Adding a Parameter Increases the Variance of an Estimated Regression Function
ARTICLE
Christopher S. Withers, Saralees Nadarajah
IJMEST Volume 42, Number 4, ISSN 0020-739X
Abstract
The linear regression model is one of the most popular models in statistics. It is also one of the simplest models in statistics. It has received applications in almost every area of science, engineering and medicine. In this article, the authors show that adding a predictor to a linear model increases the variance of the estimated regression function, and so generally increases the width of a confidence interval. The authors illustrate these facts using real and simulated data sets. (Contains 2 tables.)
Citation
Withers, C.S. & Nadarajah, S. (2011). Adding a Parameter Increases the Variance of an Estimated Regression Function. International Journal of Mathematical Education in Science and Technology, 42(4), 515-523. Retrieved August 7, 2024 from https://www.learntechlib.org/p/167249/.
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