A subset of the original Boston dataset in MASS package
Boston2.Rd
We remove observations with crime rate over 3.2 as suggested in previous studies to obtain the remaining 374 observations.
Usage
data(Boston2)
Format
A data frame with 374 rows and 14 variables. It is a subset of the original Boston dataset in the MASS package. This one contains the sample with crime rate lower than 3.2. It contains the following columns:
crim
per capita crime rate by town.zn
proportion of residential land zoned for lots over 25,000 sq.ft.indus
proportion of non-retail business acres per town.chas
Charles River dummy variable (= 1 if tract bounds river; 0 otherwise).nox
nitrogen oxides concentration (parts per 10 million).rm
average number of rooms per dwelling.age
proportion of owner-occupied units built prior to 1940.dis
weighted mean of distances to five Boston employment centres.rad
index of accessibility to radial highways.tax
full-value property-tax rate per \$10,000.ptratio
pupil-teacher ratio by town.black
1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town.lstat
lower status of the population (percent).medv
median value of owner-occupied homes in \$1000s.
References
Li, K.-C. (1991). Sliced inverse regression for dimension reduction. Journal of the American Statistical Association, 86(414):316–327.
Chen, X., Zou, C., and Cook, R. D. (2010). Coordinate-independent sparse sufficient dimension reduction and variable selection. The Annals of Statistics, 38(6):3696–3723.
Qin, Y., Li, S., Li, Y., and Yu, Y. (2017). Penalized Maximum Tangent Likelihood Estimation and Robust Variable Selection. arXiv:1708.05439 [stat]. arXiv: 1708.05439.
Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102.
Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.
Examples
head(Boston2)
#> crim zn indus chas nox rm age dis rad tax ptratio black lstat
#> 1 0.00632 18 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 4.98
#> 2 0.02731 0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 396.90 9.14
#> 3 0.02729 0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 392.83 4.03
#> 4 0.03237 0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 394.63 2.94
#> 5 0.06905 0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 396.90 5.33
#> 6 0.02985 0 2.18 0 0.458 6.430 58.7 6.0622 3 222 18.7 394.12 5.21
#> medv
#> 1 24.0
#> 2 21.6
#> 3 34.7
#> 4 33.4
#> 5 36.2
#> 6 28.7