### Abstract

To the worker who only has a hammer, we are told, everything looks like a nail. Solutions to applied statistical problems are framed by the limitations imposed by statistical computing packages and languages. For better or worse, we can do what the packages do: we cannot do what the packages won't do. Statistical languages like R have basic tools that allow the analyst to design new hammers, but even in R we cannot build an arbitrary hammer, only ones within the limits imposed by the R language. XLISP-STAT imposes different limitations, so we can produce different hammers. In this article, I look at some of the tools in XLISP-STAT that allow the user to think about graphics in ways that cannot be easily replicated in other statistical languages. The interactive graphical methods available in XLISP-STAT lead to very different methodology than would be developed without the tools that XLISP-STAT provides. The general approach to graphics and indeed to data analysis in general is quite different in a package like Arc that is built on top of XLISP-STAT, than it is in other statistical packages. We discuss why that might be true, and why this depends on design options created by XLISP-STAT.

Original language | English (US) |
---|---|

Pages (from-to) | 1-12 |

Number of pages | 12 |

Journal | Journal of Statistical Software |

Volume | 13 |

State | Published - Feb 2005 |

### Fingerprint

### Keywords

- Arc
- Computing
- Graphics
- XLISP-STAT

### Cite this

*Journal of Statistical Software*,

*13*, 1-12.

**Lost opportunities : Why we need a variety of statistical languages.** / Weisberg, Sanford.

Research output: Contribution to journal › Article

*Journal of Statistical Software*, vol. 13, pp. 1-12.

}

TY - JOUR

T1 - Lost opportunities

T2 - Why we need a variety of statistical languages

AU - Weisberg, Sanford

PY - 2005/2

Y1 - 2005/2

N2 - To the worker who only has a hammer, we are told, everything looks like a nail. Solutions to applied statistical problems are framed by the limitations imposed by statistical computing packages and languages. For better or worse, we can do what the packages do: we cannot do what the packages won't do. Statistical languages like R have basic tools that allow the analyst to design new hammers, but even in R we cannot build an arbitrary hammer, only ones within the limits imposed by the R language. XLISP-STAT imposes different limitations, so we can produce different hammers. In this article, I look at some of the tools in XLISP-STAT that allow the user to think about graphics in ways that cannot be easily replicated in other statistical languages. The interactive graphical methods available in XLISP-STAT lead to very different methodology than would be developed without the tools that XLISP-STAT provides. The general approach to graphics and indeed to data analysis in general is quite different in a package like Arc that is built on top of XLISP-STAT, than it is in other statistical packages. We discuss why that might be true, and why this depends on design options created by XLISP-STAT.

AB - To the worker who only has a hammer, we are told, everything looks like a nail. Solutions to applied statistical problems are framed by the limitations imposed by statistical computing packages and languages. For better or worse, we can do what the packages do: we cannot do what the packages won't do. Statistical languages like R have basic tools that allow the analyst to design new hammers, but even in R we cannot build an arbitrary hammer, only ones within the limits imposed by the R language. XLISP-STAT imposes different limitations, so we can produce different hammers. In this article, I look at some of the tools in XLISP-STAT that allow the user to think about graphics in ways that cannot be easily replicated in other statistical languages. The interactive graphical methods available in XLISP-STAT lead to very different methodology than would be developed without the tools that XLISP-STAT provides. The general approach to graphics and indeed to data analysis in general is quite different in a package like Arc that is built on top of XLISP-STAT, than it is in other statistical packages. We discuss why that might be true, and why this depends on design options created by XLISP-STAT.

KW - Arc

KW - Computing

KW - Graphics

KW - XLISP-STAT

UR - http://www.scopus.com/inward/record.url?scp=15944363777&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=15944363777&partnerID=8YFLogxK

M3 - Article

VL - 13

SP - 1

EP - 12

JO - Journal of Statistical Software

JF - Journal of Statistical Software

SN - 1548-7660

ER -