A subset of the original Boston dataset in MASS package
Boston2.RdWe 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:
crimper capita crime rate by town.znproportion of residential land zoned for lots over 25,000 sq.ft.indusproportion of non-retail business acres per town.chasCharles River dummy variable (= 1 if tract bounds river; 0 otherwise).noxnitrogen oxides concentration (parts per 10 million).rmaverage number of rooms per dwelling.ageproportion of owner-occupied units built prior to 1940.disweighted mean of distances to five Boston employment centres.radindex of accessibility to radial highways.taxfull-value property-tax rate per \$10,000.ptratiopupil-teacher ratio by town.black1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town.lstatlower status of the population (percent).medvmedian 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