## Abstract

Regression is the study of the conditional distribution of the response *y* given the predictors * x*. In a 1D regression,

*y*is independent of

*given a single linear combination*

**x***of the predictors. Special cases of 1D regression include multiple linear regression, binary regression and generalized linear models. If a good estimate*

**β**^{T}**x***of some non-zero multiple*

**ˆb***c*of

**β***can be constructed, then the 1D regression can be visualized with a scatterplot of*

**β***versus*

**ˆb**^{T}**x***y*. A resistant method for estimating

*c*is presented along with applications.

**β**## Recommended Citation

Olive, David J. "Visualizing 1D Regression." (Jan 2004).

## Comments

Published in

Theory and Applications of Recent Robust Methods, edited by M. Hubert, G. Pison, A. Struyf and S. Van Aelst, Series: Statistics for Industry and Technology, Birkhauser: Basel, 221-233. The original publication is available at www.springerlink.com.