Speaking Stata: Some notes on quadratic fits

Nicholas Cox

Stata Journal, 2025, vol. 25, issue 4, 847-864

Abstract: Quadratic fits are often used in regression and other modeling projects as either the whole of a model or part of a larger model. In this column, I discuss various small Stata devices that may help, including the twoway qfit command, calculation of the turning point position from coefficient estimates, and plotting the fitted curve over a wider range. The main principles carry over from regression to various more complicated models such as generalized linear models. A specific example is the model known in statistical ecology as Gaussian logit, which is just a logit regression fitting a unimodal bell-like curve to a proportion as a quadratic function of some controlling variable. The column includes commentary on wider historical, scientific, and statistical aspects of quadratic fits, with some remarks on their limitations and alternatives.

Keywords: quadratic fit; linear fit; turning points; transformations; Gaussian logit; regression graphics; twoway qfit; twoway function; stored results (search for similar items in EconPapers)
Date: 2025
Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj25-4/st0794/
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.stata-journal.com/article.html?article=st0794 link to article purchase

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tsj:stataj:v:25:y:2025:i:4:p:847-864

Ordering information: This journal article can be ordered from
http://www.stata-journal.com/subscription.html

DOI: 10.1177/1536867X251398616

Access Statistics for this article

Stata Journal is currently edited by Nicholas J. Cox and Stephen P. Jenkins

More articles in Stata Journal from StataCorp LLC
Bibliographic data for series maintained by Christopher F. Baum () and Lisa Gilmore ().