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The first major branch off of the abdominal aorta is:
The first mаjоr brаnch оff оf the аbdominal aorta is:
Midterm Exаm 1 - Open Bооk Sectiоn (R) - Pаrt 2 Instructions The R Mаrkdown/Jupyter Notebook file includes the questions, the empty code chunk sections for your code, and the text blocks for your responses. Answer the questions below by completing the R Markdown/Jupyter Notebook file. You may make slight adjustments to get the file to knit/convert but otherwise keep the formatting the same. Once you've finished answering the questions, submit your responses in a single knitted file as HTML only. Partial credit may be given if your code is correct but your conclusion is incorrect or vice versa. Next Steps: Save the .Rmd/.ipynb file in your working directory - the same directory where you will download the "home_energy_consumption.csv" data file into. Having both files in the same directory will help in reading the .csv file. Read the question and create the code necessary within the code chunk section immediately below each question. Knitting this file will generate the output and insert it into the section below the code chunk. Type your answer to the questions in the text block provided immediately after the response prompt. Knit the file as you work on the exam questions so that you will not encounter knitting issues at the end of the exam. Once you've finished answering all questions, knit this file and submit the knitted file as HTML on Canvas. Submitting any other file type will result in a penalty applied to your exam grade. Mock Example Question This will be the exam question - each question is already copied from Canvas and inserted into individual text blocks below, you do not need to copy/paste the questions from the online Canvas exam. # Example code chunk area. Enter your code below the comment Mock Response to Example Question: This is the section where you type your written answers to the question. Depending on the question asked, your typed response may be a number, a list of variables, a few sentences, or a combination of these elements. Data Set home_energy_consumption.csv (right-click the link and select to open in new window/tab) Starter TemplatesYou may use either the R Markdown or Jupyter Notebook Starter Template: R Markdown Starter Template: Fall2024_Midterm_1_R_starter_template.rmd (right-click the link and select to open in new window/tab) Jupyter Notebook Starter Template: SPR2022 - Midterm Exam 1 Part 2.ipynb (right-click the link and select to open in new window/tab) Ready? Let's begin. We wish you the best of luck!
Bаckgrоund In this exаm, yоu will be cоnsidering vаrious attributes to predict the monthly energy consumption of a household. The dataset contains the following variables: Household Size: The number of people living in the household. (Quantitative variable) Home Size: The size of the home in square feet. (Quantitative variable) Number of Rooms: The total number of rooms in the home. (Quantitative variable) Household Income: The household's annual income in US dollars. (Quantitative variable) Type of Home: The classification of the home, such as "Detached house," "Townhouse," or "Semi-detached house." (Qualitative variable) Heating System Type: The type of heating system used in the home, such as "Solar" or "Gas." (Qualitative variable) Cooling System Type: The type of cooling system used in the home, such as "Window units," "Central AC," or "None." (Qualitative variable) Insulation Quality: A rating (from 1 to 5) of the quality of the home's insulation, with 5 being the best quality. (Quantitative variable) Ownership Status: Whether the household owns or rents the home (e.g., "Owner" or "Renter"). (Quantitative variable) Work from Home Frequency: The number of days per week that household members work from home. (Quantitative variable) Smart Home Devices: Indicates whether the household has smart home devices installed ("Yes" or "No"). (Qualitative variable) Solar Panel Installation: Indicates whether the home has solar panels installed ("Yes" or "No"). (Qualitative variable) Monthly Energy Consumption: The household's monthly energy consumption in kilowatt-hours (kWh). (Response variable) #read the csv fileset.seed(100)#this seed has been set to 100 to ensure results are reproducible. DO NOT CHANGE THIS SEEDenergy_consumption = read.csv("home_energy_consumption.csv",header=TRUE) energy_consumption$Type_of_Home=as.factor(energy_consumption$Type_of_Home)energy_consumption$Heating_System_Type=as.factor(energy_consumption$Heating_System_Type)energy_consumption$Cooling_System_Type=as.factor(energy_consumption$Cooling_System_Type)energy_consumption$Ownership_Status=as.factor(energy_consumption$Ownership_Status)energy_consumption$Smart_Home_Devices=as.factor(energy_consumption$Smart_Home_Devices)energy_consumption$Solar_Panel_Installation=as.factor(energy_consumption$Solar_Panel_Installation) #Dividing the dataset into training and testing datasetstestRows = sample(nrow(energy_consumption),0.2*nrow(energy_consumption))testData = energy_consumption[testRows, ]trainData = energy_consumption[-testRows, ]row.names(trainData)