Delivery Time Data

Delivery Time Data, from Montgomery and Peck (1982). The aim is to explain the time required to service a vending machine (Y) by means of the number of products stocked (X1) and the distance walked by the route driver (X2).

data(delivery, package="robustbase")

Format

A data frame with 25 observations on the following 3 variables.

n.prod

Number of Products

distance

Distance

delTime

Delivery time

Source

Montgomery and Peck (1982, p.116)

References

P. J. Rousseeuw and A. M. Leroy (1987) Robust Regression and Outlier Detection; Wiley, page 155, table 23.

Examples

data(delivery)
summary(lm.deli <- lm(delTime ~ ., data = delivery))
#> 
#> Call:
#> lm(formula = delTime ~ ., data = delivery)
#> 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -5.7880 -0.6629  0.4364  1.1566  7.4197 
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept) 2.341231   1.096730   2.135 0.044170 *  
#> n.prod      1.615907   0.170735   9.464 3.25e-09 ***
#> distance    0.014385   0.003613   3.981 0.000631 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Residual standard error: 3.259 on 22 degrees of freedom
#> Multiple R-squared:  0.9596,	Adjusted R-squared:  0.9559 
#> F-statistic: 261.2 on 2 and 22 DF,  p-value: 4.687e-16
#> 

delivery.x <- as.matrix(delivery[, 1:2])
c_deli <- covMcd(delivery.x)
c_deli
#> Minimum Covariance Determinant (MCD) estimator approximation.
#> Method: Fast MCD(alpha=0.5 ==> h=14); nsamp = 500; (n,k)mini = (300,5)
#> Call:
#> covMcd(x = delivery.x)
#> Log(Det.):  10.81 
#> 
#> Robust Estimate of Location:
#>   n.prod  distance  
#>    5.895   268.053  
#> Robust Estimate of Covariance:
#>            n.prod  distance
#> n.prod      7.072     133.9
#> distance  133.915   32279.1