lab-5.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# Lab 5 - Probability and Simpson’s Paradox\n",
    "### The Outliers\n",
    "### Authors: \n",
    "  - Vera Jackson\n",
    "  - Miguel Peralta\n",
    "  - Cas Laskowski\n",
    "  - Sarah Gilbride\n",
    "  - \"Team member 5\"\n",
    "### Date: November 27th, 2024"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::: callout-important\n",
    "This is a *Team* Lab, meaning you can and should work with your project team on it.\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "In this lab you'll review and get practice with a variety of concepts, methods, and tools you've encountered thus far, with a focus on misrepresentation and ethics.\n",
    "\n",
    "## Learning objectives\n",
    "\n",
    "By the end of the lab, you will...\n",
    "\n",
    "-   Calculate single event, conditional, and \"and\" probabilities.\n",
    "-   Interpret probabilities in the context of the problem.\n",
    "-   Display a fundamental understanding of Simpson's Paradox.\n",
    "-   Practice teamwork and collaboration on GitHub.\n",
    "\n",
    "## Guidelines\n",
    "\n",
    "As we've discussed in lecture, your plots should include an informative title, axes should be labeled, and careful consideration should be given to aesthetic choices.\n",
    "\n",
    "::: callout-note\n",
    "Remember that continuing to develop a sound workflow for reproducible data analysis is important as you complete the lab and other assignments in this course.\n",
    "There will be periodic reminders in this assignment to remind you to **Run all, commit, and sync** your changes to GitHub.\n",
    "You should have at least 3 commits with meaningful commit messages by the end of the assignment.\n",
    ":::\n",
    "\n",
    "# Getting started\n",
    "\n",
    "All team members should clone the team GitHub repository for the lab.\n",
    "Then, one team member should edit the document Markdown cell by adding the team name to the `Team name` field and adding the names of the team members contributing to lab to the `Authors` field.\n",
    "Hopefully that's everyone, but if someone doesn't contribute during the lab session or throughout the week before the deadline, their name should not be added.\n",
    "If you have 4 members in your team, you can delete the line for the 5th team member.\n",
    "Then, this team member should render the document and commit and push the changes.\n",
    "All others should **not** touch the document at this stage.\n",
    "\n",
    "``` markdown\n",
    "# Lab 5 - Probability and Simpson’s Paradox\n",
    "### Team name\n",
    "### Authors: \n",
    "  - \"Team member 1\"\n",
    "  - \"Team member 2\"\n",
    "  - \"Team member 3\"\n",
    "  - \"Team member 4\"\n",
    "  - \"Team member 5\"\n",
    "### Date: ADD DATE HERE\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Questions\n",
    "\n",
    "::: callout-important\n",
    "Pick another member of the team write the answer to Part 1.\n",
    "All others should contribute to the discussion but only one person should type up the answer, render the document, commit, and push to GitHub.\n",
    "All others should **not** touch the document.\n",
    ":::\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1 - Probability and you\n",
    "\n",
    "### Question 1\n",
    "\n",
    "We use probabilities all the time when making decisions.\n",
    "*As a group*, provide *two* real world examples of when you've used probability to make decisions in your every day life.\n",
    "Think critically.\n",
    "Be creative."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Depending on the time of day, the squirrels are more active in one section of the neighborhood than the other. Depending on the probability of squirrels being present on a certain street or in the park, I will alter my route when walking my dog, so as to reduce any incidents of leash-pulling and barking at trees.\n",
    "\n",
    "\n",
    "- Checking the weather - while we may not be estimating the probability ourselves, we depend on the probability that it will rain as determined by weather apps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::: callout-important\n",
    "After the team member working on Part 1 renders, commits, and pushes, another team member should pull their changes and render the document.\n",
    "Then, they should write the answer to Part 2.\n",
    "All others should contribute to the discussion but only one person should type up the answer, render the document, commit, and push to GitHub.\n",
    "All others should **not** touch the document.\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2 - Risk of coronary heart disease\n",
    "\n",
    "This data set is from an ongoing cardiovascular study on residents of the town of Framingham, Massachusetts.\n",
    "We want to examine the relationship between various health characteristics and the risk of having heart disease.\n",
    "\n",
    "### Question 2\n",
    "\n",
    "Load in the data set called `education-disease` and answer the following questions below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4240 entries, 0 to 4239\n",
      "Data columns (total 16 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   male             4240 non-null   int64  \n",
      " 1   age              4240 non-null   int64  \n",
      " 2   education        4135 non-null   float64\n",
      " 3   currentSmoker    4240 non-null   int64  \n",
      " 4   cigsPerDay       4211 non-null   float64\n",
      " 5   BPMeds           4187 non-null   float64\n",
      " 6   prevalentStroke  4240 non-null   int64  \n",
      " 7   prevalentHyp     4240 non-null   int64  \n",
      " 8   diabetes         4240 non-null   int64  \n",
      " 9   totChol          4190 non-null   float64\n",
      " 10  sysBP            4240 non-null   float64\n",
      " 11  diaBP            4240 non-null   float64\n",
      " 12  BMI              4221 non-null   float64\n",
      " 13  heartRate        4239 non-null   float64\n",
      " 14  glucose          3852 non-null   float64\n",
      " 15  TenYearCHD       4240 non-null   int64  \n",
      "dtypes: float64(9), int64(7)\n",
      "memory usage: 530.1 KB\n"
     ]
    },
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       "      <th>heartRate</th>\n",
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       "      <th>TenYearCHD</th>\n",
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       "   male  age  education  currentSmoker  cigsPerDay  BPMeds  prevalentStroke  \\\n",
       "0     1   39        4.0              0         0.0     0.0                0   \n",
       "1     0   46        2.0              0         0.0     0.0                0   \n",
       "2     1   48        1.0              1        20.0     0.0                0   \n",
       "3     0   61        3.0              1        30.0     0.0                0   \n",
       "4     0   46        3.0              1        23.0     0.0                0   \n",
       "\n",
       "   prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  heartRate  glucose  \\\n",
       "0             0         0    195.0  106.0   70.0  26.97       80.0     77.0   \n",
       "1             0         0    250.0  121.0   81.0  28.73       95.0     76.0   \n",
       "2             0         0    245.0  127.5   80.0  25.34       75.0     70.0   \n",
       "3             1         0    225.0  150.0   95.0  28.58       65.0    103.0   \n",
       "4             0         0    285.0  130.0   84.0  23.10       85.0     85.0   \n",
       "\n",
       "   TenYearCHD  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           1  \n",
       "4           0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education_disease = pd.read_csv(\"data/education-disease.csv\")\n",
    "\n",
    "education_disease.info()\n",
    "\n",
    "education_disease.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3\n",
    "\n",
    "How many levels of education are there in these data?\n",
    "How many levels of disease are there?\n",
    "**Hint:** The `unique()` function might be helpful.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Levels of education: [ 4.  2.  1.  3. nan]\n",
      "Levels of disease: [0 1]\n"
     ]
    }
   ],
   "source": [
    "print(\"Levels of education: \" + str(education_disease['education'].unique()))\n",
    "\n",
    "print(\"Levels of disease: \" + str(education_disease['TenYearCHD'].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 4 levels of education (1 through 4), with N/A or left blank if education is unknown.\n",
    "\n",
    "There are 2 levels of disease, 0 and 1, indicating no or yes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4\n",
    "\n",
    "Convert the data to a two-way table where each cell is the number of people falling into each combination of Disease and Education.\n",
    "**Hint:** Use `groupby` and `pivot_table`.\n",
    "Your answer should be a 4x3 data frame with counts in each cell.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 2 entries, 0 to 1\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   1.0     2 non-null      float64\n",
      " 1   2.0     2 non-null      float64\n",
      " 2   3.0     2 non-null      float64\n",
      " 3   4.0     2 non-null      float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 80.0 bytes\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>education</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
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       "      <th>0</th>\n",
       "      <td>1397.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>601.0</td>\n",
       "      <td>403.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>323.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>70.0</td>\n",
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      "text/plain": [
       "education      1.0     2.0    3.0    4.0\n",
       "TenYearCHD                              \n",
       "0           1397.0  1106.0  601.0  403.0\n",
       "1            323.0   147.0   88.0   70.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add code here.\n",
    "\n",
    "education_disease_pivot = education_disease.groupby(['TenYearCHD', 'education']).size().reset_index(name='count').pivot_table(index='TenYearCHD', columns='education', values='count')\n",
    "\n",
    "education_disease_pivot.info()\n",
    "\n",
    "education_disease_pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the summary table you created above, answer the remaining questions.\n",
    "You do not have to use Python functions for your calculations, you can use Python as a calculator using the values from the summary table.\n",
    "Make sure to show your work, i.e., instead of reporting just the final answer, use Python to calculate that in a way we can see the counts you've used along the way.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5\n",
    "\n",
    "What is the probability of a random individual having high school or GED education and not being high risk for cardiovascular disease?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "education    1.0   2.0  3.0  4.0  total\n",
      "TenYearCHD                             \n",
      "0           1397  1106  601  403   3507\n",
      "1            323   147   88   70    628\n",
      "\n",
      "Total Sample Population: 4135\n",
      "\n",
      "Total count of those with high school or GED education and not being high risk: 1106\n",
      "\n",
      "Probability that a random individual has a high school or GED education and not being high risk: 0.2674727932285369\n"
     ]
    }
   ],
   "source": [
    "# add all numbers together to determine total population\n",
    "education_disease_pivot['total'] = education_disease_pivot[1.0]+education_disease_pivot[2.0]+education_disease_pivot[3.0]+education_disease_pivot[4.0]\n",
    "\n",
    "print(education_disease_pivot)\n",
    "\n",
    "total_population_count = education_disease_pivot['total'].sum()\n",
    "\n",
    "print(\"\\nTotal Sample Population: \" + str(total_population_count))\n",
    "\n",
    "\n",
    "# The probability that someone has a HS degree/GED (A) AND not being high risk for CHD (B) = P(AnB)\n",
    "\n",
    "count_HS_lowrisk = education_disease_pivot.loc[0, 2.0] #location 0 corresponds to low risk\n",
    "\n",
    "print(\"\\nTotal count of those with high school or GED education and not being high risk: \" + str(count_HS_lowrisk))\n",
    "\n",
    "p_HS_lowrisk = count_HS_lowrisk/total_population_count\n",
    "\n",
    "print(\"\\nProbability that a random individual has a high school or GED education and not being high risk: \" + str(p_HS_lowrisk))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 6\n",
    "\n",
    "What is the probability that a random individual who is already high risk for cardiovascular disease has a college education?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Probability that, given someone is high-risk, has a college education: 0.14012738853503184\n"
     ]
    }
   ],
   "source": [
    "count_highrisk = education_disease_pivot.loc[1, 'total']\n",
    "count_college_highrisk = education_disease_pivot.loc[1, 3.0]\n",
    "\n",
    "p_givenhighrisk_college = count_college_highrisk/count_highrisk\n",
    "\n",
    "\n",
    "print(\"\\nProbability that, given someone is high-risk, has a college education: \" + str(p_givenhighrisk_college))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "::: callout-important\n",
    "After the team member working on Part 2 renders, commits, and pushes, another team member should pull their changes and render the document.\n",
    "Then, they should write the answer to Part 3.\n",
    "All others should contribute to the discussion but only one person should type up the answer, render the document, commit, and push to GitHub.\n",
    "All others should **not** touch the document.\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3 - Bike rentals\n",
    "\n",
    "Bike sharing systems are new generation of traditional bike rentals where whole process from membership, rental and return back has become automatic.\n",
    "You are tasked to investigate the relationship between the temperature outside and the number of bikes rented in the Washington DC area between the years 2011 and 2022.\n",
    "You will be investigating data for the months June, July, September, and November.\n",
    "\n",
    "Below is a list of variables and their definitions:\n",
    "\n",
    "| Variable  | Definition                                                                     |\n",
    "|------------------|------------------------------------------------------|\n",
    "| `season`  | Numerical representation of Spring (2), Summer (3), and Fall (4)               |\n",
    "| `year`    | Numerical representation of 2011 (0) or 2012 (1)                               |\n",
    "| `month`   | Month in which data were collected                                             |\n",
    "| `holiday` | Indicator variable for whether data were collected on a holiday (1) or not (0) |\n",
    "| `weekday` | Numerical representation of day of week                                        |\n",
    "| `temp`    | Temperature in Celsius                                                         |\n",
    "| `count`   | Number of bike rentals for that day                                            |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 7\n",
    "\n",
    "(a) Read in the `bike` data.\n",
    "    Then, create a scatter plot that investigates the relationship between the number of bikes rented and the temperature outside.\n",
    "    Include a straight line of best fit to help discuss the discovered relationship.\n",
    "    Summarize your findings in 2-3 sentences.\n",
    "\n",
    "(b) Another researcher suggests to look at the relationship between bikes rented and temperature by each of the four months of interest.\n",
    "    Recreate your plot in part a, and color the points by month.\n",
    "    Include a straight line for each of the four months to help discuss each month's relationship between bikes rented and temperature.\n",
    "    In 3-4 sentences, summarize your findings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Temperature ($\\\\degree$C)')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import Bike Data\n",
    "bike = pd.read_csv(\"data/bike.csv\")\n",
    "\n",
    "# Code Question 7 Graph Part (a):\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.regplot(data =bike, y='count',x='temp',ci = None, line_kws={\"color\": \"darkred\"}, color='indianred', scatter_kws={'alpha': 0.5})\n",
    "plt.title('Bikes Rented vs Outside Temperature:\\n')\n",
    "plt.suptitle('Washington DC, 2011 and 2012', fontsize=10, y = .91)\n",
    "plt.ylabel('Bikes Rented')\n",
    "plt.xlabel('Temperature ($\\\\degree$C)')\n",
    "\n",
    "##Sources:\n",
    "##https://stackoverflow.com/questions/19926246/inserting-a-degree-symbol-into-python-plot "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer Question 7 A: ###\n",
    "\n",
    "Based on the scatterplot above, the relationship between the number of bikes rented and the temperature outside appears to be a positive one. The line of best fit indicates that, in Washington DC in 2011 and 2012, as the temperature (degrees celsius) increases, the number of bikes rented also appears to increase. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x137a9cd40>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code Question 7 Graph Part (b):\n",
    "bike['Month'] = bike['month'].apply(lambda x: 'June' if x == 6 else 'July' if x == 7 else 'September' if x == 9 else 'November' if x == 11 else 'Null')\n",
    "\n",
    "sns.lmplot(data =bike, y='count',x='temp',hue = 'Month',ci=False, legend=False, scatter_kws={'alpha': 0.7}, palette='colorblind', height = 6, aspect = 1.5)\n",
    "plt.title('Bikes Rented vs Outside Temperature by Month:')\n",
    "plt.suptitle('Washington DC, 2011 and 2012\\n', fontsize=10, y=.975)\n",
    "plt.ylabel('Bikes Rented')\n",
    "plt.xlabel('Temperature ($\\\\degree$C)')\n",
    "plt.legend(title='Month:', bbox_to_anchor=(0.5, -0.22), loc='lower center', ncol = 4, frameon = False)\n",
    "\n",
    "##Sources:\n",
    "##https://stackoverflow.com/questions/19926246/inserting-a-degree-symbol-into-python-plot "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer Question 7 B: ###\n",
    "\n",
    "Based on the scatterplot above, the relationship between the number of bikes rented and the temperature outside for each of the four months included in the dataset all appear to be negative. For each of the months included in the graph, each line of best fit apears to be negative, to varying degrees. For each of these months (June, July, September, and November) within Washington DC in 2011 and 2012, as the temperature (degrees celsius) increases, the number of bikes rented apears to decrease. September appears to have the least dramatic decline, while June and July appear to have stronger negative relationships between temperature and the number of bikes rented. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please watch the following video on Simpson's Paradox [here](https://www.youtube.com/watch?v=ebEkn-BiW5k).\n",
    "After you do, please answer the following questions.\n",
    "\n",
    "### Question 8\n",
    "\n",
    "In your own words, summarize Simpson's Paradox in 2-3 sentences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer Q8\n",
    "\n",
    "Simpson's Paradox is statistical phenomenon where a trends evident within groups disappears or reverses when the groups are combined into a whole. That is to say, when a dataset is only analyzed as a whole without exploring and determing if there are confounding variables that have different trends, we can easily make faulty conclusion. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 9\n",
    "\n",
    "Compare and contrast your findings from part (a) and part (b).\n",
    "What's different?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answer Q9\n",
    "The visualization in part (a) contains all of the groups combined, while part (b) separates the groups by month. The trendline in part (a) shows a positive relationship between the two variables, in which the number of bikes rented increases as the temperature increases. However, in part (b) the opposite relationship is shown when the data points are separated by month, there is a downward trend within each group where the number of bikes rented decreases as the temperature increases. \n",
    "\n",
    "This is an example of Simpson's Paradox because the downward trend present in the data when it is separated by month disappears and actually reverses when the groups are combined. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 10\n",
    "\n",
    "Think critically about your answer to part d.\n",
    "What other context from this study could be creating this paradox?\n",
    "That is, identify a potential confounding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer Q10\n",
    "\n",
    "It is evident that the dataset is subject to the Simpson's Paradox in that it appears that bike rentals increase as the temperature increases when looked at as a whole. However, when grouped by month, the trend actually is reversed with a small decrease each month as temperatures rise. We are including two years, so it would be interesting to break out the month and year to see if there are different trends during the different years. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Closing an issue with a commit\n",
    "\n",
    "Go to your GitHub repository, you will see an issue with the title \"Learn to close an issue with a commit\".\n",
    "\n",
    "Your goal is to close this issue with a commit to practice this workflow, which is the workflow you will use when you are addressing feedback on your projects.\n",
    "\n",
    "-   Go to the relevant section in your lab .ipynb file.\n",
    "-   Delete the sentence that says \"Delete me\".\n",
    "-   Render the document.\n",
    "-   Commit your changes from the git tab with the commit message *\"Delete sentence, closes #1.\"*\n",
    "-   Push your changes to your repo and observe that the issue is now closed and the commit associated with this move is linked from the issue.\n",
    "\n",
    "GitHub allows you to close an issue directly with commits if the commit uses one of the following keywords followed bu the issue number (which you can find next to the issue title): close, closes, closed, fix, fixes, fixed, resolve, resolves, and resolved.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wrap-up\n",
    "\n",
    "## Submission\n",
    "\n",
    "::: callout-warning\n",
    "Before you wrap up the assignment, make sure all of your documents are updated on your GitHub repo.\n",
    "We will be checking these to make sure you have been practicing how to commit and push changes.\n",
    "\n",
    "You must turn in the .ipynb file by the submission deadline to be considered \"on time\".\n",
    ":::\n",
    "\n",
    "::: callout-important\n",
    "## Checklist\n",
    "\n",
    "Make sure you have:\n",
    "\n",
    "-   attempted all questions\n",
    "-   run all code in your Jupyter notebook\n",
    "-   committed and pushed everything to your GitHub repository such that the Git pane in VS Code is empty\n",
    ":::\n",
    "\n",
    "## Grading\n",
    "\n",
    "The lab is graded out of a total of 50 points.\n",
    "\n",
    "On Questions 1 through 10, you can earn up to 5 points on each question:\n",
    "\n",
    "-   5: Response shows excellent understanding and addresses all or almost all of the rubric items.\n",
    "\n",
    "-   4: Response shows good understanding and addresses most of the rubric items.\n",
    "\n",
    "-   3: Response shows understanding and addresses a majority of the rubric items.\n",
    "\n",
    "-   2: Response shows effort and misses many of the rubric items.\n",
    "\n",
    "-   1: Response does not show sufficient effort or understanding and/or is largely incomplete.\n",
    "\n",
    "-   0: No attempt.\n"
   ]
  }
 ],
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