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Updated: Jul 20, 2026

A Finite Element Approach for Locating the Center of Resistance of Maxillary Teeth

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Published on: April 8, 2020

Multiple linear regression as an analytical tool in cephalometric studies

J M Dibbets¹, C A Trotman, J A McNamara

¹Department of Orthodontics, Philipps-University, Marburg, Germany.

British Journal of Orthodontics

|February 1, 1997

Summary

Multiple linear regression analysis offers a powerful alternative to subgrouping for studying craniofacial growth, effectively accounting for age and gender influences without losing statistical power.

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Midface Hypoplasia and Cranial Base Morphology in Syndromic Craniosynostosis: A Comparative Analysis Study Using a Predictive Regression Model

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Area of Science:

Craniofacial biology
Biostatistics
Orthodontics

Background:

Investigating factors like orthodontic therapy on craniofacial growth requires accounting for age and gender.
Traditional methods involve subgrouping, which reduces statistical power and requires hard-to-find matched controls.

Purpose of the Study:

To evaluate the applicability of multiple linear regression analysis as an alternative to subgrouping for studying craniofacial growth.
To verify the practical outcomes of multiple linear regression analysis in biological data, as prior verification has been lacking.

Main Methods:

Applied multiple linear regression analysis to biological data, bypassing the need for subgrouping.
Conducted two separate tests using different datasets, predictor sets, and control samples to verify the method's applicability.

Main Results:

Multiple linear regression analysis demonstrated a viable approach for analyzing craniofacial growth data.
The verification process, despite initial mathematical challenges, confirmed the practical utility of the regression model.

Conclusions:

Multiple linear regression analysis provides a statistically sound and practical solution for studying the effects of interventions like orthodontic therapy on craniofacial growth.
This method overcomes the limitations of subgrouping, enhancing statistical power and simplifying study design in biological research.