Problem: When shopping for a used vehicle outside of the traditional window sticker, the city fuel economy should always be provided.

Residency: Group Assignment

Problem: When shopping for a used vehicle outside of the traditional window sticker, the city fuel

economy should always be provided. Unfortunately, it is not always that obvious. If it were as

simple as knowing the year, make, and model, it wouldn’t be that difficult to use an internet

search to find the information. However, different options available on vehicle models are

typically needed to identify what the city fuel economy is. Researching what influences the fuel

economy can offer insight and potentially direct a buyer to the best option.

 

Question: Considering the vehicle manufacturers Toyota, Honda, and Kia, between 1991 and 2020,

amongst the fuel economy on the highway, the vehicle make, and the year, the primary fuel

type, and the number of engine cylinders, the engine displacement, and the vehicle class, and

the tailpipe carbon dioxide emissions for the primary fuel type, what features have the most

influence when prediction the fuel economy in the city, using the data consolidated by the DOE

(n.d.a).

 

Data:

• The data and data dictionaries are online. Links and the references formatted per APA 7:

o A direct link to the raw data is: https://www.fueleconomy.gov/feg/epadata/vehicles.csv

o U.S. Department of Energy (n.d.a). Download fuel economy data [Data set].

www.fueleconomy.gov: The official U.S. government source for fuel economy information [Data

set]. Office of Energy Efficiency & Renewable Energy.

https://www.fueleconomy.gov/feg/epadata/vehicles.csv

o A direct link to the data dictionary is: https://www.fueleconomy.gov/feg/ws/index.shtml#vehicle

o U.S. Department of Energy (n.d.b). Fueleconomy.gov web services [Data code book].

www.fueleconomy.gov:The official U.S. government source for fuel economy information. Office

of Energy Efficiency & Renewable Energy.

https://www.fueleconomy.gov/feg/ws/index.shtml#vehicle

 

Requirements for the analysis:

• BEFORE subsetting, for the field that represents the different vehicle classes, using the following

programming code to recode this variable before conducting analysis. For this code the object name of

the data is df, you will need to update this part of the code to match the object name of the data set in

your analysis: vehClass = df$VClass

levels(vehClass) <- 1:34

levels(vehC) <- list(car=c(1:6,14,29:30),

van= c(7:8,31:34),

truck = c(9:11,23:26),

SUV = c(12:13,21:22,27:28),

other = 15:20)

Replace the data in VClass with the data in vehC.

• Do not delete missing values.

• For observations where the primary fuel type is electricity, modify the fields representing the number of

engine cylinders and the engine displacement by replacing the value with a zero. Ask questions if you

don’t understand.

• A description similar to the carbon dioxide emitted from the tailpipe is attached to more than one

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