Descriptive information and the appraised total price (in Euros) for apartments in Vitoria, Spain.
A data frame with 218 observations on the following 16 variables:
the number of the observation
the market total price (in Euros) of the apartment including garage(s) and storage room(s)
the total living area of the apartment in square meters
a factor
indicating the neighborhood where the apartment is located with levels
Z11, Z21, Z31, Z32, Z34, Z35,
Z36, Z37, Z38, Z41, Z42, Z43,
Z44, Z45, Z46, Z47, Z48, Z49,
Z52, Z53, Z56, Z61, and Z62.
a factor indicating the condition of the apartment
with levels 2A, 2B, 3A, 3B, 4A,
4B, and 5A. The factors are ordered so that 2A is the
best and 5A is the worst.
age of the aprtment
floor on which the apartment is located
total number of rooms including bedrooms, dining room, and kitchen
a factor indicating the percent of the
apartment exposed to the elements. The levels E100, E75,
E50, and E25, correspond to complete exposure, 75% exposure,
50% exposure, and 25% exposure respectively.
is an ordered factor indicating the state of
conservation of the apartment. The levels 1A, 2A, 2B,
and 3A are ordered from best to worst conservation.
the number of bathrooms
the number of garages
indicates the absence (0) or presence (1) of elevators.
an ordered factor
from best to worst indicating the category of the street with levels
S2, S3, S4, and S5
a
factor indicating the type of heating with levels 1A, 3A,
3B, and 4A which correspond to: no heating, low-standard
private heating, high-standard private heating, and central heating
respectively.
the number of storage rooms outside of the apartment
Ugarte, M. D., Militino, A. F., and Arnholt, A. T. (2008) Probability and Statistics with R. Chapman & Hall/CRC.
modTotal <- lm(totalprice ~ area + as.factor(elevator) + area:as.factor(elevator), data = vit2005) modSimpl <- lm(totalprice ~ area, data = vit2005) anova(modSimpl,modTotal)#> Analysis of Variance Table #> #> Model 1: totalprice ~ area #> Model 2: totalprice ~ area + as.factor(elevator) + area:as.factor(elevator) #> Res.Df RSS Df Sum of Sq F Pr(>F) #> 1 216 3.5970e+11 #> 2 214 3.0267e+11 2 5.704e+10 20.165 9.478e-09 *** #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1rm(modSimpl, modTotal)