Data on household gasoline demand with price, income, and geographic characteristics.
data('gasdemand')A data.frame with 3640 observations and 23 variables, among them:
log_q: log gasoline quantity demanded
log_p: log gasoline price
log_y: log household income
log_driver: log number of drivers in household
log_hhr_age: log household head age
log_hhsize: log household size
total_wrkr: total number of workers
publictransit_d: =1 if public transit available
distance_oil1000: distance to nearest oil refinery (in 1000s)
cl5_secondcity_d: =1 if in a second city
cl5_smtown_d: =1 if in a small town
cl5_suburban_d: =1 if suburban area
cl5_urban_d: =1 if urban area
popdensity_d2-d8: population density indicators
share: survey weight
state_fips: state FIPS code
region: region code
data('gasdemand')
head(gasdemand)
#> log_q log_p log_y cl5_secondcity_d cl5_smtown_d cl5_suburban_d
#> 1 8.143545 0.2298073 9.433484 1 0 0
#> 2 8.034972 0.2299092 11.119883 1 0 0
#> 3 7.608235 0.2298260 10.768485 0 1 0
#> 4 7.452868 0.2298071 11.695247 0 1 0
#> 5 7.807454 0.2297862 11.695247 0 1 0
#> 6 7.696466 0.2299038 11.191341 0 0 1
#> cl5_urban_d distance_oil1000 log_driver log_hhr_age log_hhsize popdensity_d2
#> 1 0 0.6570877 1.3862944 3.465736 1.609438 0
#> 2 0 0.6570877 0.6931472 3.610918 1.098612 0
#> 3 0 0.6570877 0.6931472 3.258096 1.098612 0
#> 4 0 0.6570877 0.6931472 3.465736 1.098612 1
#> 5 0 0.6570877 1.0986123 3.931826 1.386294 1
#> 6 0 0.6570877 0.6931472 3.610918 1.386294 0
#> popdensity_d3 popdensity_d4 popdensity_d5 popdensity_d6 popdensity_d7
#> 1 0 1 0 0 0
#> 2 0 0 1 0 0
#> 3 1 0 0 0 0
#> 4 0 0 0 0 0
#> 5 0 0 0 0 0
#> 6 0 1 0 0 0
#> popdensity_d8 publictransit_d share state_fips total_wrkr region
#> 1 0 0 0.34641027 1 3 5
#> 2 0 0 0.05755571 1 1 5
#> 3 0 0 0.05337439 1 2 5
#> 4 0 0 0.01808682 1 2 5
#> 5 0 0 0.02578386 1 2 5
#> 6 0 0 0.03819802 1 2 5