyxcPD4/bt21FRUVzj33//TUSDBw82TUQNGDBgzZo1ixYtMt5bY/2AiYmJ\nffv27dKlS1hYGJdNISIvL6/Q0FAiOnbsmHEW7mhsbGxgYOCsWbM8PT0bNGhARBEREfHx8Z6e\nnmvWrOH2rGMYZuLEib169dLr9eHh4aYvPy0tbdSoUVw2hYiEQuGcOXOIKDc39+HDh0WfuiVL\nlnh5eeXl5Y0ZM6ZWrVojRoz44YcfYmNjC1vIYWVg27dvT0pKqlat2o8//mhcsyGTyX788UeF\nQpGQkLB///6iz4D1Z9smdvuLrFWr1rRp0/74448dO3ZwCTciys3N/fHHH1u3bt29e/erV6+W\nbGSBQNCwYcO7d++apoJZlr1x44a7u3u1atWK7VCa1wUAAAAAr4YdOyTz5smVyn+2AI6JEQwd\n6piWZs9vaILYWEVQED8xkXvIe/5cMXas8MoVO04B/yjkxje2uBviwC5EIstfj43LUwAAAABs\n8tNPwoLrvliWwsNtXkBgq/z8/CtXrgQEBJw8eZJhGOO2UUR09OhRIhowYIBZ/Q+xWNy7d28i\nOnXqFNfCXQ8/evSo2Ur08ePHh4SEGO/4t37Apk2bcptCmS3BadSoERE9e/as4AsZNmyY6UMu\nW/Duu+8a8zGczZs3371795tvvjF7+rhx40wfNmjQgFu9wS1aKELt2rWvXr0aEhLi4OCQkZGx\nY8eOcePGNWvWrEaNGiNHjjx9+rRZfysD45JGgYGBMpnMtJuTk1PXrl2JqODIZmfA+rNtE7vd\nlHf9+vU9e/bs2bPHWO+FiHg8HlfC6NSpU6dPn544ceLy5ctLUI+xe/fu69at+/nnn41ZsmPH\njqWnpw8dOtTKDgAAAABQxS1fLjNrSU7mbd8uCQnJs9cU0rVrGZXKrFG2cmVWIRUaocS0Xbqw\nEonZ2dZ7euobNaqokKoUPz+tWMyq1S9UKG3QQO/lVbU2wAEAAAB7ycmxXPm8sPYSGzVq1KhR\noyweEolE3333Xd++fY0tN27cIKIdO3acOXPGrDO3buPuv8ujp0yZcvTo0aioqObNm48YMaJX\nr15vvPGG2XV8mwbkZGZmnjx58v79+8+ePVOpVET05MkTIrK462Dr1q1NH966dYuIvLy8zLpZ\n3M9JKBTWq1fPrNHR0TEtLc2arWtdXV1Xrlz57bffnjx58syZMxcuXPj7779TUlK2bdu2bds2\nf3//3bt3V69e3abAuG4+Pj4Fp2vatOnBgwdNQq1+AAAgAElEQVRv375t1m52Bmw921YqbUIl\nNTV1x44dW7ZsiYmJMW2vX7/+qFGjRo8enZWVtXr16i1btmi12rVr1yYmJu7fv58rp2N048aN\nP/74g/tZr9enpaVt3bqVe9ivXz+FQtG9e/dz587t2rUrPj6+YcOGiYmJFy9erFevHrerHREV\n2wEAAAAAqjKNhh4/trAY5f59e65Q4T94ULCRFx9vxymAo69bVzlvnsPMmcYWVibLWb+eCnxr\nhbJQr55+3ry8WbP+q5cpl7Pff5/D2PmKBwAAAFQVvXrpwsIsLDXu1Utn34kcHR25whscrVbL\n1cGaMWPGxIkTjZXkOVw137i4uLi4OIujZWVlcT/4+/vv27cvNDQ0Li4uLCwsLCzMxcUlICBg\n8uTJ7dq1K8GARLRu3bqZM2daWa1KIpGY1TLnXpeVNWZkMpnZepESkEgkgYGBgYGBRJSbm3vh\nwoXt27fv2bPn7Nmzffv2jY6O5jJMVgbGFW1ydjav20dE3L5qZlWdCp4Bm8629Ur4fUOv1x89\nerR///4eHh6hoaHGbIpIJBo4cODx48fj4+PDwsJq167dvHnzjRs3Xrt2zdvbm4gOHjz4448/\nmo12+/btff8yGAwZGRnGh9xfDI/Hmzt37gcffPDgwYM9e/bcunWrT58+CxcuNJa4KbYDAAAA\nAFRlIhE5O1vYjKhGDXvuUGRwdy/YyP57KxbYlyo4OPPEifwxYzQBAXkhIRnR0bq2bSs6qCpk\nzJj848czx4xRBQRoQkLyrlzJaNPGztc7AAAAoOpo2tTg52f+WcLHR9+jh50/YHz33XfPTDx5\n8oRbKpGdnW2WTSEiblXA2rVr2UJcMdndNzAw8M6dO8eOHZs0aVLDhg25za/at28fFhZWggH3\n798/adKk3Nzc4ODg6OjozMxMrsPJkyctvq6Cl8G57IVpjYzy5ODg0KdPn127du3evZuIfvvt\ntyNHjtgUGFP4rTpcdZaCG3lZHMHKX5/1bF6hcvv27fDw8G3btpntnta8efPg4OCPPvrIzc2t\n4LOaNWt2+vTpZs2aZWZm/vDDDx9//LHp0QEDBgwYMKDoeSUSSRELsqzpAAAAAABV2fDhqrVr\npaYtEgk7cKD5Dl2loRo+XFSgsKFqxAg7TgGmdK1b615c1w/lqU0bXZs2Vt0yCQAAAFCsn3/O\n/+QTyf79QrWahELq0UO3eXPxm02VkkQi2bBhQ7du3TZu3Dho0CB/f3/To25ubsnJycUWETHi\n8/k9e/bs2bMnEd28eXPp0qVbt25dsGBBjx49OnXqZNOAa9asIaJBgwZt2rTJtD0vz9r9il1d\nXZ8/f84tB6lAgwYNmj179r179/766693333X+sBcXFzS0tK4VSZmuEZjHffC2Prrs5INK1Q2\nbdr01ltveXt7L1682BiHg4NDcHDw5cuXb9y4ERoaajGbwqlVq1ZoaCgRxcbGljJoAAAAAABb\nzZqV16ePxvhQoWBXrsxt0sSeJR80PXrkzZrFmpQMzB8/XvVvkT8AAAAAACiMQEA//KBKScnJ\nzs5JS8vZtStfZl4DsUy88847I0aMYFk2ODhYqVSaHmrRogUR/fbbbyUYtnnz5uHh4VxFlhMn\nTtg64J07d4ioT58+Zu2XLl2yMgBuv6iChUbu3LmzY8eO//u//7NynKKtXLnSz89v1qxZRfQR\nCAREZCysbmVgXPWU69evFxyQK45isbyKqdL8+opgQ0Jl7Nixly9fNj5s3779jz/+mJSUtGnT\npo7Wldl84403yJY0GgAAAACAvYhE7Nat2SdOZC5dmvvjjzlXr2YMGGD/9e9506ZlXr6cs2ZN\n7qpVGVeuKBcssPsUAAAAAABgRytWrHBzc3vw4MFMkxJ9RMSlQ06dOsXVMDc1cODAIUOGcGmP\nJ0+ejB8/fvjw4QVHdnV1JSJjXXcrByQiBwcHIuIK0Rs9e/aMW7BisSi9GS4Zc+jQoZycHNP2\n+fPnjxgxYteuXcWOYI3k5OTz58+vW7fOYuaDiH799Vcud+Lr62tTYFy3I0eOmGW5UlJSzp07\nR0S9evUqOjbrz7ZNbK6h4u7uHhIScuPGjejo6DFjxnC/WiuJxeIaNWpwOSgAAAAAeFkwSqV8\n/nyXNm3catd27tGj4K5WL5HWrXVBQar331dXq2Yooyn09eqpBw9WDRum9/IqoymIKDaWP3So\no5eXW5MmrmPHKhISSltDsso6dEj8zjvOtWu7tW3psKLDSWk9b/eaNd1r1HB5/XXZ4sX79/D8\n/Z1r13Zr29Zl6VKZSoWq6wAAAACvGnd39+XLlxPRunXrLly4YGwfOHBgo0aNDAbDhx9+aNyx\nSaVSzZw5MzIy8ujRo9WqVSMiFxeXyMjIiIiIL7/80pg7IaLo6OhDhw4RkZ+fn00DElHLli2J\nKDw83JhTiY2N7d69e0BAAPeUxMTEol/U0KFDX3vttYyMjCFDhnB7ZLEsu2nTJi5jMW7cuBKc\nqNDQ0A4dOsyZM8fY8tlnn9WqVSsnJ8fPz2/t2rWm+2slJCQsWbKE2wDtzTff7Nq1q02BDR06\ntG7duqmpqePGjTMWXMnOzg4KClKpVC1atOBORRGsP9s2saGGSvfu3YODgz/44AORySYGhTEY\nDAaDgcfjmRaH6dat27Nnz0oQJQAAAABUGJZVjB4tOnOGeyT46y/Hjz7K2bRJ/d57FRtXVfbw\nIT8gwDknh7u4zxw4II6OFp47l+nmVlZZolfV7t2SyZP/uUXsUZLkWxoSS9L99AER8Z882b4s\nZzy5/nP0EX/JEtndu/wff8wpdDgAAAAAeDmNHDly27ZtZ86cCQ4OjomJkUqlRCQWiyMjI3v0\n6PHrr796enr6+vryeLybN2+mpaWJRKLIyEhuAYpMJtu6deuAAQO++eab1atXN27cWC6XJyUl\nxcXFEdGQIUOMl/6tHJCI5syZs3///ujo6MaNG7dp0yY5OfnXX3/t1avXxo0bo6KiUlNT/fz8\nunTp8r///a+wVySTySIjI3v27Hn06FEPD4+GDRsmJyenpqYS0dy5c7maLraKjY29evVq/fr1\njS3u7u4nTpzo169fXFzc5MmTJ0+erFAoZDJZRkaGRvPPfst+fn6RkZHGNIGVgcnl8p9++qlX\nr14RERFRUVEtWrRQqVQ3b97Mzc197bXXfvrpJz6/mFvKrD/bNrFhhQqPx9uyZUtaWpo1nRcu\nXCgUCrllNQAAAADw8hIdP27MphjJZ80iK5aZ2wujVAr++ot/9y7pdKbtOh3FxfH//FOQm1u1\n1g18/bXs32zKP549461YIa2oeF5SGg3NmSM3azxA75+k7kSUT9LptNT86AHxlSvCcooPAAAA\nAMrRhg0bJBLJvXv3vvjiC2Njy5Ytr1+//vnnn3t5ef3555/R0dEKhSI4OPjatWs9evQwdgsI\nCIiOjh4/fnzNmjXj4uIuX76clZXVs2fPnTt3RkREmM5i5YAtW7aMiorq1KlTamrq2bNnNRrN\nypUrDx48KJVKf/jhhzp16jx58qTYRSodOnS4fv36uHHjuKi0Wm23bt2OHj06b948+5wyIiLy\n8fG5ceNGeHj4Bx980KBBA71en5qaKpFImjVrNnLkyKioqLNnz5pVXrcysPbt28fExEyaNMnV\n1fXq1as3btzw9PScPXt2TExM06ZNrYnNyrNtE4ZlWWu7MgwRxcXFeVmxd8HWrVuDgoJee+21\nx48flyyy8sHlvl5eCoVCLBZnZGRYs3Heq4TH4zk6OmZmZlZ0IOVNKpXK5fKcnBzjSreqw8XF\nhVsGWKUIBAJnZ2eVSpWbm1vRsZQ3hUKhUqm0Wm1FB1Le3N3ddTpdFXx/k0gkDMOYLo6uIpyc\nnIRCYSX/QCJbskS21PyyMhGl//mnoU6dko0pEAhkMll2drY1naXffy9bvJhRKolIX79+7ooV\n2rffJqKrV4VTpzrcv88nIqmUnTYtLySksv8JyeVyqVSalZVVyvc3X1+Xhw/Nb8hq31575EhW\naYYtU66urunp6RUdxQvu3eN37OhSsP1bmjWTFsVQy1b0d8GjCxYox4+39i9NJBI5Ojoqlcqq\n+f6Wm5tbBb+nuLq6ajQaK9/fXiVyuVyn01XN7ykMw1S297dyIBKJRCJR1fyeguswVYpEInFw\ncKiC12GEQqGTk5N9xzQrmGEvCoWiLIYFKIzNNVSsdPfuXSKycjkLAAAAAFRarLSQdQ8yWTnM\nLt63Tx4Wxvxbh5D/8KHjyJH8R4+ePuWNGOHIZVOIKD+f+eYb+Y4dknIIqTKQSi3cFFUuv5BX\nikRi+d4yGeURkZQsp0AsnnwAAAAAAKgKiqmhsmjRIrOWjRs3mq3QMaPT6eLi4nbv3k1Eds9k\nAgAAAEA503TrJp8/36xR6+trKPIzob3IvvvOrIXJyZFs2rRVtjwjw3ybr1WrpMOHq8ohqgrX\nu7cmNtb8k3zv3pX9xkmWpbNnRTExfGdntmtXbd26pbi312AQnTghuH3b4Oameecdg4dHCcao\nXdvQooXu+vUXzqSU8ntTFBF50b3mdPMmNTc9KpGwXbtWudWTAAAAAADAKSahMmvWLLOWZcuW\nWT/6W2+9ZXNEAAAAAFCZ6L29lfPmyU12szW4u+euXVs+s/MePSrYyH/0KEFuoQJhQgLfYCBe\nWa3BrkQ+/TT/l19Ev/3234f5Xr00I0dW6mSSUknvvedkLEAiFrPffKMsWcxMRobToEGCa9e4\nh6xMlrtihbp//xIMtW5dznvvOZsm55bS9EYUR0QMsdtphL/0Sla+mDskErELFyrr1Klae7wA\nAAAAAIBRMQmVcePGcfVedC/W/7SGt7f3qlWrShoYAAAAAFQW+RMnajt2FB86xEtO1jVvrhox\ngi2vhciG6tX5Dx+aN9asWV1qKNi5enVDVcimEJFIxB4+nLl3r+TqVQGfT126aPv2VTPmK3Yq\nl+nTGdNy7mo1M3u2/I03dD4+Nn/RcJgxw5hNISImL88hNFTXpo3e09PWoby99dHR6du2Se7c\nEdSsof/Q8ajv/RRdXGtWJNK1a+fZv//Vmspt2/R37/Jr1TIMGKBu1szmaAEAAAAA4JVRTEJl\nw4YNRJSXl/fHH3907tyZiD777LOit/wiImdnZy8vL39/fz7fwp2DAAAAAPDS0bVpo2vTpvzn\nVY0aJZ8717SFFYtVw4cPEan+9z+JSvVCDmHUqEq9RMO++HwaPFg1eHBFx2EdlqWICPOEj1rN\n/Pyz2NaECpOfLz5ypGCj6NCh/KlTSxCbqysbEmIsl+KfQ/6mR93IEBqaV4JhAQAAAADg1VNM\nQoUjk8nefvtt7udx48Z5eXmVZUgAAAAAAP/IHz+eHx8v2bqVe8gqFMqFC3UtWzYl/Xff5c6Y\n4ZCV9c9l+sGDVSEhuPBdSWm1TG6uhfb0dJuX1TA5OWRp9TwvI6MEgQEAAAAAAFjPqoQKZ+7c\nuUTk6upaZsEAAAAAALyIx8tdtix//HjBtWusVKrr2NHw78fRfv3Ufn6aq1eFublMq1a6xo1R\n2aKC6PVU3MJ0kYitX58KbN5GJfitGdzcWBcXpkD6RN+4sa1Dvdqs+LVUupnKL2YAAAAAgBKx\nYZPpefPmzZs3DwkVAAAAAChnei8v9YABmoAAw4ufRV1d2d69NQMHqpFNKX+8pCTFhAluXl7u\ndes69+4t/OWXovt//TVr1lK3rn74cNt3aePzlZ9/btam9/ZW9+tn81CvIo2GVq2StW7tWquW\ne9u2Lhs3Sm2vhmkVJi9PvmCBq4+Pu4eHS8eOkp07iTX/FVspK4v54gt506autWu7d+7scuCA\n2L6hAgAAAADYS6ErVG7fvk1EEomkfv36pi22atq0aYkCAwAAAACASorJy3Pq149/7x73UPD7\n7079+mUdOaJt376wpwwZwqakKBculKWk8BiGOnfWLl6c6+hYkkvwqtGjGZ1Otnw5k5FBPJ6m\ne/fcb79lJZISvphXy5dfOmzZ8s+pePSI/+WX8tRU3uzZSrtPpPjkE9HRo9zP/Hv3HKZOZZTK\n/LFjbR3HYKBRoxx/+UXIPYyN5Y8dq9BoaNAgtT3DBQAAAACwh0ITKt7e3kTUqlWra9eumbbY\nii3pbUoAAAAAAFA5Sf73P2M2xUj+5ZeZJ08W8azhw1XDh6uSkngKBevgUIqvCQyTP25c/scf\n854+ZV1dWam05EP9S6+nu3cFqalMo0b6mjUN/8yTkyOIjWX5fL23NyuTlX6Wsnb3Lt+YTTFa\ns0Y6enR+rVoGO04kvHTJmE0xki1YoBo2zNYTFRUlMmZTjObMkffrpxbYsEE1AAAAwMvt1KlT\n3bt3d3JyyszMLLpnZGTkwIED69Wr97DgjrpQ9mzY8gsAAAAAAICIBDdvWm604m6qWrUMpcqm\nGDGM4bXX7JJNuXlT8M47zp07O/fr59SihWtoqINGw0jCw11btXIKCHDu1culdWtxZGTpJypr\nN29aSEHo9XTrlp1TExb/AJj8fP79+7YOZTHm9HReUhKqqQAAAIB9rFq1irFO3759KzrYl8O5\nc+dGjx7dvHlzhUIhEAgUCkXTpk2HDx9+7Nixig6tzBX6wfqtt94iokaNGpm1AAAAAABAFWdx\nFQIrkxHDlH8wpZSTw3z0kSIh4b/L9zt2SJwyEtYenW5s4aWnO4SEGOrV0/r6VkSM1pLLLWeq\n7JPBMlHYMhRWLrd1KJnMcmyFvRYAAAAAW/F4PD7/hXs1DAYDt6+SWbvZw8qpR48e169fF4lE\nFTK7TqcLCgqKiIggIoZhateu7eHhkZKScufOnTt37kRERAwePHjHjh0vxZksmUITKhcvXiy2\nBQAAAAAAKorwwgXh77+zPJ727bd1b7xRnlNr+vaVbNtm1qi26w19ej393/+Jb97kOzuzPXtq\nPD31dhzc1OHDYtNsCmdTlOdiksvpv9IjjFot+f77Sp5Q6dhR6+ZmSEt7YR+C114ztG5t58L0\nmq5dWZmMycszbdQ1b65v0MDWoXr21CxaJFOrX0jFvfmm1tXVnnuUAQAAQFU2ZcqUKVOmmLbM\nnDlz8eLFb7zxxu+//15RUZWYo6Ojj49PRc2+cOHCiIgIsVi8ePHioKAgJycnrv3BgweLFi36\n4Ycfdu/e7evrO23atIqKsKxhyy8AAAAAgJeNXq8YNcqpf3/Zt9/Kv/nGuVcvh5kzy3N+jb9/\n/iefmLbovL2V8+fba/yMDKZbN+fRoxXLl8vmzJF36uS8dWtZ1Zx//NjCdyK1QZhEtcwa+YmJ\nZRSDvSgU7Nq1uVLpf2s7HB3ZDRtyRCI7r/Ywe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rsphM8rx5sjAJGQAAIABJREFUWWFh5fM7/b//9jIrtcUiJSVJNWsG55T3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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 1080
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(tidy_data, aes(x = Teaching.Score, y = University.Rank, color = \"Teaching.Score\")) +\n",
    "  geom_point() +\n",
    "  geom_point(aes(x = Research.Score, color = \"Research.Score\")) +\n",
    "  scale_x_continuous(limits = c(1, 100), breaks = seq(0, 100, by = 10)) +\n",
    "  labs(x = \"Score on a scale of 0-100\", y = \"University Ranking\", color = \"Variables\", title = \"Research and Teaching Scores vs. University Ranking\") +\n",
    "  scale_color_manual(values = c(\"blue\", \"red\")) +\n",
    "  theme(axis.text = element_text(size = 12),  \n",
    "        axis.title = element_text(size = 20),  \n",
    "        plot.title = element_text(size = 25),\n",
    "        legend.text = element_text(size = 15),\n",
    "        legend.title = element_text(size = 15))  \n",
    "\n",
    "options(repr.plot.width = 18, repr.plot.height = 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54f2fdd2-0f79-478b-b189-5e339506876b",
   "metadata": {},
   "source": [
    "Below, we plot the research and teaching scores vs. the female to male ratio. The plot appears scattered for both scores. Although there seems to be a higher teaching and research score for a higher female to male ratio, the magnitude of difference is not very convincing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "b4cec4c6-b439-4226-8995-ac606a264a74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 1080
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(tidy_data, aes(x = Teaching.Score, y = Female.Male.Ratio, color = \"Teaching.Score\")) +\n",
    "  geom_point() +\n",
    "  geom_point(aes(x = Research.Score, color = \"Research.Score\")) +\n",
    "  scale_x_continuous(limits = c(1, 100), breaks = seq(0, 100, by = 10)) +\n",
    "  labs(x = \"Score on a scale of 0-100\", y = \"Female.Male.Ratio\", color = \"Variables\", title = \"Research and Teaching Score vs. Female to Male Ratio\") +\n",
    "  scale_color_manual(values = c(\"blue\", \"red\")) +\n",
    "  theme(axis.text = element_text(size = 12),  \n",
    "        axis.title = element_text(size = 20),  \n",
    "        plot.title = element_text(size = 25),\n",
    "        legend.text = element_text(size = 15),\n",
    "        legend.title = element_text(size = 15))  \n",
    "\n",
    "options(repr.plot.width = 18, repr.plot.height = 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c01e5825-2592-4704-8d2d-643b52cb29e5",
   "metadata": {},
   "source": [
    "Lastly, we plot the research score vs. the teaching scores. These scores were seperately surveyed. There is a strong positive linear relationship between the two scores. This result is quite intuitive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "d50d0042-e035-4ce0-8ffd-24b73369842a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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buerK/gdR8mqIDD6wMgHBzCoYBwyAoQDg7hUEA4CAgHkdLC0UOe1ff8zWm9hWfh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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 360,
       "width": 1080
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(tidy_data, aes(x=Research.Score, y=Teaching.Score)) +\n",
    "       geom_point()+ scale_x_continuous(breaks = seq(0, 100, by = 5)) +\n",
    "scale_y_continuous(breaks = seq(0, 100, by = 5)) +\n",
    "labs(x = \"Research Score from 0-100\", y = \"Teaching Score from 0-100\", title = \"Research Score vs. Teaching Score\") +\n",
    "       theme(axis.text = element_text(size = 12),  \n",
    "             axis.title = element_text(size = 20),  \n",
    "             plot.title = element_text(size = 25))  \n",
    "\n",
    "options(repr.plot.width = 18, repr.plot.height = 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87da6661-cff0-468a-8373-3de2e55ffeac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "01e626d9-6394-4912-8672-8e3390acf9f1",
   "metadata": {},
   "source": [
    "## My Methods and Plan"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79d345cd-2dd7-4ad5-921b-8d0460b876b7",
   "metadata": {},
   "source": [
    "In my project, I am exploring how research score changes based on university rank, teaching score, and female to male ratios. To do my best in avoiding possible confounding variables, I will conduct my exploration with all variables provided, except for the names of the universities. By logic, the names of the universities have no relationship with any of the other variables, such as teaching score and student count. Furthermore, the location variable has many levels. This can cause overfitting and the resulting model may be difficult to interpret. In order to avoid this, I will aim to categorize location more generally, such as by continent. \n",
    "\n",
    "Since I plan to conduct the exploration with nearly all the variables in the dataset, using backward selection will be appropriate. This method will control Type 1 error and will lower the risk of overfitting compared to forward selection. As this method starts with a full model, it also lowers the risk of leaving out confounding variables. I will split my data into a training set and testing set, utilizing the 30% and 70% ratio. Lasso will be appropriate to use here for variable selection, since we have many factors and some may not be important. It will be more effective than Ridge, which tends to retain all variables in the full model. \n",
    "\n",
    "Using Lasso would require the assumptions of linearity in the data, independence, and the absence of multicollinearity. It also assumes there is no relationship if variables have small coefficients. This may not be true in reality. The backward selection method also requires the assumption of linearity and assumes the model residuals are independent and normal.\n",
    "\n",
    "A potential weakness in using the Lasso approach will be the location variable, which is categorical. I can use one-hot encoding in order to use Lasso, but it will be hard to control the risk of multicollinearity and will negatively affect the results of variable selection and the model built. The backward selection method also has limited performance when it comes to this categorical variable. Removing the categorical variable may lead to multicollinearity problems, especially since the location variable may be associated with student numbers, international student percentages, etc. \n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e5c45e6-e235-4ef2-9716-845dc0984edc",
   "metadata": {},
   "source": [
    "# Implementation of a proposed model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1c785269-28f3-4074-a9a9-08391adfc452",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 8 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Adj.R.Squared</th><th scope=col>Cp.Value</th><th scope=col>X.Intercept.</th><th scope=col>No.of.student</th><th scope=col>Industry.Income.Score</th><th scope=col>University.Rank</th><th scope=col>Female.Male.Ratio</th><th scope=col>International.Outlook.Score</th><th scope=col>No.of.student.per.staff</th><th scope=col>Teaching.Score</th><th scope=col>Citations.Score</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>0.8229885</td><td>163.405578</td><td>Yes</td><td>x  </td><td>x  </td><td>x  </td><td>x  </td><td>x  </td><td>x  </td><td>Yes</td><td>x  </td></tr>\n",
       "\t<tr><th scope=row>2</th><td>0.8867495</td><td> 57.587540</td><td>Yes</td><td>x  </td><td>x  </td><td>Yes</td><td>x  </td><td>x  </td><td>x  </td><td>Yes</td><td>x  </td></tr>\n",
       "\t<tr><th scope=row>3</th><td>0.9050573</td><td> 28.080283</td><td>Yes</td><td>x  </td><td>x  </td><td>Yes</td><td>x  </td><td>x  </td><td>x  </td><td>Yes</td><td>Yes</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>0.9117516</td><td> 17.976093</td><td>Yes</td><td>x  </td><td>x  </td><td>Yes</td><td>x  </td><td>Yes</td><td>x  </td><td>Yes</td><td>Yes</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>0.9167894</td><td> 10.720769</td><td>Yes</td><td>x  </td><td>x  </td><td>Yes</td><td>x  </td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>0.9196978</td><td>  7.011872</td><td>Yes</td><td>Yes</td><td>x  </td><td>Yes</td><td>x  </td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>0.9203004</td><td>  7.051088</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>x  </td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr>\n",
       "\t<tr><th scope=row>8</th><td>0.9197052</td><td>  9.000000</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 8 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & Adj.R.Squared & Cp.Value & X.Intercept. & No.of.student & Industry.Income.Score & University.Rank & Female.Male.Ratio & International.Outlook.Score & No.of.student.per.staff & Teaching.Score & Citations.Score\\\\\n",
       "  & <dbl> & <dbl> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 & 0.8229885 & 163.405578 & Yes & x   & x   & x   & x   & x   & x   & Yes & x  \\\\\n",
       "\t2 & 0.8867495 &  57.587540 & Yes & x   & x   & Yes & x   & x   & x   & Yes & x  \\\\\n",
       "\t3 & 0.9050573 &  28.080283 & Yes & x   & x   & Yes & x   & x   & x   & Yes & Yes\\\\\n",
       "\t4 & 0.9117516 &  17.976093 & Yes & x   & x   & Yes & x   & Yes & x   & Yes & Yes\\\\\n",
       "\t5 & 0.9167894 &  10.720769 & Yes & x   & x   & Yes & x   & Yes & Yes & Yes & Yes\\\\\n",
       "\t6 & 0.9196978 &   7.011872 & Yes & Yes & x   & Yes & x   & Yes & Yes & Yes & Yes\\\\\n",
       "\t7 & 0.9203004 &   7.051088 & Yes & Yes & Yes & Yes & x   & Yes & Yes & Yes & Yes\\\\\n",
       "\t8 & 0.9197052 &   9.000000 & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 8 × 11\n",
       "\n",
       "| <!--/--> | Adj.R.Squared &lt;dbl&gt; | Cp.Value &lt;dbl&gt; | X.Intercept. &lt;chr&gt; | No.of.student &lt;chr&gt; | Industry.Income.Score &lt;chr&gt; | University.Rank &lt;chr&gt; | Female.Male.Ratio &lt;chr&gt; | International.Outlook.Score &lt;chr&gt; | No.of.student.per.staff &lt;chr&gt; | Teaching.Score &lt;chr&gt; | Citations.Score &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 0.8229885 | 163.405578 | Yes | x   | x   | x   | x   | x   | x   | Yes | x   |\n",
       "| 2 | 0.8867495 |  57.587540 | Yes | x   | x   | Yes | x   | x   | x   | Yes | x   |\n",
       "| 3 | 0.9050573 |  28.080283 | Yes | x   | x   | Yes | x   | x   | x   | Yes | Yes |\n",
       "| 4 | 0.9117516 |  17.976093 | Yes | x   | x   | Yes | x   | Yes | x   | Yes | Yes |\n",
       "| 5 | 0.9167894 |  10.720769 | Yes | x   | x   | Yes | x   | Yes | Yes | Yes | Yes |\n",
       "| 6 | 0.9196978 |   7.011872 | Yes | Yes | x   | Yes | x   | Yes | Yes | Yes | Yes |\n",
       "| 7 | 0.9203004 |   7.051088 | Yes | Yes | Yes | Yes | x   | Yes | Yes | Yes | Yes |\n",
       "| 8 | 0.9197052 |   9.000000 | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |\n",
       "\n"
      ],
      "text/plain": [
       "  Adj.R.Squared Cp.Value   X.Intercept. No.of.student Industry.Income.Score\n",
       "1 0.8229885     163.405578 Yes          x             x                    \n",
       "2 0.8867495      57.587540 Yes          x             x                    \n",
       "3 0.9050573      28.080283 Yes          x             x                    \n",
       "4 0.9117516      17.976093 Yes          x             x                    \n",
       "5 0.9167894      10.720769 Yes          x             x                    \n",
       "6 0.9196978       7.011872 Yes          Yes           x                    \n",
       "7 0.9203004       7.051088 Yes          Yes           Yes                  \n",
       "8 0.9197052       9.000000 Yes          Yes           Yes                  \n",
       "  University.Rank Female.Male.Ratio International.Outlook.Score\n",
       "1 x               x                 x                          \n",
       "2 Yes             x                 x                          \n",
       "3 Yes             x                 x                          \n",
       "4 Yes             x                 Yes                        \n",
       "5 Yes             x                 Yes                        \n",
       "6 Yes             x                 Yes                        \n",
       "7 Yes             x                 Yes                        \n",
       "8 Yes             Yes               Yes                        \n",
       "  No.of.student.per.staff Teaching.Score Citations.Score\n",
       "1 x                       Yes            x              \n",
       "2 x                       Yes            x              \n",
       "3 x                       Yes            Yes            \n",
       "4 x                       Yes            Yes            \n",
       "5 Yes                     Yes            Yes            \n",
       "6 Yes                     Yes            Yes            \n",
       "7 Yes                     Yes            Yes            \n",
       "8 Yes                     Yes            Yes            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using regsubsets(), we conduct an exhaustive search for variable selection by considering all possible subsets of predictors:\n",
    "subsets = regsubsets(Research.Score~. + No.of.student + Industry.Income.Score, data = tidy_data,method=\"exhaustive\")\n",
    "s = summary(subsets)\n",
    "predictors <- ifelse(s$which, \"Yes\", \"x\")\n",
    "\n",
    "table <- data.frame(\"Adj R Squared\" = s$adjr2,\n",
    "                    \"Cp Value\" = s$cp, \n",
    "                    predictors)\n",
    "\n",
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee4c99b7-bc02-4a99-9224-886e427bbf4f",
   "metadata": {},
   "source": [
    "Using regsubsets() from the leap package, we evaluate the adjusted R squared and Cp value of all possible subsets of predictors to find the best and most efficient linear model. According to the table, I think that the most optimal model is #5 due to its high adjusted R squared of 0.917 and a Cp value of 10.720769. With running costs in mind, I chose this model because the adjusted R squared doesn't increase much and the Cp value does not get much closer to 9 as the models get larger beyond 5 predictors. These results were expected as we saw that Research scores had moderate to high linear correlation values with the variables selected. (University Rank, International Outlook Score, No. of Students per staff, Teaching Score, and Citations Score)"
   ]
  }
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