## Hawkins, Bradu, Kass's Artificial Data

### Description

Artificial Data Set generated by Hawkins, Bradu, and Kass (1984). The
data set consists of 75 observations in four dimensions (one response
and three explanatory variables). It provides a good example of the
masking effect. The first 14 observations are outliers, created in
two groups: 1–10 and 11–14.
Only observations 12, 13 and 14 appear as outliers when using
classical methods, but can be easily unmasked using robust
distances computed by, e.g., MCD - covMcd().

### Usage

`data(hbk, package="robustbase")`

### Format

A data frame with 75 observations on 4 variables, where the last
variable is the dependent one.

- X1
x[,1]

- X2
x[,2]

- X3
x[,3]

- Y
y

### Note

This data set is also available in package wle as
`artificial`

.

### Source

Hawkins, D.M., Bradu, D., and Kass, G.V. (1984)
Location of several outliers in multiple regression data using
elemental sets.
*Technometrics* **26**, 197–208.

P. J. Rousseeuw and A. M. Leroy (1987)
*Robust Regression and Outlier Detection*;
Wiley, p.94.

### Examples

```
data(hbk)
plot(hbk)
summary(lm.hbk <- lm(Y ~ ., data = hbk))
hbk.x <- data.matrix(hbk[, 1:3])
(cHBK <- covMcd(hbk.x))
```