Abstract
This brief lecture discusses statistical problems associated with postulating and fitting models in engineering and the sciences. Particular emphasis is placed on the two-model problem: the employment of both deterministic and stochastic components within a model. Further, the use of empirical versus theoretical models on the part of both statisticians and experimenters is examined.
Keywords
linear models, non-linear models, non-independence
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Hunter, J. Stuart
(1992).
"BEYOND LINEARITY AND INDEPENDENCE,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1390
BEYOND LINEARITY AND INDEPENDENCE
This brief lecture discusses statistical problems associated with postulating and fitting models in engineering and the sciences. Particular emphasis is placed on the two-model problem: the employment of both deterministic and stochastic components within a model. Further, the use of empirical versus theoretical models on the part of both statisticians and experimenters is examined.
