# ash data

### Description

Data from 99 ash samples originating from different biomass, measured on 9 variables; 8 log-transformed variables are added.

### Usage

1 |

### Format

A data frame with 99 observations on the following 17 variables.

`SOT`

a numeric vector

`P2O5`

a numeric vector

`SiO2`

a numeric vector

`Fe2O3`

a numeric vector

`Al2O3`

a numeric vector

`CaO`

a numeric vector

`MgO`

a numeric vector

`Na2O`

a numeric vector

`K2O`

a numeric vector

`log(P2O5)`

a numeric vector

`log(SiO2)`

a numeric vector

`log(Fe2O3)`

a numeric vector

`log(Al2O3)`

a numeric vector

`log(CaO)`

a numeric vector

`log(MgO)`

a numeric vector

`log(Na2O)`

a numeric vector

`log(K2O)`

a numeric vector

### Details

The dependent variable Softening Temperature (SOT) of ash should be modeled by the elemental composition of the ash data. Data from 99 ash samples - originating from different biomass - comprise the experimental SOT (630-1410 centigrades), and the experimentally determined eight mass concentrations the listed elements. Since the distribution of the elements is skweed, the log-transformed variables have been added.

### Source

K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.

### References

K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.

### Examples

1 2 |

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